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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

Food waste is a serious problem worldwide, including in Europe. Research efforts are being carried out to reduce food waste. In this paper, we focus on using modern digital technologies (also known as Industry 4.0 technologies) to reduce waste in food supply chains. Based on interactions with a number of food companies in Europe over the last four years using Action Research, we provide new insights on the motivations and challenges for food companies when they are engaged in the use of technologies for reducing food waste in their supply chains. Motivations for firms include improved food quality of their produce, improved reliability, support in meeting legal requirements, a green image, and improved revenues from selling the food that has been saved. However, data security issues and trust issues posed challenges in using these technologies. Since this is an emerging area of research, we look at potential business models for technology companies for working with food companies in reducing food waste, identify value propositions and value capture, and look at how these investments in technologies can improve the sustainability of food businesses. We believe technology companies can leverage the opportunities, develop new business models with value propositions around the use of technologies, and support food companies via timely alerts in case of potential quality issues. Value capture occurs via the sale of hardware and subscriptions.

Background The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Our relationship with cultural heritage has been transformed by digital technologies. Opportunities have emerged to preserve and access cultural heritage material while engaging an audience at both regional and global level. Accessibility of technology has enabled audiences to participate in digital heritage curation process. Participatory practices and co-production methodologies have created new relationships between museums and communities, as they are engaged to become active participants in the co-design and co-creation of heritage material. Audiences are more interested in experiences vs services nowadays and museums and heritage organisations have potential to entertain while providing engaging experiences beyond their physical walls. Mixed reality is an emerging method of engagement that has allowed enhanced interaction beyond traditional 3D visualisation models into fully immersive worlds. There is potential to transport audiences to past worlds that enhance their experience and understanding of cultural heritage.

Objective To evaluate the validity and reliability of inertial measurement unit (IMU) sensors in the assessment of spinal mobility in axial spondyloarthritis (axSpA). Methods A repeated measures study design involving 40 participants with axSpA was used. Pairs of IMU sensors were used to measure the maximum range of movement at the cervical (Cx) and lumbar (Lu) spine. A composite IMU score was defined by combining the IMU measures. Conventional metrology and physical function assessment were performed. Validation was assessed considering the agreement of IMU measures with conventional metrology and correlation with physical function. Reliability was assessed using intra-class correlation coefficients (ICCs). Results The composite IMU score correlated closely (r = 0.88) with the BASMI. Conventional Cx rotation and lateral flexion tests correlated closely with IMU equivalents (r = 0.85, 0.84). All IMU movement tests correlated strongly with BASFI, while this was true for only some of the BASMI tests. The reliability of both conventional and IMU tests (except for chest expansion) ranged from good to excellent. Test–retest ICCs for individual conventional tests varied between 0.57 and 0.91, in comparison to a range from 0.74 to 0.98 for each of the IMU tests. Each of the composite regional IMU scores had excellent test–retest reliability (ICCs=0.94–0.97), comparable to the reliability of the BASMI (ICC=0.96). Conclusion Cx and Lu spinal mobility measured using wearable IMU sensors is a valid and reliable assessment in multiple planes (including rotation), in patients with a wide range of axSpA severity.

The development of recreational trails has gained popularity in recent years and therefore many scholars have studied various aspects of them. However, the recreational trail theoretical framework lacks an understanding of the relationship between the stage of trail development and income multiplier value. This research aims to examine this relationship and thus advance the traditional theory of recreational trail economic impact by providing an explanation of the relationship between the stage of trail development and the income multiplier. This study applied a combined approach of Recreation Opportunity Spectrum (ROS) to assess the stage of trail development and the Ad hoc model to estimate the income multiplier and economic impact. The results of this study reveal that there is a strong correlation between the stage of trail development and income multiplier and provide a novelty in traditional recreational trail management and economic impact theory thus enriching the topical literature. Fundacao para a Ciencia e a Tecnologia (FCT) EAPA_797/2018 info:eu-repo/semantics/publishedVersion

Rationale: Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders. Aims: This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system. Methodology: a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018–2022, focusing primarily on mental health management using technology. Results: We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

According to the International Council of Museums (ICOM, https://icom.museum/en/) current definition, “A museum is a non-profit, permanent institution in the service of society and its development, open to the public, which acquires, conserves, researches, communicates and exhibits the tangible and intangible heritage of humanity and its environment for the purposes of education, study and enjoyment.” As we move into the third decade of the 21st century, cultural heritage institutions throughout the world are using innovative digital solutions to enhance their work in all areas and also in how they communicate and exhibit heritage. This journal Special Issue presents articles that show examples and case studies demonstrating how invention and innovation can successfully take place when Museums and Digital Media converge.

UNSTRUCTURED The increased use of sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the elderly population in remote and rural areas who may struggle with lengthy commutes to clinics. However, the usability of such systems can often discourage patients from adopting these services. To increase the adoption rates of wearable technology, the usability factors influencing continued device usage in the elderly population are examined. This work focuses on evaluating the usability of a popular activity tracker, the Xiaomi Mi Band 3, amongst the over 65 population. The study consisted of 65 elders wearing the wearable sensor for 7 days while only doffing the device for charging. The study was conducted across 4 different regions; Northern Ireland, Ireland, Sweden, and Finland, to diminish any geographical differences in usability perception. At the end of the week, a customised usability questionnaire was taken by the participants to gain insights into their experience. The aim of the study was to identify influencing factors on whether an elder would continue using a wearable device. This paper concludes that comfort and accuracy are the two main influencing factors in sustaining wearable device usage. The study formed part of The Smart sENsor Devices fOr rehabilitation and Connected health (SENDoc) project, which assessed the usability of sensors for remote rehabilitation of elders in the Northern Periphery of Europe.

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers’ hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer’s hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.

Background Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection.

Continuous monitoring of food loss and waste (FLW) is crucial for improving food security and mitigating climate change. By measuring quality parameters such as temperature and humidity, real-time sensors are technologies that can continuously monitor the quality of food and thereby help reduce FLW. While there is enough literature on sensors, there is still a lack of understanding on how, where and to what extent these sensors have been applied to monitor FLW. In this paper, a systematic review of 59 published studies focused on sensor technologies to reduce food waste in food supply chains was performed with a view to synthesising the experience and lessons learnt. This review examines two aspects of the field, namely, the type of IoT technologies applied and the characteristics of the supply chains in which it has been deployed. Supply chain characteristics according to the type of product, supply chain stage, and region were examined, while sensor technology explores the monitored parameters, communication protocols, data storage, and application layers. This article shows that, while due to their high perishability and short shelf lives, monitoring fruit and vegetables using a combination of temperature and humidity sensors is the most recurring goal of the research, there are many other applications and technologies being explored in the research space for the reduction of food waste. In addition, it was demonstrated that there is huge potential in the field, and that IoT technologies should be continually explored and applied to improve food production, management, transportation, and storage to support the cause of reducing FLW.

Smartphones are becoming increasingly pervasive in almost every aspect of daily life. With smartphones being equipped with multiple sensors, they provide an opportunity to automatically extract information relating to daily life. Information relating to daily life could have major benefits in the area of health informatics. Research shows that there is a need for more objective and accurate means of measuring health status. Hence, this work investigates the use of multi-modal smartphone sensors to measure human behaviour and generate behaviour profiles which can be used to make objective predictions related to health status. Three sensor modalities are used to compute behaviour profiles for three different components of human behaviour. Motion sensors are utilised to measure physical activity, location sensors are utilised to measure travel behaviour and sound sensors are used to measure voice activity related behaviour. Sensor fusion, using a genetic algorithm, is performed to find complementary and co-operative features. Using a behaviour feature composed of motion, sound and locations data, results show that a Support Vector Machine (SVM) can predict 10 different health metrics with an error that does not exceed a clinical error benchmark.

Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

Biomechanical analysis of gait is commonly used in physiotherapy. Ground reaction forces during phases of gait is one element of kinetic analysis. In this article, we analyze if the MoveSole® smart insole is valid and accurate equipment for measuring ground reaction forces in clinical physiotherapy. MoveSole® StepLab is a mobile measurement system for instant underfoot force measurements during gait. Unique electromagnetic film (EMFI) based sensor technology and printed electronics production technology is integrated in the MoveSole® StepLab measurement system. The MoveSole® StepLab measures plantar ground reaction force distribution over the sensors and provides an estimation of the maximum total ground reaction force. We developed a two phase validation process to extract relevant parameters and compared the results to a Kistler force plate using the BioWare® analyzing program as a reference method. Our results show that MoveSole® smart insoles reach the strong level of accuracy needed in clinical work concerning highest ground reaction forces during step (Pearson correlation .822 - .875). The correlation of the time when the maximum ground reaction force occurred was moderate, e.g. during heel strike or toe-off (Pearson correlation natural gait speed .351 - .462, maximum gait speed .430). Our conclusion is that MoveSole® smart insoles are a potential tool for analyzing and monitoring gait ground reaction forces during physiotherapy processes.

Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices. Wearable sensors have been used in many healthcare applications to monitor patients at home and throughout their rehabilitation. Data collected from wearable sensors allow monitoring of patient recovery during rehabilitation and assist clinicians in diagnosing. Activities of Daily Living (ADL) is considered as an assessment criterion for various disease conditions. Wearable devices enable the collection of information associated with different range of movement (ROM) tests that measure ADL. In an ambulatory monitoring setting, the volume of data collected by wearable sensors can become complex and challenging to process. Extraction of ROM tests can be laboursome, and often fraught with misclassification of movement. Hence it is difficult to analyse and make conclusions/predictions from movement datasets using manual assessment techniques. This paper examines whether ROM tests can be automatically detected and extracted from wearable sensor data using Artificial Intelligence (AI) techniques.This research examines and discusses clinical trial data collected from patients suffering from Axial SpondyloArthritis (AxSpA). AxSpA is a disease that affects spinal cord mobility. In this trial, Inertial Measurement Unit (IMU) sensors are attached to the lower back and neck of the patient, and data corresponding to clinical trial movements are recorded. An AI system is trained and tested using these datasets, and the prediction accuracy of the system is examined. The system will be capable of detecting ROM tests within long-term datasets once the AI system used in this analysis is sufficiently trained by an adequate amount of data for efficient pattern recognition.

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.

The objectives of this study were to evaluate the reliability of wearable inertial motion unit (IMU) sensors in measuring spinal range of motion under supervised and unsupervised conditions in both laboratory and ambulatory settings. A secondary aim of the study was to evaluate the reliability of composite IMU metrology scores (IMU-ASMI (Amb)). Forty people with axSpA participated in this clinical measurement study. Participant spinal mobility was assessed by conventional metrology (Bath Ankylosing Spondylitis Metrology Index, linear version—BASMILin) and by a wireless IMU sensor-based system which measured lumbar flexion-extension, lateral flexion and rotation. Each sensor-based movement test was converted to a normalized index and used to calculate IMU-ASMI (Amb) scores. Test-retest reliability was evaluated using intra-class correlation coefficients (ICC). There was good to excellent agreement for all spinal range of movements (ICC &gt, 0.85) and IMU-ASMI (Amb) scores (ICC &gt, 0.87) across all conditions. Correlations between IMU-ASMI (Amb) scores and conventional metrology were strong (Pearson correlation ≥ 0.85). An IMU sensor-based system is a reliable way of measuring spinal lumbar mobility in axSpA under supervised and unsupervised conditions. While not a replacement for established clinical measures, composite IMU-ASMI (Amb) scores may be reliably used as a proxy measure of spinal mobility.

Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

This paper examines the current state of the art of commercially available outdoor footfall sensor technologies and defines individually tailored solutions for the walking trails involved in an ongoing research project. Effective implementation of footfall sensors can facilitate quantitative analysis of user patterns, inform maintenance schedules and assist in achieving management objectives, such as identifying future user trends like cyclo-tourism. This paper is informed by primary research conducted for the EU funded project TrailGazersBid (hereafter referred to as TrailGazers), led by Donegal County Council, and has Sligo County Council and Causeway Coast and Glens Council (NI) among the 10 project partners. The project involves three trails in Ireland and five other trails from Europe for comparison. It incorporates the footfall capture and management experiences of trail management within the EU Atlantic area and desk-based research on current footfall technologies and data capture strategies. We have examined 6 individual types of sensor and discuss the advantages and disadvantages of each. We provide key learnings and insights that can help to inform trail managers on sensor options, along with a decision-making tool based on the key factors of the power source and mounting method. The research findings can also be applied to other outdoor footfall monitoring scenarios.

BACKGROUND Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. OBJECTIVE This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. METHODS This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. RESULTS Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. CONCLUSIONS Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

Background Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.

Background Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. Methods This prospective cohort study included N = 3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012–2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. Results During a mean follow-up of 2.6 years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ≥ 4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25–0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38–1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32–1.05). Conclusions A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity.

BACKGROUND Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. OBJECTIVE This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. METHODS We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. RESULTS In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. CONCLUSIONS The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences. CLINICALTRIAL

The symptoms of (PD) often begins on one side of the body before separating bilaterally. Early symptoms of PD are rest tremors, bradykinesia and rigidity. Common signs are a decrease in arm swing, shuffling foot movement and slowness arising from a chair. Rigidity of muscles commonly affects a patient limbs, neck and shoulder PD is currently diagnosed after the neuro degenerative process has started. To optimize and improve PD quality of care, it should be diagnosed early in its onset once symptoms are not yet evident. The current gold standard for patient assessment of PD is the completion of symptom diaries. These diaries are subjective and usually do not accurately reflect what is taking place throughout the day. It is exceedingly difficult for an untrained observer such as family members to provide accurate description of movement characteristic in PD. An alternative way to exercise the abnormal movement in PD is to use a wearable technology system which could aid early detection of the disease and help clinicians manage medication when symptoms fluctuate throughout the day. This paper will evaluate the effectiveness of a wearable multi-sensor kinematic system capable of detecting the tell-tale signs of PD when performing tailored exercise routines. Each exercise routine will examine movement for potential PD symptoms and report finding for deeper clinician analysis.

Wearable sensor technology is often used in healthcare environments for monitoring, diagnosis and recovery of patients. Wearable sensors can be used to detect movement throughout measurement of standardized functional tests, which are considered part of the assessment criteria for Activities of Daily Living (ADL). The volume of data collected by sensors for long term assessment of ambulatory movement can be very large in tuple size since they may contain detailed 3-D sensor information. Extracting recorded movement data corresponding to standardized functional tests from an entire data set is complex and time consuming. This paper examines whether standardized functional tests can be automatically detected from long term data collected by wearable technology devices using Artificial Intelligence (AI) techniques. The current research work is aligned with clinical trial data generated by patients who are suffering from Axial Spondylo Arthritis (axSpA). These datasets contain Inertial Measurement Unit (IMU) values corresponding to individual patient functional tests for axSpA. Rotation angles with respect to each functional test are plotted against time. Individual movements that form part of a functional test are constructed for training and testing the AI system. Individual movement patterns are split into training and testing data inputs and are used to train the Neural Network (NN) system and to estimate overall prediction accuracy of the NN system. NN model is trained in such a way that the learned system can predict new functional test patterns with respect to the trained data and it is compared with expected data set and returned the accuracy of prediction. Once the semi supervised learning phase of AI system has successfully finished with adequate amount of data, it is capable for automatically detect gait and posture changes of patients at home.

Gait-based person identification suffers from the problem of different covariate factors such as clothing and carrying objects, which drastically reduce the recognition rate. Most existing methods capture dynamic and static information and remove the covariate factors without any systematic study. However, it has been reported in the literature that the head is one of the important features and the removal of the head from static information decreases the recognition rate. In our preliminary study, we developed a novel random walk (RW)-based gait extraction method that retains the head portion and removes certain static body parts to reduce the effect of covariate factors. The RW-based method is a novel gait feature extraction method and should be exploited more for its discriminative power to separate different body parts efficiently. However, the dynamic part is also significant in gait information, which is not very effectively represented in the RW-based gait extraction method. Therefore, a discrete Fourier transform (DFT)-based frequency component of the gait is considered to represent the dynamic part of gait information. Furthermore, we propose a novel gait recognition algorithm that fuses dynamic and static information from DFT- and RW-based representations. The proposed method systematically retains the discriminative static gait information along with the frequency attribute embedded as the dynamic gait information. Extensive experiments on the Chinese Academy of Sciences, Institute of Automation and the HumanID datasets have been carried out to demonstrate that the proposed fused gait features-based approach outperforms the existing methods, particularly when there are substantial appearance changes.

In the last decade the total global number of displaced refugees has increased significantly and consistently. This has been further inflated by 5 million Syrians who were externally displaced in 2017. Often these individuals flee their home countries without identification documentation and thus find it extremely difficult to verify their true identities when they attempt to reconstruct their lives in their new homelands. The requirement for identification documentation is a constant element of daily life within modern societies. This functions to both protect the rights of individuals and also the entities and organisations they interact with. The need to provide identification documentation can present significant challenges for displaced people trying to reconstruct their lives. In the digital age where individuals are creating large digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to ‘proof’ that an asserted identity is genuine. Online social contextual data - or ‘Digital identities' - pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that could potentially allow refugees who are resettling to build a digital identity document over time as they settle in a new land, without the need for multiple sources of traditional identity documentation. This solution utilizes data sourced from an individual's digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques. Where identity sources are considered reliable, a secure and intelligent digital identification artefact will be created. This artefact will emulate a blockchain-inspired ledger and may subsequently be used to prove identity in place of traditional paper documentation.

Background Axial Spondyloarthritis (axSpA) is characterised by progressive loss of spinal mobility, due to inflammation and structural damage. Conventional mobility tests have been used alongside radiographic structural damage and disease activity scores in order to explore the overall relationship between spinal mobility, structural damage and disease activity. This has rarely been done at the segmental level e.g. at the level of the lumbar spine only. The ViMove inertial motion sensor-based system allows segmental spinal mobility to be directly measured, namely at the lumbar spine level. Objectives To analyse the relationship between mobility, structural damage and disease activity in the lumbar spine of patients with axSpA. Methods Lumbar spinal mobility was measured using the the ViMove system with a pair of sensors located at L1 and the sacrum. Lumbar ROM in the three planes (anterior flexion - LFF, extension - LFE, lateral flexion – LL, and rotation - LR) was measured. Radiographs were obtained to calculate the lumbar part of the mSASSS (LmSASSS). Other outcome measures were: BASMI, Schober and lateral flexion (measured with a tape), BASDAI and ASDAS-CRP. Subgroups of patients with high and low levels of lumbar structural damage were defined, by dichotomising the population based on the median LmSASSS (5.5). Pearson correlations between measures, Student T tests for significant differences between groups and linear regression analysis, to analyse relationship between structural damage and mobility, were obtained. Results 44 axSpA patients were recruited from the COSPAR cohort of the Reina Sofia University Hospital (39% females, age 43±12 years, disease duration 17±14 years, BMI 26±3). All mobility measurements (except for LFE) in the severe damage group were significantly reduced compared to the group with less damage (p Conclusion Other studies have shown a relationship between mobility, structural damage and disease activity using a more global assessment. Our study focuses on the analysis of structural damage and mobility in the lumbar vertebrae, and our results suggest that structural damage is the dominant factor determining spinal mobility in this region (especially lumbar frontal flexion). Further studies are needed combining these results with MRI. Acknowledgement This study was funded by FOREUM (www.foreum.org), Junta de Andalucia (PIN-0079-2016) and XXI Research Promotion Program of the University of Cordoba (Spain). Disclosure of Interests Juan L. Garrido-Castro: None declared, Inmaculada Concepcion Aranda-Valera: None declared, Cristina Gonzalez-Navas: None declared, Pedro Machado Consultant for: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Philip Gardiner: None declared, Joan Condell: None declared, Eduardo Collantes Estevez: None declared

Online accounts and services have become a ubiquitous facet of modern existence. As society becomes increasingly dependent on digital services to conduct the fundamental aspects of daily life, online identities may possess the capacity to provide verification that an individual is a ‘real person’ with a ‘true’ identity. Traditionally verification of identity has been achieved by cursory examination of paper documentation and simple matching of the attributes contained therein. Improved technology and printing capacity has since reduced confidence in this type of identity documentation, highlighting the need for methods better acquainted with the digital age. A recognized challenge in utlising online accounts as proof of identification is the various levels of reliability associated with different types of accounts. This variation is based on the importance of account and the processes of verification required to initially create the account. This paper explores the application of Bayes theorem to determine the reliability of online accounts based on the account type and the attributes it contains. Bayes rule is applied subsets of attributes contained within various types of online accounts that are presented as ‘identity sources’. This seeks to demonstrate that less reliable sources such as social media accounts will produce lower trust scores than more reliable sources such as online banking. The results for which will be used to determine if digital identity sources can be relied upon in place of traditional paper identification documentation. This research and testing has been conducted as an element of a larger intelligent identity authentication system that seeks to create a solution that proves an identity is genuine via an individual's digital footprint.

Background Loss of spinal mobility is one of the most characteristic problems for people with axial spondyloarthritis (axSpA) and is predictive of loss of function. Traditional measures such as the BASMI fail to capture many elements of spinal mobility and lack responsiveness to change. Inertial Motion Unit (IMU) sensors can be used to accurately measure spinal movement without requiring significant operator expertise. Objectives The primary objective of this study was to test the reliability of these new tools in patients with axSpA and to develop a new composite spinal mobility index. Our secondary objective was to apply the OMERACT ‘Truth’ filter to evaluate the new index for bias, for clinical relevance, and for convergent validity with existing measures1. Methods Patients with axSpA fulfilling ASAS classification criteria were recruited. ViMove system was used to obtain ROM by attaching two IMU sensors at the cervical (Occiput-T3) and lumbar spine (L1-Sacrum). Intra-rater, inter-rater and test-retest reliability of IMU tests were assessed by intraclass correlation coefficients (ICC). The maximum range of movement for anterior flexion/extension (AFE), lateral flexion (Left+Right) and rotation (Left+Right) were obtained for the lumbar and cervical region. These six values were used in a composite score (IMU-ASMI) which referenced equivalent ROM values from normal subjects in an earlier criterion validity study. Pearson correlation coefficients with BASFI were calculated for each component as well as the overall score. Results The study included 40 patients (12 females, 28 males) with a mean age of 48 (27-41). Subjects had a wide range of severity of axSpA. The mean BASMI was 4.8 (range 0.7 to 8.2, SD 1.9). The mean IMU-ASMI was 4.0 (range 0.1-9.2, SD 2.1). The sensor based measurements had good to excellent reliability (Table 1) and correlated closely with BASMI (r=0.79). The mean BASFI was 4.6 and the IMU-ASMI correlated closely with BASFI (r=0.71). Face Validity: Each IMU test presents spinal movement in angles and can also be represented as a normalized severity index analogous to BASMI. The mean cervical and lumbar IMU-ASMIs were 3.5 and 4.4 units, respectively. Construct Validity: Do IMU movements correlate with their corresponding traditional measurements? As expected, the closest correlations were between IMU and goniometer cervical rotation (r=0.85) and between IMU and tape measure lumbar side flexion (r=0.84). Correlations between Schober’s test and IMU lumbar AFE and between tragus to wall and IMU cervical FE were moderate (r=0.62, 0.65). Do IMU movements correlate with BASFI? Correlation coefficients were as follows: lumbar AFE -0.57; rotation -0.59; side flexion -0.45; cervical F/E -0.55; rotation -0.61; side flexion -0.39. BASFI correlations with BASMI were comparable. Content Validity/Comprehensiveness: No major ceiling or floor bias issues were found in the composite indices. Intermalleolar distance (BASMI) represents hip rather than spinal mobility, but it correlates with BASFI and is not in the IMU-ASMI. IMU-ASMI includes lumbar rotation which accounts for 27% (0-53%) of the lumbar mobility score. Conclusion IMU sensors can be used by non-experts to accurately and reliably measure spinal mobility in patients with axSpA. Lumbar rotation is an important new outcome measure. References [1] Tugwell P, et al. J Rheum 2014 41(5):1000-1004 Acknowledgement This study was funded by FOREUM (www.foreum.org) Disclosure of Interests Philip Gardiner Grant/research support from: Educational support to attend rheumatology conferences from AbbVie, Abbott, MSD, Novartis, Amgen, Pfizer, Roche, UCB., Speakers bureau: Menarini, Dawn Small Employee of: Medical representative - Farmitalia Carlo Erba, Pharmacia, Pharmacia & Upjohn, Yamanouchi/Astellas., Paid instructor for: Medical representative - Farmitalia Carlo Erba, Pharmacia, Pharmacia & Upjohn, Yamanouchi/Astellas., Pedro Machado Consultant for: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: Abbvie, BMS, Celgene, Janssen, MSD, Novartis, Pfizer, Roche and UCB, Karla Munoz Esquivel: None declared, Joan Condell: None declared, Antonio Cuesta-Vargas: None declared, Juan L. Garrido-Castro: None declared

Background:Spinal mobility is an important outcome in axial spondyloarthritis (axSpA). Standardized metrology tests have been defined and validated. Nevertheless, conventional metrology measures lack responsiveness, particularly in the context of assessing short term response to treatments. Advanced metrology devices have also been used in axSpA, namely optical motion capture systems (MoCap) and Inertial Measurement Systems (IMU). These systems have better psychometric properties in terms of accuracy and reliability for analysing spinal mobility.Objectives:To compare the responsiveness of spinal mobility using conventional and advanced metrology.Methods:Spinal mobility was measured using an IMU based system (ViMove©) with sensors located at: occiput and T3 for cervical mobility, L1 and sacrum for lumbar mobility, and T3 and sacrum for thoracic+lumbar mobility. A MoCap system (UCOTrack©) using 13 markers was also used to record the movements in 3 planes (flexion+extension, rotation, and lateral flexion). The UCOASMI [1] index was calculated using measures obtained by MoCap and by the IMU system. Other metrology indexes based on IMU measures developed by the authors [2} were also calculated. Patients were assessed before (PRE) and 3 months after (POST) TNF blocker treatment. The Wilcoxon signed-rank test was used to test differences between pairs of pre- and post-treatment. Cohen’s d was use for calculating the effect size of improvement.Results:12 axSpA patients were recruited from the CASTRO cohort (42% female, age 46±11 years, disease duration 16±13 years). Results PRE and POST are shown in Table 1: means (sd), statistical significance, and effect size. Disease activity and functional indexes were lower in POST and these differences were significant, especially in ASDAS. Conventional metrology lacked responsiveness, except for cervical rotation. MoCap showed moderate responsiveness in cervical rotation, in thoracolumbar mobility and with regards to the UCOASMI. Using IMU measures, responsiveness was good in terms of cervical flexion and rotation. For Lumbar (L_) and Lumbar Thoracic (LT_), lateral flexion shown significant improvement and lumbar rotation shown the best responsiveness.PREPOSTSignESPREPOSTSignESActivityIMUBASDAI6.5 (1.6)4.9 (2.7)*0.64C_FLEX77 (22)83 (19)*-0.25BASFI6.5 (2.3)4.8 (3.2)*0.58C_ROT78 (25)84 (22)*-0.22ASDAS3.6 (1.0)2.5 (1.2)**0.87C_FL54 (19)54 (17)NSMetrologyL_FLEX46 (16)44 (19)NSLatFlex9.3 (4.1)10.6 (3.3)NSL_ROT21 (9)27 (9)***-0.66TWD13.2 (4.2)12.8 (3.6)*0.06L_FL32 (1)38 (10)*-0.52Schober4.7 (1.5)4.9 (1.5)NSLT_FLEX62 (28)63 (24)NSIMD89 (15)90 (15)NSLT_ROT63 (19)70 (19)NSCervRot104 (28)118 (24)**-0.51LT_FL52 (15)60 (15)**-0.51BASMI3.8 (1.3)3.4 (1.2)NSTrk_FLEX109 (28)112 (24)NSMoCapTrk_ROT98 (31)116 (21)**-0.59C_FLEX81 (22)87 (20)*-0.28Trk_FL39 (9)46 (10)**-0.7C_ROT101 (28)112 (25)***-0.38TTrk_FLEX119 (36)128 (30)NSC_FL58 (20)58 (18)NSTTrk_ROT137 (34)161 (35)**-0.67L_FLEX107 (24)114 (21)***-0.26TTrk_FL59 (15)69 (16)**-0.6L_ROT67 (21)73 (20)NSiUCOASMIL5.7 (1.5)5.0 (1.4)***0.48L_FL57 (18)65 (20)**-0.41iUCOASMILT6.0 (1.5)5.4 (1.3)**0.39UCOASMI6.1 (1.6)5.3 (1.6)**0.50iCxLPASMI3.9 (1.8)3.2 (1.5)**0.40iCxLuASMI3.2 (1.4)2.7 (1.3)***0.40NS, not significant; *, pConclusion:Conventional metrology lacks short-term responsiveness. Advanced metrology using MoCap and IMU showed, in this pilot study, better responsiveness compared to conventional measures. Furthermore, IMU provide a feasibility similar to conventional metrology, and could be easily implemented in the context of clinical trials and observational studies.References:[1]Validation of a new objective index to measure spinal mobility: the University of Cordoba Ankylosing Spondylitis Metrology Index (UCOASMI). Rheumatol Int. 2014 Mar;34(3):401-6.[2]Validity and Reliability of a Sensor-Based Electronic Spinal Mobility Index for Axial Spondyloarthritis. Rheumatology (Oxford). 2020 Apr 28;keaa122.Disclosure of Interests:None declared

Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual’s RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

Closed-circuit television footage can be used to assemble an often-complex picture of an incident and aid in the identification of suspects after a crime or terrorist attack has occurred. For example, such footage allowed the police to not only identify the 7/7 London bombers but also to piece together the details of the bombers' movements prior to the attack. In the case of the London bombers little attempt was made to disguise their identities but where such identities are concealed it is possible to identify suspects based on other unique biometric characteristics such as the style of walk, referred to as gait. Gait feature–based individual identification has received increased attention from biometrics researchers. In this article, we propose a novel gait biometric methodology that could contribute to the counterterrorism effort and the identification of individuals involved in crime.

Since 9/11, there has been an increased interest in the use of biometric technologies as a way to counter the threat of terrorism and to protect a nation's critical national infrastructure and infrastructure assets. Biometric features can be used to verify an individual's identity or in the identification of an individual. Such features can be divided into two types, namely physical and behavioral characteristics. Physical biometrics are concerned with direct measurements of an individual's body and would include fingerprints, facial geometry and iris patterns. In contrast, behavioral biometrics are concerned with the indirect measurements of an individual, namely their patterns of behavior such as gait, voice and keystroke dynamics. Thus, each individual has a set of unique behavioral biometric features, which they usually adhere to under normal conditions. Based on this concept it is then possible to create different behavioral biometric profiles for each individual that can distinguish one indi...

In a bid to stamp out fraudulent crime, there is increased pressure on individuals to provide evidence that they possess a ‘real’ identity. Counterfeiting and fake identities have reduced confidence in traditional paper documentation as proof of identity, this has created a demand for an intelligent digital alternative. Recent government implementations and identity trends have also improved the popularity of digital forms of identification.Authentication of identity is a salient issue in the current climate where identity theft through record duplication is on the rise. Identity resolution techniques have proven effective in filtering duplicated and fake records in identity management systems. These techniques have been further improved by the implementation of machine learning techniques which are capable of revealing patterns and links that have formerly gone undetected. Research has also suggested that incorporating non-standard attributes in the form of social contextual data can increase the efficiency and success-rate of these fraud detection methods.In the digital age where individuals are creating large digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to ‘proof’ that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity.This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques. Where identity sources are considered reliable, a secure and intelligent digital identification artefact will be created. This artefact will emulate a blockchain-inspired ledger and may subsequently be used to prove identity in place of traditional paper documentation.

BackgroundIncreasing numbers of women living with chronic conditions are becoming pregnant (Jølving et al., 2016). The presence of chronic conditions can contribute to adverse outcomes in pregnancy and in later life for mother and infant (Gilmore et al., 2015; Schetter and Tanner, 2012). Over recent years, the role of increasing BMI in pregnancy has gained prominence, with risk factors for maternal obesity well documented in literature. AimTo determine the relationship between BMI and other chronic conditions (multimorbidity) and pregnancy and birth outcomes in Northern Ireland. MethodA cross-sectional cohort study was undertaken using data from the NIMATS database (2014-2015), accessed via the Honest Broker Service, with approval by the Office for Research Ethics Committees Northern Ireland (ORECNI) and the Honest Broker Governance Board. ResultsRegression analyses demonstrated statistically significant relationships between increasing BMI and maternal conditions such as asthma, diabetes, epilepsy, hypertension, mental health and thyroid disease as well as a number of labour and birth outcomes. Mostly, these relationships were shown to increase with increasing BMI. DiscussionThe findings from this study have important implications for the provision of preconception care, maternity services (including health professional training), child health services and the public health agenda.

The use of biometrics has become a popular method to counter the threat of terrorism. Biometric features can be used to confirm identity (biometric authentication) or to identify an individual (biometric identification). Such features include physical (e.g. fingerprints and DNA) and behavioural characteristics (e.g. handwriting and gait). This paper is concerned with one type of behavioural characteristics, namely gait. Gait refers to an individual's style or manner of walk and thus represents one of the body language information signifiers that can identify individuals distinctively in surveillance videos. Recently, gait recognition for individual identification has received increased attention from biometrics researchers as gait can be captured at a distance by using a low-resolution camera. Human gait properties can be affected by clothing and the carrying of objects both of which have been identified within the existing literature as causing difficulties in gait recognition. In this paper, we propose ...

We consider the problem of person identification using gait sequences under normal, carrying bag and different clothing conditions as the main concern. It has been demonstrated that Gait Energy Image (GEI) can attain a better gait recognition rate under normal conditions. However, it has been shown that GEI is not robust enough to handle the carrying bags and different clothing conditions. Instead of GEI, there are several appearance based gait features in the available literature to reduce the effect of covariate factors by keeping dynamic parts and removing the static parts of the gait features under the assumption that the carrying bags and different clothing conditions affect mostly the static parts. It is however shown in the literature that the static parts also contain valuable information and removal of certain static parts such as head by mistake thigh typed certainly decreases the recognition rate.Our main objective has been to increase the gait recognition rate on different clothing and carrying bag covariate gait sequences. Therefore instead of removing static parts, the Joint Sparsity Model (JSM) is applied to identify the carrying bags and different clothings conditions from GEI features. If a set of GEI feature vectors is submitted to JSM model then a common component and an innovations component for each GEI feature are obtained. The innovations component that has unique characteristic to each of features is considered to identify the covariate conditions. The identified covariate conditions are removed from GEI features and a novel gait feature called GEI JSM is generated. The dimension of GEI JSM is reduced using Random Projection (RP) approach and ? 1 -norm minimization technique based sparse representation is used for classification. It is demonstrated that the RP and ? 1 -norm minimization based sparse representation approach provides statistically significant better results than that of the existing individual identification approaches.

In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.

The term ‘steganography’ encapsulates the practice of secretly embedding data into digital mediums including video, image and audio files. Although steganography is often associated with nefarious activities, conceptually it asserts several characteristics that render it useful in contemporary security applications. Not just a mechanism for criminals to communicate secret information over a digital channel, steganography is also used as a legitimate method of ensuring integrity of digital media artefacts and for identification of same. This application of steganography allows for identification images storing additional information to verify both the identity of the subject as well as the authenticity of the image. Developed methods of steganography invoke various spatial domain techniques that are successful in covertly concealing data within ‘innocent’ carrier images. The techniques include linear methods such as those which replace the least significant bit (LSB) of the bytes in an image and frequency domain methods including discrete cosine transform (DCT), discrete wavelet transform (DWT) and discrete Fourier transform (DFT). The success of a steganographic algorithm is hinged on the method's ability to successfully embed data, so that the data remains concealed within a carrier image; and also to successfully extract the same data uncorrupted. Often modern image coding formats include lossy compression in the frequency domain; this can result in data loss, corruption and noise within the image when carrier images are re-encoded. To ensure data extraction is successful, error correction functions must be invoked to counteract noise and ensure embedded data is extracted without any loss or corruption. In exploring steganographic software, the functionality and reliability of a novel steganographic application ‘Intelligent Identity Authenticator’ (IIA) was assessed. IIA invokes the use of steganography to conceal real-time identity information within images on identity cards. The functionality of IIA is based on a unique algorithm that utilizes DWT to embed a string of characters within an identity image. When data is extracted, it provides a link to further documentation relating to the data subject, allowing for verification of the claimant's identity and authenticity of the identity card. The embedding and extraction functions executed by IIA were found to be mostly reliable, except where data had been embedded within a carrier image that was characterized by a large proportion of black pixels. In these cases, the extracted data string experienced significant loss and corruption, thus preventing access to the identity verification documentation. This paper explores the potential cause of this specific corruption and discusses extensive testing conducted on control images. The results are analysed to identify an improved solution that could rectify the issue, with an aim to improving both the functionality and reliability of the IIA system.

Background Improving joint mobility is an important outcome for patients with arthritis, but finger joint range of motion is rarely measured in clinic. Electronic gloves with movement sensors have been developed to measure joint movement accurately and it is now possible to assess dynamic mobility of the finger joints. However these gloves are expensive and it is likely that when carrying out measurements in the patient population they would be used with inner disposable gloves to avoid nonsocomical infection. Establishing accuracy and usability of electronic gloves whilst wearing disposable inner gloves is therefore an important pre-requisite for studies in patients with arthritis. Objectives To establish the accuracy and repeatability of measurements of finger movement obtained using two different electrogoniometric gloves worn with and without an inner disposable glove. Methods We used two different types of electrogoniometric glove for the purpose of this study. One is the commercially available 5DT dataglove 14 Ultra (5DT, 2011) and the other was produced to our specifications by Tyndall National Institute, University College Cork. We called this the “IMU glove”. We developed a graphical interface for both devices to facilitate detailed evaluation of joint movement in each finger. Both gloves were tested using a protocol adapted from Dipietro, Sabatini, & Dario, (2003). Results Table 1 displays comparison of Coefficient of Variation (CV) readings for both data gloves. Figure shows this information graphically. Results show no significant change for 5DT angular readings with and without a surgical glove worn underneath the data glove. Results for PIP sensors show an improvement in repeatability with a surgical glove. CV variance was smaller for MCP sensors with a surgical glove worn underneath the data glove compared with no surgical glove. CV for the IMU data glove show negligible changes in MCP readings when a surgical glove is worn underneath. PIP readings show small changes when using a surgical glove. Conclusions Inner disposable gloves can be worn when using electrogoniometric gloves for testing finger movement without loss of accuracy or any significant discomfort in patients with arthritis. References 5DT, 2011. 5DT Data Glove 14 Ultra [WWW Document]. URL http://www.5dt.com/products/pdataglove14.html(accessed 1.10.12). Dipietro, L., Sabatini, A.M., Dario, P., 2003. Evaluation of an instrumented glove for hand-movement acquisition. J. Rehabil. Res. Dev. 40, 179–89. Disclosure of Interest None declared

Information systems in the digital age have become increasingly dependent on databases to store a multitude of fundamental data. A key function of structured databases is to house authentication credentials that verify identity and allow users to access more salient personal data. Authentication databases are frequently a target of attack as they potentially provide an avenue to commit further, more lucrative crimes. Despite the provision of industry standard best practice recommendations from organisations such as Open Web Application Security Project (OWASP), Payment Card Industry Security Standards Council (PCI-SSC), Internet Engineering Task Force (IETF) and Institute of Electrical and Electronics Engineers (IEEE), often practical security implementations within industry flounder. Lacking or substandard implementations have cultivated an environment where authentication databases and the data stored therein are insecure. This was demonstrated in the 2016 exposure of a breach experienced by Yahoo where approximately one billion user credentials were stolen. The global technology company was found to be using obsolete security mechanisms to protect user passwords. Dated implementations such as these pose serious threat as they render authentication data highly vulnerable to theft and potential misuse. This paper offers a novel solution for securing authentication databases on non-compliant Apache servers. The method applies the recommended best practice mechanisms in the form of salt, one-way encryption (hashing) and iterations to both pre-existing and newly created passwords that are stored on insecure systems. The proposed solution can be implemented server-side, with little alteration to the existing infrastructure, unbeknownst to the user. It possesses the potential to improve system security, aid compliance, preserve privacy and protect users.

Background Patients with Rheumatoid arthritis suffer from pain, stiffness and reduced mobility of their finger joints. Instrumented electronic goniometric gloves have been developed which can enable dynamic measurements to be made with accuracy and efficiency [1]. This technology could be employed in assessing patients with rheumatoid arthritis and could also be used as an aid to rehabilitation. However, pain and swelling in the joints might limit the applicability of these measurement devices as to date they have not been tested in this patient group. Objectives The purpose of this pilot study was to establish the usability of two different electronic gloves in patients with mild to moderate Rheumatoid arthritis. We wished to establish if patients experienced difficulty donning or doffing either glove or if carrying out a series of measurements with the gloves on caused a change in pain and stiffness. We also investigated differences in the usability and preference between the two Datagloves. Methods We compared a commercially available electronic glove (the 5DT dataglove 14 Ultra) with a bespoke IMU based electronic glove produced to our specifications by Tyndall National Institute, University College Cork (Figure). We developed a programming interface for both devices to facilitate calibration and detailed evaluation of joint movement. Nine patients with mild to moderate rheumatoid arthritis who were experiencing significant but not severe pain and early morning stiffness in their hands were recruited. After calibration, the patients worked through a protocol of finger flexion and extension movements, which were repeated for each glove and again to test repeatability. The patients completed questionnaires before and after using the gloves on their pain and stiffness levels and at the end of the session on glove donning and doffing usability and preference between the two gloves. Results All nine patients were able to don and doff the IMU glove without any difficulty compared to 4/9 for the 5DT glove. Seven of 9 patients expressed a preference for the IMU glove. In 2/9 patients, joint stiffness increased after using the gloves and 4/9 patients reported an increase in pain. Seven out of nine patients were not able to complete the fourth set of movement tests because of discomfort. Conclusions Electrogoniometric gloves are usable in patients with rheumatoid arthritis, but patients cannot tolerate longer protocols without an increase in discomfort. References Dipietro L, et al. (2003). Evaluation of an instrumented glove for hand-movement acquisition. Journal of Rehabilitation Research and Development, 40(2), 179–89. Disclosure of Interest None declared