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Computing systems comprise a surreptitious and intrinsic part of our daily life activities. Applications that support humans in daily life facilitate the development of the so-called Intelligent Environments. Like any technology Intelligent Environments can fail. This paper examines potential negative consequences of such systems if they are too naively or optimistically developed and used. The aim of this work is to encourage those contributing to the technical area to reflect on these issues and to provide symbiotic solutions which make such a powerful technical development safer for humans so that it can unfold all its potential to empower future citizens, especially the vulnerable.

An activity monitoring and reminder deliveryframework, referred to as iMessenger, is presented. iMessengerincludes five independent modules and adopts alayered structure to assemble each of these modules: contextsensing, context extraction, context management,context-aware reminders, and human–computer interactions. This paper presents the details of the context managementmodule that has adopted ontological modeling and reasoning technologies. The ontological approach can support both distributed context integration and advanced temporal reasoning capabilities. iMessenger has the abilityto infer inconsistencies between what the user was expected to do and what the user is actually doing, and supply appropriate feedback to encourage people to follow their predefined agendas correctly in addition to keep healthy postures during their daily activities. The framework hasbeen validated using simulated scenarios within the Protege environment.

Objective: Adapting therapeutic practice from traditional face-to-face exchange to remote technology-based delivery presents challenges for the therapist, patient, and technical writer. This article documents the process of therapy adaptation and the resultant specification for the SMART2 project-a technology-based self-management system for assisting long-term health conditions, including chronic pain. Materials and Methods: Focus group discussions with healthcare professionals and patients were conducted to inform selection of therapeutic objectives and appropriate technology. Results: Pertinent challenges are identified, relating to (1) reduction and definition of therapeutic objectives, and (2) how to approach adaptation of therapy to a form suited to technology delivery. The requirement of the system to provide dynamic and intelligent responses to patient experience and behavior is also emphasized. Conclusion: Solutions to these challenges are described in the context of the SMART2 technology-based intervention. More explicit discussion and documentation of therapy adaptation to technology-based delivery within the literature is encouraged.

Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC (p >; 0.05); however, there were significant differences between the classifiers for each ranking method (p

Ambient assisted living (AAL) services that provide support for people to remain in their homes are increasingly being used in healthcare systems around the world. Typically, these ambient assisted living services provide additional information though location-awareness, presence-awareness, and context-awareness capabilities, arising from the prolific use of telecommunications devices including sensors and actuators in the home of the person receiving care. In addition there is a need to provide abstract information, in context, to local and remote stakeholders. There are many different viewing options utilizing converged networks and the resulting explosion in data and information has resulted in a new problem, as these new ambient assisted living services struggle to convey meaningful information to different groups of end users. The article discusses visualization of data from the perspective of the needs of the differing end user groups, and discusses how algorithms are required to contextualize and convey information across location and time. In order to illustrate the issues, current work on nighttime AAL services for people with dementia is described.

Decision support systems (DSS) are software entities that assist the physician in the decision making process. They have found application in medicine due to the large amounts of data (e.g. laboratory measurements such as blood pressure, heart rate, body-mass index) and information (e.g. patient history, population statistics based on age and sex) that must be considered before diagnosing any disease or recommending a therapy. A well known example is the embedded software in defibrillators which allows a 'shock' to be delivered, by analyzing the electrocardiogram for known conditions (heart attack). The shock can restart the heart and timely delivery can resuscitate the patient. As well as assisting in primary diagnosis, a DSS can reduce medical error, assist compliance with clinical guidelines, improve efficiency of care delivery and improve quality of care. Decision support still has significant acceptance issues in clinical routine, but can achieve more prominence, as systems are demonstrated to provide effective knowledge based support. Data mining is often used to provide some insight to a data set and update our accepted knowledge. In this section, we discuss a study which examines where electrocardiographic information should be recorded from a patient's torso in order to increase diagnostic yield.

This paper investigates optimal model selection for posture recognition. Accuracy and computational time are related to the trained model in a supervised classification. An optimal model selection is important for a reliable activity monitoring system. Conventional guidance on model training uses large instances of randomly selected data in order to characterize the classes. A new approach to the training of a multiclass support vector machine (SVM) model suited to limited training sets such as used in posture recognition is provided. This approach picks a small training set from misclassified data to improve an initial model in an iterative and incremental fashion. In addition, a two step grid-search algorithm is used for the parameters setting. The best parameters were chosen according to the testing accuracy rather than conventional validating accuracy. This new approach for model selection was evaluated against conventional approaches in an activity classification study. Nine everyday postures were classified from a belt-worn smart phone’s accelerometer data. The classification derived from the small training set and the conventional randomly selected training set differed in two aspects: classification performance to new data (85.1% Pick-out small training set vs. 70.3% conventional large training set) and computational efficiency (improved 28%).

Anytime-anywhere student support is a growing area of investigation as indicated by its inclusion in a range of University policies and academic research. A range of technologies can be applied to support teaching and learning, however they have limitations in providing students with anytime-anywhere support. This article proposes a holistic approach, which blends different available technologies in a Virtual Mentor. A Virtual Mentor is not normally intended to replace a human mentor but can offer support when a human mentor is not available. This new approach is contrasted with currently used technologies to show its advantages.

We have developed a personalised self management system to support self management of chronic conditions with support from health-care professionals. Accelerometers are used to measure gross levels of activity, for example walking around the house, and used to infer higher level activity states, such as standing, sitting and lying. A smart phone containing an accelerometer and a global positioning system (GPS) module can be used to monitor outdoor activity, providing both activity and location based information. Heart rate, blood pressure and weight are recorded and input to the system by the user. A decision support system (DSS) detects abnormal activity and distinguishes life style patterns. The DSS is used to assess the self management process, and automates feedback to the user, consistent with the achievement of their life goals. We have found that telecare and assistive technology is feasible to support self management for chronic conditions within the home and local community environments.

ReliefF has proved to be a successful feature selector but when handling a large dataset, it is computationally expensive. We present an optimization using Supervised Model Construction which improves starter selection. Effectiveness has been evaluated using 12 UCI datasets and a clinical diabetes database. Experiments indicate that compared with ReliefF, the proposed method improved computation efficiency whilst maintaining the classification accuracy. In the clinical dataset (20,000 records with 47 features), feature selection via Supervised Model Construction (FSSMC) reduced the processing time by 80%, compared to ReliefF, and maintained accuracy for Naive Bayes, IB1 and C4.5 classifiers.

The number of persons living with chronic conditions such as Chronic Obstructive Pulmonary Disease, Dementia and Stroke is projected to continue to rise in the decades ahead. One emerging approach to reducing the consequent socio-economic burdens is the self-management paradigm whereby a patient is empowered through education, goal setting, measurement and quantification of health metrics, and feedback. This empowerment enables a patient to effectively manage their own long-term conditions. Here we present the current generic data models for educational content provision, setting goals and recording measurements utilised in the KeepWell self-management technology platform. We provide implementation details for each component and consequently identify areas of future work.

Aging effects cause significant changes in the functionality and abilities of the elderly that affect the overall interaction of elder people with their environment and other people. However, since the aging process occurs gradually in a person’s life, it is not possible for humans who belong to younger generations to experience the effects of aging. Towards this end we propose the use of a Virtual Reality (VR) based application that allows users to experience the effects of aging before such changes affect their body and mind. The proposed application targets two main objectives: The first objective is to allow product and software designers to experience problems faced by the elderly so that they design products friendly to older people. The second objective is to increase the appreciation and social inclusion towards the elderly. The development of this application relies on an extensive study of the literature that describes biological changes due to aging, so that several age-related effects are simulated in a VR environment in order to allow users to virtually experience life as an older person. During an experimental evaluation users were requested to complete a simple task in the age simulated VR-environment using a VR headset, and the reactions of the users were registered using appropriate questionnaires. The results of a preliminary investigation show that users can have a better understanding and appreciation towards older people after they are exposed in a VR age simulated environment.

Brain-Neural Machine/Computer Interface BNCI has been used successfully as an assistive technology to restore communication, improve control and thus potentially enhance social inclusion. Recently BNCI technology and interfaces have evolved to become more usable, thereby allowing the recording of brain activity to become part of the wider self-quantification movement. A hybrid BNCI can provide a viable but alternative interface for Human Computer Interaction, which combines the inputs from BNCI and eye tracking. This hybrid approach has maintained information transfer rate but increased robustness and overall usability. The combination of two complementary technologies provides the possibility for investigating new ways of human enhancement and has the potential to open up new medical applications.

A Web portal was developed to assist in diabetes education and self-management. Access to reliable information is an essential component of effective self-care in diabetes. Education was provided as a series of learning objects, aimed at patients with either Type 1 or Type 2 diabetes. Where appropriate, material was also provided via multimedia learning tools, such as tutorials and interactive quizzes. Throughout the development of the Web portal, periodic assessment was carried out by an expert patient group. This comprised 10 individuals, four of whom had Type 1 diabetes and two with Type 2. The expert group provided feedback to the health-care professionals involved with content development. There were also regular discussion forums with both patients and health-care staff. The portal was designed to facilitate effective self-management, by providing ongoing support with regular feedback on progress.

This study aimed to confirm neuroaffective processing deficits in psychopaths by measuring late brain event-related potential (ERP) components and behavior in groups of psychopathic and nonpsychopathic inmates of a Singaporean prison while they performed two tasks. In a Categorization task, affective stimuli were task-relevant and required focused attention, while in a Vigilance task, affective pictures were presented in the background while participants discriminated vertical from oblique lines. Psychopaths showed differences in late positive ERPs that were sensitive to affective stimulus properties (valence and arousal) in the Categorization, but not in the Vigilance task, suggesting that only under conditions of focused attention did psychopaths show a neuroaffective processing deficit. In the Categorization task, psychopaths also showed a significantly larger prefrontal negative ERP (N350) whose amplitude correlated positively with the behavioral facet of psychopathy. In the Vigilance task, psychopaths both missed more targets and showed significantly smaller target-evoked parietal ERPs when viewing arousing pictures, suggesting their attentional focus was disrupted by the affective background.

Ambient Intelligence aims to enhance the way people interact with their environment to promote safety and to enrich their lives. A Smart Home is one such system but the idea extends to hospitals, public transport, factories and other environments. The achievement of Ambient Intelligence largely depends on the technology deployed (sensors and devices interconnected through networks) as well as on the intelligence of the software used for decision-making. The aims of this article are to describe the characteristics of systems with Ambient Intelligence, to provide examples of their applications and to highlight the challenges that lie ahead, especially for the Software Engineering and Knowledge Engineering communities. In particular we address system specification and verification for the former and knowledge acquisition from the vast amount of data collected for the latter. .

Background Despite its widespread use, the limitations of the 12-lead electrocardiogram (ECG) are undisputed. The main deficiency is that just a small area of the precordium is interrogated and for some abnormalities information may be transmitted to a region of the body surface where information is not recorded. In this study, we attempted to optimize the 12-lead ECG by using a data-driven approach to suggest alternate recording sites. Methods A sequential lead selection algorithm was applied to a set of 744 body surface potential maps (BSPMs), consisting of recordings from subjects with myocardial infarction, left ventricular hypertrophy, and no apparent disease. A number of scenarios were investigated in which pairs of precordial leads were repositioned; these pairs were V3 and V5, V4 and V5, and V4 and V6. The algorithm was also used to find optimal positions for all 6 precordial leads. Result Through estimation of entire surface potential distributions it was found that each of the scenarios, with 2 leads repositioned, captured more information than the standard 12-lead ECG. The scenario with V4 and V6 repositioned performed best with a root mean square error of 22.3 microvolts and a correlation coefficient of 0.967. This configuration also fared favorably when compared to the scenario where all 6 precordial leads were repositioned as optimizing all 6 leads offered no significant improvement. Conclusion This study demonstrated the use of a lead selection algorithm in enhancing the 12-lead ECG. The results also indicated that repositioning just 2 precordial leads can provide the same level of information capture as that observed when all precordial leads are optimally placed.

This paper provides a review of technology that has emerged to facilitate pervasive brain-computer interface applications. Such technology is beginning to be applied to the human-computer interface, as part of multimodal interaction. As the technology taps into brain state and provides an associated quantitative assessment, it has potential for the real-time assessment of a person's cognitive state. As such, many emerging applications could be addressed such as the assessment of learning. In the future, it may even be possible to tune learning materials in a manner appropriate to the state of a user using a feedback loop. In particular, the interaction of brain-computer interface technology with technologies such as augmented reality holds some promise, however, the technology is as yet unverified beyond lifestyle and gaming interaction. Consequently, this paper aims to review low-cost, commercial brain-computer interface technologies and posits their use as an interaction modality within future learning environments.

Introduction: Patient monitoring is both a prevalent and critical nursing duty. Given that it requires the interpretation of vital signs and intricate decision-making, nurses could benefit from simulation-based training. Currently there is a lack of an open data structure for capturing training scenarios that can be used to augment simulation software and virtual reality applications.

Androgen receptor (AR) signalling and the PI3K pathway mediate survival signals in prostate cancer, and have been shown to regulate each other by reciprocal negative feedback, such that inhibition of one activates the other. Understanding the reciprocal regulation of these pathways is important for disease management as tumour cells can adapt and survive when either single pathway is inhibited pharmacologically. We recently carried out genome-wide exon-specific profiling of prostate cancer cells to identify novel androgen-regulated transcriptional events. Here we interrogated this dataset for novel androgen-regulated genes associated with the PI3K pathway. We find that the PI3K regulatory subunits PIK3R1 (p85α) and PIK3R3 (p55γ) are direct targets of the AR which are rapidly repressed by androgens in LNCaP cells. Further characterisation revealed that the PIK3CA p110α catalytic subunit is also indirectly regulated by androgens at the protein level. We show that PIK3R1 mRNA is significantly under-expressed in prostate cancer (PCa) tissue, and provide data to suggest a context-dependent regulatory mechanism whereby repression of the p85α protein by the AR results in destabilisation of the PI3K p110α catalytic subunit and downstream PI3K pathway inhibition that functionally affects the properties of prostate cancer cells.

In the not too distant future, the median population age will tend towards 65; an age at which the need for dependency increases. Most older people want to remain autonomous and self-sufficient for as long as possible. As environments become smarter home automation solutions can be provided to support this aspiration. The technology discussed within this paper focuses on providing a home automation system that can be controlled by most users regardless of mobility restrictions, and hence it may be applicable to older people. It comprises a hybrid Brain-Computer Interface, home automation user interface and actuators. In the first instance, our system is controlled with conventional computer input, which is then replaced with eye tracking and finally a BCI and eye tracking collaboration. The systems have been assessed in terms of information throughput; benefits and limitations are evaluated.

Three RFID reader based network deployment algorithms (grid-covering, diagonal and mixed) were evaluated in this paper. Experimental results show that the grid-covering method can be used to minimize hardware costs, but it leads to many indeterminate positions. The diagonal method can be used to solve the indeterminate problem, however increases the number of readers, especially in a large tracking field. The mixed algorithm can be used to avoid the indeterminate issue and also has the minimum reader number when deployed in a large space. However, it is not suitable for a small tracking field. An optimal deployment algorithm is selected from these three algorithms according to the environmental conditions and the localization requirement. In addition, an optimal RFID reader network deployment combined with a subarea-mapping algorithm can be used to minimize the hardware costs while improving the fine-grained indoor localization accuracy.

In this paper, we present the design and development of a smart insole that may be used to assess long term chronic conditions that affect the elderly population such as Stroke, Dementia, Parkinson;s disease, Cancer, Cardiac Disease and Diabetes. This smart insole offers the potential for evidence base rehabilitation. The ICT solution detect the plantar foot pressure in a free living context through the integration of piezo sensors, microcontroller and Bluetooth technology to empirically measure the pressure at important pressure points. The insole consists of 32 piezo sensors, 01 tri-axial accelerometers, temperature sensor and force sensor to automatically switch ON/OFF the insole. The accelerometers provide context for orientation. The design comprises two flexible PCBs encased in a padded layer, in order to protect the sensors and provide comfort to wearer.

Brain Computer Interface (BCI) technology has achieved limited success outside of laboratory conditions. This technology is hindered by practical considerations of set up, lack of robustness and low Information Transfer Rate (ITR). There are two interfaces in a BCI system: the brain’s interface with the computer and the computer-environment interface, which provides access to applications for the user. Three user services were implemented: control of the smart home, entertainment and communication. These may be accessed through a graphical user interface controlled by a BCI. The paper contrasts the performance of an SSVEP based system with a hybrid BCI comprising eye gaze and muscle response (measured at the scalp). The hybrid developed utilizes the EPOC for recording electrical potential and an EyeTribe gaze tracker; these can be combined to provide more robust interaction with applications. Average ITR for the eye tracker and hybrid approaches (190-200 bpm) are higher than for our SSVEP approach (approx. 15 bpm), for the same applications. The poor performance of our SSVEP system was due to the temporal duration of the stimulation (7s) and partly because not all participants could achieve an accuracy of greater than 50%. The current challenge is the replacement of the scalp recorded muscle component with a reliable user modifiable EEG measure.

Within a technological home-based self-management of dementia paradigm there exists a nexus between a person with dementia, personalised reminding technology, detection of the adherence to reminders and an additional human-in-the-loop, for example a caregiver. To date, much work has focused on either the reminding technology per-se or monitoring adherence with less emphasis on integrating these aspects into a coherent self-management system for dementia. Within this paper, we present our current work on closing the remind-sense-reason-act loop. The proposed framework is outlined and we describe an early-stage prototype that incorporates; (i) mobile-based reminding technology, (ii) detection of adherence by using environmental sensors and (iii) the potential to contact a carer in the event of non-compliance.

Contextual information within smart environments varies greatly. Whilst upper-level ontologies for contextual information relating to smart environments have been previously proposed, they have typically only been applicable to specific domains. As such, a wide range of entity names are used, overlooking other important relevant entities. This paper presents a conceptualisation and generation of an upper-level ontology,which encompasses the key factors, deemed important for context within smart environments. These factors include User, Object, Location, Natural Element, Application, Event and Temporal. The use of the model to support high-level context generation is also discussed in the form of an indicative short vignette (an envisaged smart environment)accompanied with the results from a real smart environment implementation (intelligent meeting room).

This paper addresses the potential of the Brain Computer Interface (BCI) for self-quantification through recording and analysis of brain activity. From the electroencephalographic (EEG) signal it is possible to quantify and investigate brain activity, allowing, for example, a measure of engagement with tasks to be derived, states of relaxation or anxiety to be determined, or levels of alertness to be assessed. This can be of particular use in areas such as immersive education, where an objective measure of task engagement would be of value. As such it may be possible to measure engagement but also to identify people who may not be able to engage fully, such as people with dyslexia.

In this paper we present a design, development and evaluation of a tool to facilitate the reminiscence of life-logged data intended for persons with dementia. Using off-the-shelf technologies such as a smartphone it is possible to effectively record and log a person's daily activates such as places visited and persons interacted with. We developed a reminiscence tool that visualizes life-logging data. The system was evaluated with six healthy participants aged between 24–46 years of age. They recorded approximately 2.5 hours of life-logging data that was later loaded into the developed tool. During the subsequent evaluation, the participant's gaze was tracked using eye-tracking hardware. Results show that the tool was easy to use and navigate through, facilitating the objective identification of events that may be useful for reminiscence.

Self-management is an approach to healthcare which aims to empower individuals to manage their own health conditions. This is of particular importance given the shifting demographics, increased prevalence of chronic conditions and financial austerity facing many countries. In this paper we present our current work on the development of a flexible, generic self-management platform which can be readily extended for specific chronic conditions. A Unified Modelling Language (UML) system class diagram is presented with the class design based upon functional requirements derived from literature and engagement with key stakeholders. We subsequently extend the UML class diagram to demonstrate how the design may be adapted for specific conditions.

A video electroencephalogram (EEG) is the gold standard test for the monitoring of long term epilepsy, differentiating types of epilepsy and investigations of non-epileptic seizures. The use of video EEG in current practice is significantly limited by cost and non-availability of resources, causing delays for patients. This development addresses whether the limitations can be overcome by mobile technology augmented by a web based ICT solution. The experiment demonstrated that disadvantages of hospital based video EEG can be addressed by using ambulatory EEG and IP cameras at the subject's home. The data obtained can potentially be of diagnostic significance, given sufficient technological rigor.

Human event-related potentials reflect cognitive processing, and are normally elicited by external events, such as acoustic sounds or visual stimuli. As such they provide an opportunity to study normal and abnormal brain function noninvasively, at sub-second resolution. Advances in multimedia technology permit specialists in informatics and neuropsychology to co-operate in the design and implementation of paradigms, which influence ERP components. The paper illustrates the progression from standard paradigms such as the auditory oddball, which can be used to study memory update through to contingent negative variation and three condition, visual paradigms which can be used to study cognitive and emotional responses. Data from a study investigating the comparative processing of target pictures and words illustrate how external stimuli influence the later cognitive potentials.

This study tested the hypothesis that a history of chronic alcohol abuse is the key variable underlying the previously reported association between criminal recidivism, impulsivity and differentiation of the Go/NoGo contingent negative variation (CNV). Forty male Singaporean prison inmates were assessed for presence of antisocial personality disorder (APD), a history of alcohol and other substance abuse, and psychopathy. CNV was recorded in the Go/NoGo paradigm in groups comprising inmates with (ALC+: N = 29) and without (ALC−: N = 11) a history of alcohol abuse. As predicted, the ALC+ group showed significantly degraded Go/NoGo CNVs (poor differentiation). This suggests that the “high risk” group of mentally disordered found to recidivate in studies by Howard and Lumsden (1996; 1997) may have shared a history of alcohol abuse. Results are discussed in the context of a developmental biopsychosocial model of risk for engaging in criminal behaviour.

A procedure to quantify the relationship between the signal-to-noise ratio (SNR) and the number of observed series of the auditory brainstem response is discussed. The commonly held belief that the SNR increases as the number of terms being averaged originates from the assumption that repeated series of the evoked potential are perfectly correlated. It is shown that in practice the correlation is far from perfect and this leads to an underestimate of the number of observations which are required to obtain a desired SNR. The presented formula quantifies this dependency for all values of the cross-correlation between series, and reduces to the accepted form for perfectly correlated signals.

In this paper, we look at how the early detection of health decline in those with Chronic Obstructive Pulmonary Disease can lead to earlier provision of intervention advice. Current tools to support the self-management of Chronic Obstructive Pulmonary Disease provide alerts only when a problem has been encountered. It is suggested that continuous monitoring of a user's health, behavior and contextual information when appropriate would provide the ability to detect health decline prior to a problem having occurred. Based on the review of the current body of work, important parameters for self-management have been identified; autonomy, methods of data transmission, and levels of intrusiveness. An autonomous context aware self-management tool is subsequently proposed. The approach aims to incorporate recent advances in mobile technology and overcome many of the limitations encountered in the solutions considered to date.

In this work we explore whether the Brain-Computer Interface (BCI) can become a pervasive technology. The primary goal of BCI technology has been to provide communication and control for people with severe neural dysfunction, which affects their ability to interact with the environment. BCI research has focused largely on noninvasive, electroencephalography (EEG) based BCI methods. A number of EEG consumer grade devices are now available; the Emotiv EPOC neuroheadset is one such device. This paper investigates the utility, performance and usability of this device in an office setting by using EEG correlates of bio-signals for control. The results suggest that it is possible for some users to achieve reasonable control with only minutes of training. However sustaining engagement over a longer duration and widespread use provide suitable areas for further investigation. As a contribution to the Doctoral Colloquium the paper provides an account of intended contributions of this BCI research, the plan of study necessary to achieve this goal, and enumerates the major research questions to be addressed in the next two years. The longer-term aim is to deploy BCI as a Pervasive technology.

By 2030 it has been predicted that Chronic Obstructive Pulmonary Disease (COPD) will affect, on a global scale, over 64 million people and will be the third leading cause of death worldwide. This work discusses the development of a self-management system that records physiological and contextual information via a smartphone, a wrist worn accelerometer for measuring activity and a chest worn device for monitoring patterns of respiration. The data recorded through this system facilitates analysis to identify changes in the patterns of the bio-signals and may pave the way for advice to be offered regarding the self-management of this long-term condition. Feedback relating to specific activity goals that can be set and monitored will be provided to the user as part of the self-management solution

GPS-enabled mobile devices can utilize location information to potentially provide a safer tracked environment allowing vulnerable people to continue with their daily activities, as much as possible. This paper presents the options for alert escalation. The aim is to provide a safety net, without triggering unnecessary alarms. The escalation procedure involves initial speech alert to the user, then a speech and vibrate alert to the user as a reminder; this is followed by a text message to an identified carer if the user has not re-entered the designated safe zone. Parameters for alert escalation can be tuned to individual circumstances. The user can seek help by getting directions from the current position to home or by calling a carer. We report on a small user evaluation (n=6), an essential pre-requisite to testing with the intended cohort.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative neurological condition categorized as an orphan disease and at present the primary treatment is managing symptoms. It leads to severe paralysis, resulting in the need for the patient to use assistive technologies to support them in their daily activities. When the condition is severe, mainstream technologies may no longer offer the support required, due to the need for reliable residual movement. Brain computer interfaces (BCI) have the potential to become a powerful assistive technology for some individuals with the most severe of neuromuscular disorders. With only ‘thought’ as an input medium the user could harness control and communication. Undoubtedly, the availability of such technology could have a major positive impact on the life of a patient with ALS, supporting their inclusion in the world and contact with people around them. However, despite decades of research and development, BCIs are still not commonplace. Many recent advances have been made but some factors still prevent widespread deployment of BCI. This chapter will introduce the background of BCI and provide a short discussion about the problems associated with BCI technology, balanced with thoughts about its potential, challenges and hopes for the future.

Technology could support the self-management of long-term health conditions such as chronic pain. This article describes an evaluation of SMART2, a personalised self-management system incorporating activity planning and review, feedback on behaviour- and acceptance-based therapeutic exercises.The SMART2 system was evaluated over a four-week trial in the homes of people in chronic pain. At conclusion, participants were interviewed to understand the experience of using and living with the SMART2 system as a therapeutic tool.Qualitative analysis of the interviews found that participants liked the system and reported making associated changes to their behaviour. Goal setting and feedback were the most useful elements of the system. A third key and unexpected element was that by simulating some of the functions of a therapist, SMART2 also simulated some of the process of interacting with a therapist.People in chronic pain may experience positive outcomes when using a self-management system designed for behaviour change. Furthermore, some of the supportive aspects of the therapeutic context can be elicited by self-management technologies. Implications of Rehabilitation Self-management technology has the potential to assist rehabilitation by supporting goal setting and providing feedback. By simulating some of the functions of a therapist, technology can simulate some of the process of therapy during rehabilitation. People in chronic pain liked using the technology in their own home and thought it could augment services delivered by clinical practitioners. Complex programmes of therapeutic exercises delivered by technology had limited success in engaging people in chronic pain.

A real-time fall detection system is proposed to distinguish various falls during daily activities. Falls are detected in two steps: first a hierarchical algorithm is used to classify the motion and motionless postures such as lying, sit-tilted, sit-upright, standing and walking; it then analyzes whether the current lying or sit-tilted postures are normal or abnormal, based on posture transition and users' current position. If an abnormal lying or sit-tilted posture is determined, a fall alert will be delivered immediately; if a possible fall is raised (such as normal lying but on the ground), then a music based alert starts playing, and a fall or normal lying will be determined according to whether the user stops the alert music. The advantages of the approach are that it can distinguish various falls efficiently (in real-time within a smart phone), and can also significantly improve the “true positives” for the slow falls with a sit-tilted posture, as well as the “true negatives” for the normal lying compared to the existed fall detection algorithms.

Science and design are two completely separated areas of expertise with their own specialists. Science analyses the existing world to create new knowledge, design uses existing knowledge to create a new world. This tunnel-vision mentality and narrow-minded approach is dangerous for problem solving, where a broad view on potential solutions is required to realise a high-quality answer on the defined problem.We state that design benefits from scientific methods, resulting in a more effective design process and in better products, while science benefits from a design approach, resulting in more efficient and effective results. Our philosophy is illustrated using examples from the field of biomedical engineering.Both methods can benefit tremendously from each other. By applying scientific methods, superior choices will be made in the design process. With design, more accurate, effective and efficient science will be performed. (C) 2013 Elsevier Ltd. All rights reserved.

The genes AF10 and AF17 have been identified as the basis of the t(10;11) and t(11;17) translocations, events that result in their fusion to the MLL/HRX gene in acute myeloid leukaemias. AF10 and AF17 bear significant homology to each other within their putative zinc finger and leucine zipper domains, although they are diverged outside these regions. The BR140 gene encodes a 140 kDa protein of unknown function that contains a putative zinc finger domain, a leucine zipper region, and, in addition, a bromo domain. The zinc finger and leucine zipper domains of BR140 have significant homology to those of AF10 and AF17, suggesting that it belongs to this newly described gene family and, therefore, could be a target for chromosome translocation. To assess the potential involvement of BR140 in chromosome translocations in leukaemia, the chromosomal location of the BR140 gene has been determined by using several independent methods. A combination of Southern analysis, polymerase chain reactions (PCR) on monochromosomal cell hybrids, and fluorescence in situ hybridisation (FISH) has been used to show that the BR140 gene maps to chromosome band 3p25.

Brain Computer Interface (BCI) technology has been used successfully in neurophysiological research laboratories, but has had less success when used outside the laboratory and particularly for people with disability. The hybrid BCI approach offers the potential for a more robust solution, with potential better usability to promote greater acceptance. The emphasis on improving human computer interaction may facilitate more widespread deployment, particularly where BCI alone has proved unsuccessful. This paper adapts an existing modular BCI architecture to support a 'mixed hybrid', by combining a BCI with a commercial eye tracker, and suggests graphical user interfaces to facilitate operation and control of a virtual environment.

Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p

Walking is often cited as the best form of activity for persons over the age of 60. In this paper we outline the development and evaluation of a smart garment system that aims to monitor the wearer's wellbeing and activity regimes during walking activities. Functional requirements were ascertained using a combination of questionnaires and two workshops with a target cohort. The requirements were subsequently mapped onto current technologies as part of the technical design process. In this paper we outline the development and second round of evaluations of a prototype as part of a three-phase iterative development cycle. The evaluation was undertaken with 6 participants aged between 60 and 73 years of age. The results of the evaluation demonstrate the potential role that technology can play in the promotion of activity regimes for the older population.

Technology has been adopted to mitigate adverse effects associated with aging. Ambient Assisted Living solutions provide user assistance and support which is potentially both efficient and effective. This paper describes initial evaluation results for a Multi-Agent system prototype that provides assistance and support during the day and night through an interface that is adapted according to a person's requirements profile, time of day and current activity or event. Appropriate feedback based on user context is important. This includes historical feedback which may indicate trends, which may not be apparent, particularly where the user may be forgetful.

Brain Computer Interaction (BCI) has mainly been utilized for communication and control, but it may also find application as a channel for creative expression, as part of an entertainment package. In this paper we provide an initial investigation on how creativity can be supported and assessed. An art-based approach was adopted to investigate the effects of achieving simple drawing and painting. Subjects were asked to complete three tasks using an Steady State Visual Evoked Potential BCI; a drawing task called 'etch-a-sketch' (TASK 1) which relied entirely upon BCI control, and two painting tasks, the first (TASK 2) with a set goal and the second (TASK 3) with more potential for user expression. The tasks varied in the proportion of control to creativity required. Participants provided feedback on their perception of the control and creative aspects and their overall experience. The painting application (TASK 3) for which users perceived that they had more creativity was well accepted; 50% of the users preferred this mode of interaction. The experimental approach described allows for an initial assessment of the acceptance of BCI-mediated artistic expression.

Altered activity patterns are often the general symptoms experienced by people with dementia. The quantity and quality of patient's daily activities such as their sleeping may be a reflection of their dementia condition and affect their quality of life. Monitoring patient's activity patterns over different periods of time may help healthcare professionals with determining the patient's cognitive impairment stage. In this research, we describe an activity pattern detection and visualization system developed to support the monitoring and assessment of activity patterns for people diagnosed with dementia, at the early stages of the disease. Analysis shows that rich information embedded in sensory data can provide useful knowledge for understanding patients' activity patterns, detect unusual events and may also be useful for examining cognitive status.

Walking is cited as the best form of exercise for persons over the age of 60. In this paper we outline the development and evaluation of a smart garment system that is used to monitor the wellbeing of users in addition to their overall physical activity regimes. A technological solution has been produced offering the desired functionality and is delivered in a simple user-friendly form factor. In this paper we outline the development and evaluation of a prototype as part of an iterative development cycle, with 6 participants aged between 60 and 73 years of age. The results show that technology has the potential to be accepted by older users and improve their activity regimes.