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The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.

There is a global challenge related to the increasing number of People with Dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring and interventions have been investigated comprehensively in the literature. The objective of this study was to investigate the challenges with the design and deployment of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future SHIB implementations to have an adequate and prompt deployment allowing research to focus on the data collection and analysis aspects. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. The SHIB was evaluated via installation in the rooms of PwD with varying degrees of dementia at Kirk House Care Home in Belfast. Sensors from the SHIB were installed to test their capabilities for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data from the sensors were collected, stored, and handled using a ‘SensorCentral’ data platform. The results of this study highlight challenges and opportunities that should be considered when designing and implementing a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support monitoring the wellbeing of PwD. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of PwD were also not designed for an appropriate integration with ambient sensors. This study provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach.

This paper presents a study which evaluated the potential for using ultra-low altitude, unmanned aerial vehicles to deliver fifth-generation (5G) cellular connectivity, particularly into areas requiring short-term enhancement in coverage. Such short-term enhancement requirements may include large gatherings of people or during disaster scenarios where there may be service outages or a need for increased bandwidth. An evaluation of this approach was conducted with empirically generated results regarding signal quality and cellular coverage—illustrating the potential of using unmanned ultra-low altitude aerial vehicles to deliver 5G cellular mobile services. Specifically, channel gain, mean time delay of the received signals ( $\tau _{\textrm {mean}}$ ), and the root-mean-square spread of the delay ( $\tau _{\textrm {rms}}$ ) were investigated for two distinct user modes at three different drone heights for three selected environments—an open area (field), a tree-lined environment, and an enclosed area. Maximum likelihood estimates for the various drone heights, user modes, and operational environments were found to be Rician distributed for the received signal strength measurements, whereas $\tau _{\textrm {mean}}$ and $\tau _{\textrm {rms}}$ for the open and tree-lined environments were Weibull distributed with the enclosed area tests being lognormally distributed. The paper also investigates how the channel gain may be affected when operating in each of the various global bands allocated for mid-5G communications, namely, Europe, China, Japan, South Korea, and North America. These regional mid-5G band allocations were found to yield minimal variance for all the environments considered.

Research interests spanning numerous domains increasingly rely upon computational systems which can store and process a large volume of variable data that is stored at high velocity-representing a big data problem. This is particularly notable within the domain of ubiquitous and pervasive computing. This domain increasingly relies on storage and retrieval of sensor data to enable outcomes such as predictive analytics and activity recognition. Several current big data platforms exist; however, they have a range of deficiencies including lack of generic interoperability with agnostic sensors and an absence of features supporting academic research. Due to these deficiencies a custom, research oriented, high performance, and big data platform was devised and implemented. This platform is called SensorCentral and is presented within this paper. SensorCentral provides a framework which enables interoperability with a large range of agnostic sensor devices whilst simultaneously providing features which support research. Research supporting features include; facility to define experiments, ability to annotate experimental instances via purpose-built mobile applications, integrated machine learning functionality, facility to export data sets, rule-based classification and an extensible platform. The flagship implementation of this platform has been in operation for over 28 months within a University research group and has been successfully integrated with a range of sensors from a variety of manufacturers. This implementation currently stores over 850 million records and has been central to several research and industrial projects. Future work will integrate this platform into the open data initiative enabling collaboration with the international community of researchers.

INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving thataffects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxietyand remain for extended periods, which affects their quality of life.OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during themanifestation of anxiety in patients with dementia.METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structuredinterviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, wedeveloped the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio torepresent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety inpatients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patientsduring episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network modeltrained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the twogroups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved frompublicly available datasets.RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitativeresults from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network andSupport Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classificationaccuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%.CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre,qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of amethodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude bysignalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

The global population is ageing, as a consequence of this there will be a greater incidence of ageing related illnesses which cause cognitive impairment–such as Alzheimer’s disease. Within residential care homes, such cognitive impairment can lead to wandering of individuals beyond the boundaries of safety provided. This wandering, particularly in urban areas can be life threatening. This study introduces a novel solution to detect, and alert caregivers of, egress of at-risk inhabitants of a care home. This solution operates through a combination of wearable Bluetooth beacons and beam-formed listening devices. In an evaluation process involving 275 egress events, this solution proved to offer accurate operation with no incidence of false positives. Notably, this solution has been deployed within a real residential care home environment for over 12 months. Proposed future work discusses improvements to this solution.

Background: Musculoskeletal injuries are common and costly. Ankle sprains are one of the most common such injuries, carrying significant risk of persistent disabling symptoms. Rehabilitation has been shown to be more effective than standard conservative approaches for musculoskeletal injuries. The use of a mobile app to present rehabilitation guidance may offer a more accessible, engaging and effective solution to ankle sprain rehabilitation. Several existing apps target the prevention and rehabilitation of ankle sprains, however evaluation details are limited. Aim: To develop a mobile phone app that provides interactive, personalised and tailored rehabilitation for people following ankle sprains. Method(s): In order to build requirements and produce content for the app (entitled ReApp 2), relevant literature was reviewed and behaviour change strategies identified from the Behaviour Change Taxonomy (BCT) developed by Michie et al (2013). A flexible and team orientated methodology called Agile was used to refine the technical specification and implemented software using feedback from technical experts (n=5) and clinicians (n=6). Technical experts consisted of participants with at least five years of experience in ICT or a related discipline. These technical experts completed the System Usability Scale (SUS) to rate the usability of the software. The SUS is a widely used usability survey, which provides a single overall score for each participant. These scores can be used to provide comparison between user groups and other software. The clinicians consisted of five physiotherapists and one sport and exercise consultant, all of whom had at least five years of experience in providing rehabilitation treatment for ankle injuries. These clinicians also completed a questionnaire and gave qualitative feedback on the clinical appropriateness of the app. Results: The identified BCTs were prioritised according to clinical importance, technical complexity and time to implement, and video and voice over content was completed. The following strategies were identified to incorporate into the app: modelling of behaviour; monitoring and receiving feedback related to individual behaviour, monitoring and receiving feedback related to the outcome of individual behaviour; and receiving reminders. ReApp 2 received a mean SUS score of 86, which is considerably above the average of 69.69 reported in Bangor et al.’s (2008) review of 2324 surveys from 206 studies. The technical experts indicated that the usability of ReApp 2 was considered to be “excellent” (SUS score over 85.58) by 60% of participants, and “good” by the remaining 40% (SUS score over 72.75). Clinicians agreed or strongly agreed that the clinical content within the app was evidence based (n=3), that the clinical content within the app was suitable for ankle rehabilitation (n=4) and that they would recommend the app to a patient presenting with ankle sprain (n=3). Conclusions: The SUS scores received from the technical evaluation in this project are considerably above average, indicating the excellent-good usability of the app. Clinicians rated the app as evidence based and 50% of clinicians would recommend the app in its current format to patients with ankle sprain.