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BACKGROUND Contact tracing is considered a key measure in preventing the spread of infectious diseases. Governments around the world adopted contact tracing to limit the spread of COVID-19 in schools. Contact tracing tools utilizing digital technology (eg, GPS chips, Bluetooth radios) can increase efficiency compared to manual methods. However, these technologies can introduce certain privacy challenges in relation to retention, tracking, and the using and sharing of personal data, and little is known about their applicability in schools. OBJECTIVE This is the second of two studies exploring the potential of digital tools and systems to help schools deal with the practical challenges of preventing and coping with an outbreak of COVID-19. The aim was to explore the views, needs, and concerns among secondary school stakeholders (parents, teachers, pupils) regarding the implementation of three digital tools for contact tracing: access cards, proximity tracking, and closed-circuit television (CCTV). METHODS Focus groups and interviews were conducted with secondary school students, parents, and teachers. The topic guide was informed by the Unified Theory of Technology and Acceptance. Data-driven and theory-driven approaches were combined to identify themes and subthemes. RESULTS We recruited 22 participants. Findings showed that there is no single solution that is suitable for all schools, with each technology option having advantages and limitations. Existing school infrastructure (eg, CCTV and smart/access cards technology) and the geography of each school would determine which tools would be optimal for a particular school. Concerns regarding the cost of installing and maintaining equipment were prominent among all groups. Parents and teachers worried about how the application of these solutions will affect students’ right to privacy. Parents also appeared not to have adequate knowledge of the surveillance technologies already available in schools (eg, CCTV). Students, who were mostly aware of the presence of surveillance technologies, were less concerned about any potential threats to their privacy, while they wanted reassurances that any solutions would be used for their intended purposes. CONCLUSIONS Findings revealed that there is not one tool that would be suitable for every school and the context will determine which tool would be appropriate. This study highlights important ethical issues such as privacy concerns, balancing invasions of privacy against potential benefits, transparency of communication around surveillance technology and data use, and processes of consent. These issues need to be carefully considered when implementing contact tracing technologies in school settings. Communication, transparency, and consent within the school community could lead to acceptance and engagement with the new tools.

This paper presents Vesta, a digital health platform composed of a smart home in a box for data collection and a machine learning based analytic system for deriving health indicators using activity recognition, sleep analysis and indoor localization. This system has been deployed in the homes of 40 patients undergoing a heart valve intervention in the United Kingdom (UK) as part of the EurValve project, measuring patients health and well-being before and after their operation. In this work a cohort of 20 patients are analyzed, and 2 patients are analyzed in detail as example case studies. A quantitative evaluation of the platform is provided using patient collected data, as well as a comparison using standardized Patient Reported Outcome Measures (PROMs) which are commonly used in hospitals, and a custom survey. It is shown how the ubiquitous in-home Vesta platform can increase clinical confidence in self-reported patient feedback. Demonstrating its suitability for digital health studies, Vesta provides deeper insight into the health, well-being and recovery of patients within their home.

The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference. We enhance and experimentally evaluate the Time-Slotted Channel Hopping protocol from the IEEE 802.15.4 standard to address these challenges. Our contributions are a new schedule and an adaptive channel selection mechanism to increase the performance of time-slotted channel hopping in this domain. Evaluation in a test house shows that the enhanced system is suitable for our e-Health application and compares favorably with state-of-the-art options. The schedule provides higher reliability compared with the minimal scheduling function from the IETF 6TiSCH Working Group and has a better energy-efficiency/reliability tradeoff than the Orchestra scheduler. Results from 29 long-term residential deployments confirm the suitability for the application and show that the system is able to adapt and avoid channels used by WiFi. In these uncontrolled environments, the system achieves 99.96% average reliability for networks that generate 7.5 packets per second on average.

Background: The emergence of new technologies measuring outcomes in Parkinson’s disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients’ own homes whilst they freely live and carry out normal day-to-day activities. Objective: This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used. Methods: Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants. Results: 65 studies were selected for data extraction. There were wide varieties of participant sample sizes (

BACKGROUND Throughout the pandemic, governments around the world have issued guidelines to manage the spread and impact of Covid-19 in schools, including measures around social distancing and contact tracing. Whether schools require support to implement these guidelines has not yet been explored in depth. Despite the development of a range of technologies to tackle Covid-19, such as contract tracing apps and electronic vaccine certificates, research on their usefulness in school settings is limited. OBJECTIVE The aim of the study was to explore the needs of school staff in managing Covid-19 and their experiences and perspectives on technological support METHODS Semi-structured interviews were conducted with UK school staff, including primary and secondary school teachers and school managers. Thematic analysis was used to analyse the data. RESULTS Via purposive sampling, we recruited 18 participants from 5 schools. Participants reported high-risk interactions between students were still taking place, especially in secondary schools, and these were not adequately monitored. There was a need to improve accuracy when identifying close contacts in common areas where students congregate. Proximity Tracking, use of Access Cards and CCTV emerged as potential solutions but there were concerns surrounding false alerts, burden, and security. CONCLUSIONS School staff have found it difficult to monitor and implement social distancing and contact tracing provisions. There are opportunities for mobile digital technologies to support school staff in keeping their students safe, however these must place minimal demands on staff and prioritize security measures.

An annotated dataset of measurements obtained using the EurValve Smart Home In a Box (SHIB) rehabilitation monitoring system is presented. The SHiB is a low cost and easily deployable kit designed to collect data from a wrist-worn wearable in a home environment. The data presented is intended to evaluate room level indoor localization methods. The wearable device registers tri-axial accelerometer measurements which are sampled and transmitted as the payload of a Bluetooth Low Energy (BLE) packet. Four receiving gateways, each placed in a different room throughout a typical residential house, extract the accelerometer data and determine a Received Signal Strength Indicator (RSSI) for each received BLE packet. RSSI values can represent propagation losses due to distance or shadowing between the wearable transmitter and the gateway receiver.The dataset is presented in two parts. The first is composed of four calibration or training sequences, carried out by ten participants to offer ground truth calibrations for four rooms in the house. We refer to the calibration phase as the steps taken to gather training data. The calibration procedure was designed to be as straight-forward as possible, to allow a participant to adequately train the SHiB system without supervision. Ten participants each carried out a straight forward calibration procedure once, with four participants carrying out the calibration twice, on different occasions. One participant carried out the calibration on a third occasion.The second part of the data consists of a free-living experiment that was carried out over a period of five and a half hours starting at 7.37 a.m. Of this, one and a half hours of measurements are recorded within a room containing a gateway, where one participant carried out activities of daily living while their ground-truth location was accurately annotated within each room with a gateway present. The calibration data can be used as a training scheme and the living data as a test scenario.The dataset can be found at https://github.com/rymc/a-dataset-for-indoor-localization-using-a-smart-home-in-a-box Keywords: Localization, RSSI, BLE, Machine learning

Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoencoder is typically connected with a clustering network, and the final clustering is jointly learned by both the autoencoder and clustering network. Instead, we propose to learn an autoencoded embedding and then search this further for the underlying manifold. For simplicity, we then cluster this with a shallow clustering algorithm, rather than a deeper network. We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters. We quantitatively show across a range of image and time-series datasets that our method has competitive performance against the latest deep clustering algorithms, including out-performing current state-of-the-art on several. We postulate that these results show a promising research direction for deep clustering. The code can be found at https://github.com/rymc/n2d, Comment: Accepted at ICPR 2020

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work.

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate.Methods and Analysis: This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and silhouette video sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ or ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews. Ethics and Dissemination: Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate.

Background Ubiquitous, smart technology has the potential to assist humans in numerous ways, including with health and social care. COVID-19 has notably hastened the move to remotely delivering many health services. A variety of stakeholders are involved in the process of developing technology. Where stakeholders are research participants, this poses practical and ethical challenges, particularly if the research is conducted in people’s homes. Researchers must observe prima facie ethical obligations linked to participants’ interests in having their autonomy and privacy respected. Objective This study aims to explore the ethical considerations around consent, privacy, anonymization, and data sharing with participants involved in SPHERE (Sensor Platform for Healthcare in a Residential Environment), a project for developing smart technology for monitoring health behaviors at home. Participants’ unique insights from being part of this unusual experiment offer valuable perspectives on how to properly approach informed consent for similar smart home research in the future. Methods Semistructured qualitative interviews were conducted with 7 households (16 individual participants) recruited from SPHERE. Purposive sampling was used to invite participants from a range of household types and ages. Interviews were conducted in participants’ homes or on-site at the University of Bristol. Interviews were digitally recorded, transcribed verbatim, and analyzed using an inductive thematic approach. Results Four themes were identified—motivation for participating; transparency, understanding, and consent; privacy, anonymity, and data use; and trust in research. Motivations to participate in SPHERE stemmed from an altruistic desire to support research directed toward the public good. Participants were satisfied with the consent process despite reporting some difficulties—recalling and understanding the information received, the timing and amount of information provision, and sometimes finding the information to be abstract. Participants were satisfied that privacy was assured and judged that the goals of the research compensated for threats to privacy. Participants trusted SPHERE. The factors that were relevant to developing and maintaining this trust were the trustworthiness of the research team, the provision of necessary information, participants’ control over their participation, and positive prior experiences of research involvement. Conclusions This study offers valuable insights into the perspectives of participants in smart home research on important ethical considerations around consent and privacy. The findings may have practical implications for future research regarding the types of information researchers should convey, the extent to which anonymity can be assured, and the long-term duty of care owed to the participants who place trust in researchers not only on the basis of this information but also because of their institutional affiliation. This study highlights important ethical implications. Although autonomy matters, trust appears to matter the most. Therefore, researchers should be alert to the need to foster and maintain trust, particularly as failing to do so might have deleterious effects on future research.

Acceleration data for activity recognition typically are collected on battery-powered devices, leading to a trade-off between high-accuracy recognition and energy-efficient operation. We investigate this trade-off from a feature selection perspective, and propose an energy-efficient activity recognition framework with two key components: a detailed energy consumption model and a number of feature selection algorithms. We evaluate the model and the algorithms using Random Forest classifiers to quantify the recognition accuracy, and find that the multi-objective Particle Swarm Optimization algorithm achieves the best results for the task. The results show that by selecting appropriate groups of features, energy consumption for computation and data transmission is reduced by an order of magnitude compared with the raw-data approach, and that the framework presents a flexible selection of feature groups that allow the designer to choose an appropriate accuracy-energy trade-off for a specific target application.

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Healthcare professionals currently lack the means to gather unbiased and quantitative multi-modal data about the long-term behaviors of patients in their home environments. SPHERE is a multi-modal platform of non-medical sensors for behavior monitoring in residential environments that aims to overcome this major limitation of healthcare provision through using the inherently cost-efficient and scalable technologies of the Internet of Things (IoT). One of SPHERE’s key tasks is to help to bring the next-generation low-power wireless networking and sensing technologies from the lab to the field by applying them in real-world environments. In this article we describe the highlights of SPHERE’s system requirements, architecture, practical challenges, as well as of the design and deployment lessons learned. By leveraging novel IoT technologies such as the IEEE 802.15.4 TSCH network protocol, SPHERE has achieved successful initial deployments in twelve volunteer houses at the time of writing.

Cardiovascular diseases are the leading cause of death in the UK, motivating the use of long term wearable devices to monitor the heart in out-of-the-clinic settings. While a wide number of heart rate measuring wearable devices are now available, they are principally based upon the photoplethysmography (PPG) rather than the electrocardiogram (ECG) and are stand-alone devices rather than integrated with Internet-of-Things infrastructures which collect and combine information from a wide range of sensors. This paper presents a wrist worn ECG sensor which integrates with the SPHERE IoT platform— the UK’s demonstrator platform for health monitoring in the home environment, combining a range of on-person and ambient sensors. The ECG device integrates ultra low power consumption electronics with personalizable 3D printed casings which maintain gold standard Ag/AgCl electrodes to provide measurements of the raw ECG waveform, heart rate, and meanNN and SDNN heart rate variability parameters. The end device allows for more than a month of battery life for a weight of

This article shares experiences and lessons learned from designing and developing the bespoke IoT (Internet of Things) sensing platforms of SPHERE (a Sensor Platform for Healthcare in a Residential Environment). These IoT platforms, which include a wrist-worn wearable sensor, a room-level environmental sensor, and a dual-radio gateway, constitute integral parts of the SPHERE system that is currently getting deployed in up to 100 houses of volunteers for up to 12 months. This article is focused on sharing several years’ worth of insight and experiences from making IoT sensing platforms for largescale academic deployments, providing information that can accelerate the development and deployment of prototypes, and enable timely research in the space of IoT.

Modern body-centric communication systems require good link quality. Antenna performance is of primary importance when meeting this requirement. This paper contributes a method suited to the difficult task of quantifying antenna performance in a body-centric communications system. In a case study, a planar wrist wearable antenna, which provides radiation pattern switching across the 2.4GHz operating band through an innovative technique that does not require an additional switching mechanism, is benchmarked against a monopole and a patch antenna in a residential setting. The performance of the antenna, and subsequently the benefits of the pattern switching technique, are successfully quantified. The holistic method includes both antenna measurements and channel simulation with ray-tracing. Results are verified against real world measurements.

A compact, enclosed, ultrawide-band (UWB) antenna array is presented to acquire data for a quantitative microwave imaging method. Compared to existing systems, the proposed array allows a UWB antenna to be placed close to a target object while, at the same time, minimizing the volume of the imaging array. The antennas and metallic enclosure are designed to easily integrate with an iterative threedimensional (3-D) nonlinear inverse scattering technique. The volume of the internal imaging domain has been minimized for this particular architecture to reduce the computational time spent on reconstructing the dielectrics within the domain. Each cavity-backed element radiates toward the target and presents stable transmission characteristics across the 1-4-GHz band.

Wearable technologies are valuable tools that can encourage people to monitor their own well-being and facilitate timely health interventions. In this paper, we present SPW-2; a low-profile versatile wearable sensor that employs two ultra low power accelerometers and an optional gyroscope. Designed for minimum maintenance and a long-term operation outside the laboratory, SPW-2 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-2 can fully cover a room and - in most cases - the adjacent room, as well.

Objectives: Hip and knee replacements are common major elective surgical interventions with over 200,000 performed annually in the UK. Not all patients achieve optimal outcomes or experience problems or delays in recovery. The number of patients needing these operations are set to increase and routine clinical monitoring is time and resource consuming for patients and healthcare providers, therefore innovative evaluation of surgical outcomes are needed. The aim of this qualitative study was to capture the patient experience of living with a novel home monitoring sensing system during the period around joint replacement. \ud \ud Setting: One secondary care hospital in the South West, UK\ud \ud Participants: 13 patients, (8 female, 63-89y) undergoing total hip or knee replacement enrolled into the study \ud \ud Design: Qualitative study with thematic analysis. The system remained in situ for up to 12 weeks after their surgery and comprised a group of low powered sensors monitoring the environment (temperature, light and humidity) and activity of people within the home. Patients were interviewed at two timepoints: before and after surgery. Interviews explored views about living with the technology, its acceptability as well as attitudes towards health technology. \ud \ud Results: Three main themes emerged: installation of home sensing technology on the journey to surgery; the home space and defining unobtrusiveness and pivotal role of social support networks\ud \ud Conclusions: Patients who agreed to the technology found living with it acceptable. A home sensing system that monitors the environment and activity of the people in the home could provide an innovative way of assessing patients’ surgical outcomes. At a time, characterised by reduced mobility, functional limitations and increased pain, patients in this study relied on informal and formal supportive networks to help maintain the system through the busy trajectory of the perioperative period. \ud \ud \ud Strengths and limitations of this study\ud • In-depth one-to-one interviews provided insight into patients’ real experiences and views as they lived with the technology in their own homes\ud • Although the sample size was small, lacked ethnic diversity and only included people willing to have technology installed in their homes, there was good diversity in age and gender and some diversity in patients undergoing hip and knee replacement \ud • Use of thematic analysis enabled robust analysis of data, including focus on the acceptability of the technology in real health-related circumstances.

Activity and decision-making around nutrition are important aspects of naturalistic human behaviour. This paper describes a participant-centric free-text annotation process intended to facilitate activity recognition in the kitchen environment. The resulting annotations are characterised. Data from the study are reviewed to establish the extent to which this annotation dataset and the sensor data it accompanies, including environmental, current and water usage, support estimation of meal complexity. Finally, planned future work in this area is discussed. Three metrics of meal complexity are identified.

Background Public involvement adds value to numerous aspects of health research, yet few studies have attempted to evaluate its impact on research. Evidence of such impact is needed to develop recommendations for best practice and ensure adequate resourcing.Aim To evaluate public involvement within a large interdisciplinary Science, Technology, Engineering and Mathematics (STEM) research project that focused on digital health.Methods The evaluation was conducted with members of the project’s Public Advisory Groups (PAG) and with researchers who had participated in involvement activities. Two questionnaires were designed based on a public involvement value systems and clusters framework.Results Responses from members of the PAG (n=10) were mostly positive towards normative values, which include moral, ethical, and political aspects of involvement in research, and towards values concerning the conduct of public involvement and best practices. Researchers’ responses (n=16) indicated they felt that involvement was generally effective and increased the quality, relevance, and generalisability of their work. However, their responses about the validity of involvement in research were varied. They also highlighted several challenges including how well public involvement impacted on research, how decisions made in the research might differ from the views generated from public involvement, and barriers to researchers’ participation.Discussion and conclusion Our evaluation suggests that members of the public and the researchers value involvement. However, there is need to consider how to embed public involvement to an even greater extent in STEM contexts and a need to address any barriers for researchers’ own involvement.

A new formulation of the Auxiliary Difference Equation (ADE) Finite Difference Time Domain (FDTD) algorithm for the simulation of dispersive materials has been presented in the literature. Although flexible and efficient, this algorithm suffers from instability when modelling lossy high contrast dielectrics. In this paper we adapt this ADE-FDTD formulation and present alternative algorithms for modelling static conductivity and Debye dispersion. The stability of these algorithms is assessed by numerical simulation in a wide variety of dielectricmedia and their performance is compared to the existing algorithm by means of a simulation of the reflection of a plane wave from a dielectric boundary. Results and comparison with theory demonstrate the stability and accuracy of the new methods. The flexibility, computational efficiency and ability to model a wide range of materials make these new methods highly attractive compared to other dispersive FDTD algorithms, particularly for modelling materials with multiple dispersion models.

Locating the surface of an object, in this case the breast, is an important first step in many imaging situations; this surface information may be a necessary part of the reconstruction, it may be needed for the cancellation of the surface reflection, or (as herein) it may be needed as a preparatory step before imaging. This paper presents two complementary approaches developed for the purpose of surface localisation. The proposed approaches are evaluated using data from both phantom measurements and volunteer scans.

This repository offers smart-home wearable accelerometer and Radio Signal Strength Indicator (RSSI) data acquired : 1) with low-cost hardware; 2) with high-resolution location annotations; 3) from four UK homes. The data are intended to evaluate RSSI-based indoor localisation methods with activity measurements provided from a user-worn wearable device. A wrist worn accelerometer records activity signatures which are relayed to a number of receiving Access Points (AP) placed throughout the building. Upon reception of a packet, each AP measures the RSSI of the received radio signal and timestamps the accelerometer measurements. Location labels are recorded automatically using a small camera which registers fiducial floor tags as the participant carries out their normal routines in a natural way. Approximately 14 h of annotated wearable measurements are provided. A scripted fingerprint measurement is provided along with several unscripted natural living recordings, where the participant carried out a number of daily household activities which are annotated, where possible, throughout. Codes are provided to access the data and to replicate the ground-truthing procedure. Machine-accessible metadata file describing the reported data (ISA-Tab format)

IntroductionOver 160 000 people with severe hip or knee pain caused by osteoarthritis undergo total hip (THR) or knee replacement (TKR) surgery each year in the UK within the National Health Service (NHS), and this number is expected to increase. Innovative approaches to evaluating surgical outcomes will be needed to respond to the increasing burden of joint replacement surgery. The Sensor Platform for Healthcare in a Residential Environment, Interdisciplinary Research Collaboration (SPHERE-IRC) have developed a system of sensors that can monitor the health-related behaviours of people living at home. The system includes sensors for the home environment (measuring temperature, humidity, room occupancy, water and electricity usage), a wristband body-worn activity monitor and silhouette (body outline) sensors. The aim of HEmiSPHERE (Hip and knEe study of a Sensor Platform of HEalthcare in a Residential Environment) is to (1) determine the accuracy and feasibility of the sensory data as it compares with conventional assessment of health outcomes after surgery using patient self-reported questionnaires, and (2) to explore how the SPHERE system is useful for everyday clinical decision-making.Methods and analysisA feasibility study recruiting and installing the SPHERE system in the homes of up to 30 NHS adult patients as they undergo a THR or TKR. Through a mixed-methods design, the SPHERE system will monitor and record continuous measurements of daily behaviour. Main outcomes will assess the relationships between environmental, behavioural and movement data and the parameters of interest from the standard clinical assessments measuring patient outcomes over time. Patient interviews and focus groups with consultant orthopaedic surgeons will provide in-depth understanding of the acceptability, feasibility and accuracy of the data.Ethics and disseminationWe aim to disseminate the findings through regional talks and seminars, international conferences and peer-reviewed journals and social media.

We are interested in hot and cold water flow detection in domestic kitchen and bathroom taps for smart home environments. Water flow monitoring is particularly valuable for long-term behavioural monitoring systems for health-related applications, as it enables the collection of long-term data on the hydration levels of the house residents, and it is associated with several activities of daily life, such as cooking and cleaning. This paper presents a water flow sensing device that is based on sensing the vibrations on the pipe when water is flowing through them. The proposed solution is noninvasive and energy efficient, as it does not require cutting the water pipes or altering the plumbing system, and consumes less then 2 uA in continuous operation. The proposed water flow sensor has been integrated to SPHERE, a sensing platform of non-medical sensors for healthcare monitoring and behavioural analytics in a home environment, and deployed to more than 15 residential properties.

The efficient and effective deployment of Internet of Things (IoT) systems in real world scenarios remains a challenge, particularly in applications such as indoor localisation. Various methods have been proposed recently to calibrate localisation systems, ranging from precise but time consuming processes to those involving little explicit calibration based on a crowdsourced collection of data over time. However it is not clear how to estimate and compare the quality of a specific instance of a calibration. In this paper we present a simple yet effective method of calibrating a Smart Home in a Box (SHiB) together with a framework to combine calibrations while assessing their quality. Our empirical results demonstrate that our calibration method can be performed by untrained users in a short period of time yet is capable of up to 92% accuracy in room level localisation on free living experimental data.

TSCH (Time-Slotted Channel Hopping) is a synchronous MAC (Medium Access Control) protocol, introduced with the recent amendments to the IEEE 802.15.4 standard. Due to its channel hopping nature, TSCH is a promising enabling technology for dependable IoT (Internet of Things) infrastructures that are deployed in environments that are prone to interference. In TSCH, medium access is orchestrated by a schedule that is distributed to all the nodes in the network. In this paper, we propose Adaptive Static Scheduling to improve the energy efficiency of TSCH networks. Adaptive Static Scheduling builds on top of static schedules and allows each pair of communicating nodes to adaptively activate a subset of their allocated slots, effectively reducing the idle listening overhead of unused slots. Moreover, the nodes can dynamically activate more slots when they need to support bursts of high traffic, without the need of redistributing new schedules. Simulation results demonstrate that Adaptive Static Scheduling outperforms static scheduling in dynamic environments, operating nearly as efficiently as an oracle with knowledge of the optimal schedule.

Smart health home systems and assisted living architectures rely on severely energy-constrained sensing devices, such as wearable sensors, for the generation of data and their reliable wireless communication to a central location. However, the need for recharging the battery regularly constitutes a maintenance burden that hinders the long-term cost-effectiveness of these systems, especially for health-oriented applications that target people in need, such as the elderly or the chronically ill. These sensing systems generate raw data that is processed into knowledge by reasoning and machine learning algorithms. This paper investigates the benefits of embedded machine learning, i.e. executing this knowledge extraction on the wearable sensor, instead of communicating abundant raw data over the low power network. Focusing on a simple classification task and using an accelerometer-based wearable sensor, we demonstrate that embedded machine learning has the potential to reduce the radio and processor duty cycle by several orders of magnitude; and, thus, substantially extend the battery lifetime of resource-constrained wearable sensors.

Purpose: Improving outcomes after joint replacement is a key research priority. After joint replacement, up to 30% of patients report minimal improvement or their symptoms get worse and not all patients are satisfied with their outcome. Poor outcomes include continuing pain, functional limitation and increased healthcare utilisation. Patient Reported Outcome Measures (PROMs) such as the Oxford Hip or Oxford Knee Score and the EQ-5D, a measure of health status, are widely used to assess outcome after joint replacement in practice and research. These measures can assess a variety of health outcomes including pain, function and health related quality of life. Though widely used, many PROMs have methodological limitations and there is debate about how to interpret results and definitions of clinically meaningful change. Previously, research has explored the relationship between PROMs and objective measures, such as timed walks or sit-to-stand tests. Such objective measures are administered in controlled, laboratory style settings, and may not reflect levels of activity in daily life. With the rapid development of monitoring technology, there is opportunity to characterise the relationship between PROMs and behaviour in a natural setting and to develop methods of passive monitoring of outcome and recovery after surgery. We are working with a multidisciplinary team which has developed a system of low powered sensors that can monitor the health-related behaviours of people living at home. The system includes: sensors for the home environment (measuring temperature, humidity, room occupancy, water and electricity usage) a wrist-band body-worn activity monitor and silhouette (body outline) sensors. The SPHERE system of sensors is now being installed in 100 homes belonging to the general population. Within this cohort we are installing the system in the homes of 20 patients who are about to undergo a total hip or knee replacement (THR/TKR). The study aims to: (a) determine to what extent the sensory data obtained from the SPHERE system is comparable to data obtained from routine clinical measures and PROMs in the assessment of patients' activity, function and recovery processes; (b) investigate whether the sensory data can detect meaningful changes in recovery.Methods: To assess the accuracy and usefulness of the sensory data, in this 1-year observational study, patients will be provided with the sensor system to monitor and record daily continuous measurements. We will make and refine appropriate data learning outcomes with the quantitative data e.g., daily measurements in the weeks parallel to the distribution of PROM assessments pre/post-operative (4–9 days, 6 and 12 weeks) and weekly measurements during the other months. The study will assess the relationships between environmental, behavioural and movement data and the parameters of interest from the PROMs assessments over time. Interviews and focus groups with patients and health professional will provide qualitative data and achieve depth in understanding the accuracy of the data, its usefulness for health professionals in decision making, and if the technology is acceptable.Results: The SPHERE sensor system has been installed in the homes of two patients. We aim to recruit up to 20 patients, (aged 18 years or over) whom are due to undergo a TKR or THR in the UK.Conclusions: This study will provide a unique understanding of home and wearable sensor technology in an orthopaedic population and will compare sensor data with current PROMs.

The Internet of Things promises to enable numerous future applications spanning many domains, including health care, and is comprised of devices that are constrained in terms of computational and energy resources. A specific health care application is to ascertain patients' activity of daily living while at home using accelerometer data from non-invasive wearables. It is often necessary to store this data on the device to be retrieved later for analysis. However, the devices typically store far more data than can be transmitted with commonly used low power radios. To mitigate the problem, this paper proposes an energy efficient, lossless compression algorithm that uses an offline frequency distribution to create a symbol-code lookup table. Using an extensive set of data from a previous study, an analysis of the entropy of activities of daily living accelerometer data is presented. The compression algorithm is compared against this estimated entropy. Energy being critical for IoT devices, the trade-off between energy cost for compression versus energy saved during transmission is also analysed.

This paper presents a flexible 2.45-GHz wireless power harvesting wristband that generates a net dc output from a -24.3-dBm RF input. This is the lowest reported system sensitivity for systems comprising a rectenna and impedance-matching power management. A complete system has been implemented comprising: a fabric antenna, a rectifier on rigid substrate, a contactless electrical connection between rigid and flexible subsystems, and power electronics impedance matching. Various fabric and flexible materials are electrically characterized at 2.45 GHz using the two-line and the T-resonator methods. Selected materials are used to design an all-textile antenna, which demonstrates a radiation efficiency above 62% on a phantom irrespective of location, and a stable radiation pattern. The rectifier, designed on a rigid substrate, shows a best-in-class efficiency of 33.6% at -20 dBm. A reliable, efficient, and wideband contactless connection between the fabric antenna and the rectifier is created using broadside-coupled microstrip lines, with an insertion loss below 1 dB from 1.8 to over 10 GHz. A self-powered boost converter with a quiescent current of 150 nA matches the rectenna output with a matching efficiency above 95%. The maximum end-to-end efficiency is 28.7% at -7 dBm. The wristband harvester demonstrates net positive energy harvesting from -24.3 dBm, a 7.3-dB improvement on the state of the art.

This study examines the performance of a simple microwave beamforming method using the Huygens scattering principle (called here the Huygens principle method) for detecting breast lesions. The beamforming method is similar to non-iterative time reversal in that the wave received is propagated back into the material, although differs in its treatment of attenuation. The single pass algorithm does not require a solution to an inverse model, making it computationally efficient and so able to offer a throughput appropriate for clinical use. Its performance is compared with time-delay beamforming, which may be implemented with similar computational complexity, on a set of phantoms, including a lossy medium, mimicking breast tissue. The method was used to image a commercially fabricated anatomically shaped breast phantom with multiple hidden inclusions mimicking tumours. The procedure was able to identify and localise significant scatterers inside the volume, with only approximate a-priori knowledge of the dielectric properties of the target object, in spite of its underlying assumption of a single scatterer model.

There's a widely known need to revise current forms of healthcare provision. Of particular interest are sensing systems in the home, which have been central to several studies. This article presents an overview of this rapidly growing body of work, as well as the implications for machine learning, with an aim of uncovering the gap between the state of the art and the broad needs of healthcare services in ambient assisted living. Most approaches address specific healthcare concerns, which typically result in solutions that aren't able to support full-scale sensing and data analysis for a more generic healthcare service, but the approach in this article differs from seamlessly linking multimodel data-collecting infrastructure and data analytics together in an AAL platform. This article also outlines a multimodality sensor platform with heterogeneous network connectivity, which is under development in the sensor platform for healthcare in a residential environment (SPHERE) Interdisciplinary Research Collaboration (IRC).

A novel wideband microwave radar imaging method is presented to detect regions of significant dielectric contrast within the breast. Clutter reduction is paramount to any radar imaging algorithm, especially with clinical patient data where the tissue composition of the breast is inhomogeneous. Time-domain data-adaptive imaging methods have been previously applied in a narrowband manner for microwave radar breast imaging when the received signal spectral content was wideband. In this study, a wideband time-domain adaptive imaging approach is presented to perform data-adaptive focusing across the spectrum to reduce clutter. An equalization filter is adapted to compensate for the propagation distortion through tissue using a calculated estimate of the average dielectric properties of the breast. The effectiveness of the proposed wideband adaptive imaging approach is evaluated in conjunction with the delay-and-sum (DAS) method using numerical, experimental, and clinical data. Target scatterers are clearly detected while clutter levels are reduced significantly, between 4 and 6 dB, when compared to the DAS technique.

Networks deployed in real-world conditions have to cope with dynamic, unpredictable environmental temperature changes. These changes affect the clock rate on network nodes, and can cause faster clock de-synchronization compared to situations where devices are operating under stable temperature conditions. Wireless network protocols such as Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard are affected by this problem, since they require tight clock synchronization among all nodes for the network to remain operational. This paper proposes a method for autonomously compensating temperature-dependent clock rate changes. After a calibration stage, nodes continuously perform temperature measurements to compensate for clock drifts at run-time. The method is implemented on low-power IoT nodes and evaluated through experiments in a temperature chamber, indoor and outdoor environments, as well as with numerical simulations. The results show that applying the method reduces the maximum synchronization error more than 10 times. In this way, the method allows reduce the total energy spent for time synchronization, which is practically relevant concern for low data rate, low energy budget TSCH networks, especially those exposed to environments with changing temperature. This work was performed under the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1. It was also partly funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 761586 (5G-CORAL), the distributed environment Ecare@Home funded by the Swedish Knowledge Foundation, and by a grant from CPER Nord-PasdeCalais/ FEDER DATA.

Non-invasive, environmental monitoring is being successfully utilised to improve health care outcomes for patients while allowing them to more safely and comfortably live in their homes instead of health care facilities. This promises to reduce costs and ease the health care burden for many countries globally. However, these systems are still in early stages of research and only highly skilled researchers and engineers can successfully deploy them. The difficulty in deploying these systems prevents their mass use and potential cost savings motivating research interest in smart homes in a box (SHiB). In this paper we present the EurValve Activity Monitoring Kit, a minimalist activity monitoring system that can be deployed in a home by the patient and still obtain valuable lifestyle and activity level information for medical clinicians. We describe the design of the system and how it is being used in the H2020 EurValve Project. The initial results show that the system is easily deployed and yet still effective for non-invasive sensing for activity classification and localisation.

Reliably discerning human activity from sensor data is a nontrivial task in ubiquitous computing, which is central to enabling smart environments. Ground-truth acquisition techniques for such environments can be broadly divided into observational and self-reporting approaches. In this paper we explore one self-reporting approach, using speech-enabled logging to generate ground-truth data. We report the results of a user study in which participants (N=12) used both a smart-watch and a smart-phone app to record their activities of daily living using primarily voice, then answered questionnaires comprising the System Usability Scale (SUS) as well as open ended questions about their experiences. Our findings indicate that even though user satisfaction with the voice-enabled activity logging apps was relatively high, this approach presented significant challenges regarding compliance, effectiveness, and privacy. We discuss the implications of these findings with a view to offering new insights and recommendations for designing systems for ground-truth acquisition ‘in the wild’.

Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard uses channel hopping to combat interference and frequency-selective fading. Due to the pseudo-random nature of TSCH standard channel hopping process, the energy usage and end-to-end delay achieved in statically configured TSCH networks are suboptimal when compared to using a dynamically updated set of channels. We investigate and compare the advantages of several different adaptive channel selection metrics and methods under the presence of external, frequency-specific interference. In our experiments, PRR-based channel quality assessment with downstream-driven channel selection shows the best results. It is able to reliably distinguish between heavily-interfered and lightly-interfered channels, reduces the number of packet retransmissions up to 2.7 times, achieves up to 22 % lower average radio-on time, and shows close-to 100 % PDR even under heavy interference.

The upcoming Internet of Things (IoT) applications include real-time human activity monitoring with wearable sensors. Compared to the traditional environmental sensing with low-power wireless nodes, these new applications generate a constant stream of a much higher rate. Nevertheless, the wearable devices remain battery powered and therefore restricted to low-power wireless standards such as IEEE 802.15.4 or Bluetooth Low Energy (BLE). Our work tackles the problem of building a reliable autonomous schedule for forwarding this kind of dynamic data in IEEE 802.15.4 TSCH networks. Due to the a priori unpredictability of these data source locations, the quality of the wireless links, and the routing topology of the forwarding network, it is wasteful to reserve the number of slots required for the worst-case scenario, under conditions of high expected datarate, it is downright impossible. The solution we propose is a hybrid approach where dedicated TSCH cells and shared TSCH slots coexist in the same schedule. We show that under realistic assumptions of wireless link diversity, adding shared slots to a TSCH schedule increases the overall packet delivery rate and the fairness of the system.

Many countries are facing burdens on their health care systems due to ageing populations. A promising strategy to address the problem is to allow selected people to remain in their homes and be monitored using recent advances in wearable devices, saving in-hospital resources. With respect to heart monitoring, wearable devices to date have principally used optical techniques by shining light through the skin. However, these techniques are severely hampered by motion artifacts and are limited to heart rate detection. Further, these optical devices consume a large amount of power in order to receive a sufficient signal, resulting in the need for frequent battery recharging. To address these shortcomings we present a new wrist ECG wearable that is similar to the clinical approach for heart monitoring. Our device weighs less than 30 g, and is ultra low power, extending the battery lifetime to over a month to make the device more appropriate for in-home health care applications. The device uses two electrodes activated by the user to measure the voltage across the wrists. The electrodes are made from a flexible ink and can be painted on to the device casing, making it adaptable for different shapes and users. In this paper we show how the ECG sensor can be integrated into an existing IoT wearable and compare the device's accuracy against other common commercial devices.

International audience; Time-Slotted Channel Hopping from the IEEE 802.15.4-2015 standard requires that network nodes are tightly time-synchronized. Existing implementations of TSCH on embedded hardware are characterized by tens-of-microseconds large synchronization errors; higher synchronization accuracy would enable reduction of idle listening time on receivers, in this way decreasing the energy required to run TSCH. For some applications, it would also allow to replace dedicated time synchronization mechanisms with TSCH. We show that time synchronization errors in the existing TSCH implementations on embedded hardware are caused primarily by imprecise clock drift estimations, rather than by real unpredictable drift variance. By estimating clock drift more precisely and by applying adaptive time compensation on each node in the network, we achieve microsecond accuracy time synchronization on point-to-point links and a

There has been significant recent interest in sensing systems and ‘smart environments’, with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or activities that the resident(s) of the home are engaged in, which may in turn be used for determining normal or abnormal patterns of behaviour (e.g. in a health-care setting). Classification algorithms, such as Conditional Random Field (CRFs), typically consider sensor activations as features but these are often treated as if they were independent, which in general they are not. Our hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone. The exhaustive approach – to take all possible combinations of sensors and learn classifier weights for each combination – is clearly computationally prohibitive. We show that through the application of signal processing and information-theoretic techniques we can learn about the sensor topology in the home (i.e. learn an adjacency matrix) which enables us to determine the combinations of sensors that will be useful for classification ahead of time. As a result we can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources. We demonstrate our results on several datasets, showing that our method is robust in terms of variations in the layout and the number of residents in the house. Furthermore, we have incorporated the adjacency matrix into the CRF learning framework and have shown that it can improve performance over multiple baselines.