Smart Sensing Technologies for Personalised Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes

Advancing the objective measurement of physical activity and sedentary behaviour context

Objective data from national surveillance programmes show that, on average, individuals accumulate high amounts of sedentary time per day and only a small minority of adults achieve physical activity guidelines. One potential explanation for the failure of interventions to increase population levels of physical activity or decrease sedentary time is that research to date has been unable to identify the specific behavioural levers in specific contexts needed to change behaviour. Novel technology is emerging with the potential to elucidate these specific behavioural contexts and thus identify these specific behavioural levers. Therefore the aims of this four study thesis were to identify novel technologies capable of measuring the behavioural context, to evaluate and validate the most promising technology and to then pilot this technology to assess the behavioural context of older adults, shown by surveillance programmes to be the least physically active and most sedentary age group. Study one Purpose: To identify, via a systematic review, technologies which have been used or could be used to measure the location of physical activity or sedentary behaviour. Methods: Four electronic databases were searched using key terms built around behaviour, technology and location. To be eligible for inclusion papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed from the inception of the database up to 04/02/2015. Searches were also performed using three internet search engines. Specialised software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms. Results: 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras and Radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems and 21 wearable cameras. Conclusion: The addition of location information to existing measures of physical activity and sedentary behaviour will provide important behavioural information. Study Two Purpose: This study investigated the Actigraph proximity feature across three experiments. The aim of Experiment One was to assess the basic characteristics of the Actigraph RSSI signal across a range of straight line distances. Experiment Two aimed to assess the level of receiver device signal detection in a single room under unobstructed conditions, when various obstructions are introduced and the impacts these obstructions have on the intra and inter unit variability of the RSSI signal. Finally, Experiment Three aimed to assess signal contamination across multiple rooms (i.e. one beacon being detected in multiple rooms). Methods: Across all experiments, the receiver(s) collected data at 10 second epochs, the highest resolution possible. In Experiment One two devices, one receiver and one beacon, were placed opposite each other at 10cm increments for one minute at each distance. The RSSI-distance relationship was then visually assessed for linearity. In Experiment Two, a test room was demarcated into 0.5 x 0.5 m grids with receivers simultaneously placed in each demarcated grid. This process was then repeated under wood, metal and human obstruction conditions. Descriptive tallies were used to assess the signal detection achieved for each receiver from each beacon in each grid. Mean RSSI signal was calculated for each condition alongside intra and inter-unit standard deviation, coefficient of variation and standard error of the measurement. In Experiment Three, a test apartment was used with three beacons placed across two rooms. The researcher then completed simulated conditions for 10 minutes each across the two rooms. The percentage of epochs where a signal was detected from each of the three beacons across each test condition was then calculated. Results: In Experiment One, the relationship between RSSI and distance was found to be non-linear. In Experiment Two, high signal detection was achieved in all conditions; however, there was a large degree of intra and inter-unit variability in RSSI. In Experiment Three, there was a large degree of multi-room signal contamination. Conclusion: The Actigraph proximity feature can provide a binary indicator of room level location. Study Three Purpose: To use novel technology in three small feasibility trials to ascertain where the greatest utility can be demonstrated. Methods: Feasibility Trial One assessed the concurrent validity of electrical energy monitoring and wearable cameras as measures of television viewing. Feasibility Trial Two utilised indoor location monitoring to assess where older adult care home residents accumulate their sedentary time. Lastly, Feasibility Trial Three investigated the use of proximity sensors to quantify exposure to a height adjustable desk Results: Feasibility Trial One found that on average the television is switched on for 202 minutes per day but is visible in just 90 minutes of wearable camera images with a further 52 minutes where the participant is in their living room but the television is not visible in the image. Feasibility Trial Two found that residents were highly sedentary (sitting for an average of 720 minutes per day) and spent the majority of their time in their own rooms with more time spent in communal areas in the morning than in the afternoon. Feasibility Trial Three found a discrepancy between self-reported work hours and objectively measured office dwell time. Conclusion: The feasibility trials outlined in this study show the utility of objectively measuring context to provide more detailed and refined data. Study Four Purpose: To objectively measure the context of sedentary behaviour in the most sedentary age group, older adults. Methods: 26 residents and 13 staff were recruited from two care homes. Each participant wore an Actigraph GT9X on their non-dominant wrist and a LumoBack posture sensor on their lower back for one week. The Actigraph recorded proximity every 10 seconds and acceleration at 100 Hz. LumoBack data were provided as summaries per 5 minutes. Beacon Actigraphs were placed around each care home in the resident s rooms, communal areas and corridors. Proximity and posture data were combined in 5 minute epochs with descriptive analysis of average time spent sitting in each area produced. Acceleration data were summarised into 10 second epochs and combined with proximity data to show the average count per epoch in each area of the care home. Mann-Whitney tests were performed to test for differences between care homes. Results: No significant differences were found between Care Home One and Care Home Two in the amount of time spent sitting in communal areas of the care home (301 minutes per day and 39 minutes per day respectively, U=23, p=0.057) or in the amount of time residents spent sitting in their own room (215 minutes per day and 337 minutes per day in Care Home One and Two respectively, U=32, p=0.238). In both care homes, accelerometer measured average movement increases with the number of residents in the communal area. Conclusion: The Actigraph proximity system was able to quantify the context of sedentary behaviour in older adults. This enabled the identification of levers for behaviour change which can be used to reduce sedentary time in this group. Overall conclusion: There are a large number of technologies available with the potential to measure the context of physical activity or sedentary time. The Actigraph proximity feature is one such technology. This technology is able to provide a binary measure of proximity via the detection or non-detection of Bluetooth signal: however, the variability of the signal prohibits distance estimation. The Actigraph proximity feature, in combination with a posture sensor, is able to elucidate the context of physical activity and sedentary time

Multi-sensor physical activity measurement in early childhood

The purpose of this dissertation was to develop, validate, and implement multi-sensor approaches for measuring physical activity and social/contextual covariates in 2-5 year-old children via wearable-, wireless communication-, and infrared-depth camera-based technologies. In Chapter 2, a three-phased study design was used to validate a method for estimating metered distances between wearable devices using accelerometer-derived Bluetooth signals. Results showed that distances, up to 20 meters, can be predicted between a single Bluetooth beacon and receiver using a Random Forest algorithm. When multiple Bluetooth beacons and receivers were used within the same environment, a moving average filter was required to recover observations lost due to noise. Overall, simulation and validation data suggest that accelerometer-derived Bluetooth signals can be used in studies of physical activity co-participation to 1) estimate metered distances between devices using a single beacon-receiver paradigm, as well as to 2) estimate the proportion of time that devices are proximal when using multiple beacons and receivers. Chapter 3 characterized the relationship between objectively measured physical activity and dyadic spatial proximities in 2 year-olds and their parents. Data revealed that the overall proportions of time that children and their parents spent in total physical activity were positively associated, and time series data revealed that this relationship remained consistent when analyzed hour-to-hour. Time spent engaged in sedentary behavior was also positively associated between children and parents; however, there was no association between child and parent moderate-vigorous physical activity volumes. Dyadic proximity results showed that girls spent more time in joint physical activity with their mothers than boys. Furthermore, children who engaged in >60 minutes of daily moderate-vigorous physical activity spent an additional 30 minutes in joint total physical activity with their mothers each day, on average, when compared to children who engaged in 60 minutes of daily moderate-vigorous physical activity participated in joint physical activity with their mothers across wider relative distances, on average, than did boys who engaged in physical activity at closer relative distances to their mothers. In Chapter 4, an original computer vision algorithm was applied to infrared-depth camera data for the purpose of converting three-dimensional videos into triaxial physical activity signals in young children. Physical activity data were collected in 2-5 year-old children during 20-minute semi-structured, indoor child-parent dyadic play sessions. Play session video data were converted into triaxial physical activity signals using a multi-phased computer vision algorithm for each child. Computer vision-derived triaxial physical activity cut points for 2-5 year-olds were calibrated against a direct observation reference system using a machine learning algorithm. Results revealed that triaxial activity signals, as measured by a dual-sensor camera, can be used to estimate both physical activity intensities and volumes in young children without the use of wearable technology. Collectively, these studies show that multi-sensor approaches to physical activity measurement are a valid means by which to measure physical activity and social/contextual covariates in young children using either wearable sensors or computer vision

An IoT-Aware Approach for Elderly-Friendly Cities

The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

The Design and Use of a Smartphone Data Collection Tool and Accompanying Configuration Language

Understanding human behaviour is key to understanding the spread of epidemics, habit dispersion, and the efficacy of health interventions. Investigation into the patterns of and drivers for human behaviour has often been facilitated by paper tools such as surveys, journals, and diaries. These tools have drawbacks in that they can be forgotten, go unfilled, and depend on often unreliable human memories. Researcher-driven data collection mechanisms, such as interviews and direct observation, alleviate some of these problems while introducing others, such as bias and observer effects. In response to this, technological means such as special-purpose data collection hardware, wireless sensor networks, and apps for smart devices have been built to collect behavioural data. These technologies further reduce the problems experienced by more traditional behavioural research tools, but often experience problems of reliability, generality, extensibility, and ease of configuration. This document details the construction of a smartphone-based app designed to collect data on human behaviour such that the difficulties of traditional tools are alleviated while still addressing the problems faced by modern supplemental technology. I describe the app's main data collection engine and its construction, architecture, reliability, generality, and extensibility, as well as the programming language developed to configure it and its feature set. To demonstrate the utility of the tool and its configuration language, I describe how they have been used to collect data in the field. Specifically, eleven case studies are presented in which the tool's architecture, flexibility, generality, extensibility, modularity, and ease of configuration have been exploited to facilitate a variety of behavioural monitoring endeavours. I further explain how the engine performs data collection, the major abstractions it employs, how its design and the development techniques used ensure ongoing reliability, and how the engine and its configuration language could be extended in the future to facilitate a greater range of experiments that require behavioural data to be collected. Finally, features and modules of the engine's encompassing system, iEpi, are presented that have not otherwise been documented to give the reader an understanding of where the work fits into the larger data collection and processing endeavour that spawned it

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

The relationship between the home environment and children’s physical activity and sedentary behaviour at home

Increasing children’s physical activity (PA) and reducing their sedentary behaviour are considered important preventative measures for obesity and several other health risk factors in children. Given children spend significant time at home, an improved understanding of these behaviours in the home environment would provide invaluable insight for interventions. Therefore, the overarching aim of this thesis was to provide new insight into how the home environment is related to children’s home-based PA and sedentary behaviour. Study 1 investigated the relationship between sufficient moderate-to-vigorous physical activity (MVPA) (≥60 min·day–1) and excessive screen-time (≥2 h·day–1) with lifestyle factors in children, and found they were associated with healthy and unhealthy factors, respectively. This study highlighted the importance of meeting PA and screen-time recommendations in relation to important health-related lifestyle factors, which is of concern, as few children were shown to meet such recommendations. Identifying the correlates of children’s behaviours is an important stage in intervention development, therefore studies 2-5 focussed on improving understanding of children’s PA and sedentary behaviour at home. Study 2 demonstrated the validity and reliability of HomeSPACE-II, a novel instrument for measuring physical factors that influence children’s home-based PA and sedentary behaviour. Using HomeSPACE-II, study 3 showed that the physical home environment is related to children’s home-based PA and sedentary behaviour. Given the established influence of social and individual factors on children’s behaviour and their confounding effects in study 3, study 4 investigated the influence of social and individual factors on: (i) children’s home-based PA and sedentary behaviour, and; (ii) the home physical environment. Study 4 revealed that parental and child activity preferences and priorities, as well as parental rules were associated with children’s home-based PA and sedentary behaviour and the physical home environment. Study 5 found clusters of social and physical factors at home, which were associated with children’s home-based PA and sedentary behaviour as well as background characteristics in the expected directions

Sedentary behaviour in office workers: correlates and interventions

Background: The concept of sedentary behaviour has emerged since the turn of the millennium and research into this area is rapidly developing. Sedentary behaviours are activities that require very little energy expenditure whilst in a sitting or reclining posture thus are distinct from physical inactivity. Previous observational studies have demonstrated that high amounts of sedentary behaviour are associated with an increased risk of obesity, type 2 diabetes, metabolic syndrome, cardiovascular disease, cancer, depression and all-cause, cardiovascular and cancer mortality. Experimental studies suggest that prolonged sedentary time causes metabolic dysregulation and could be the explanation for the associated negative health effects. Breaks in prolonged sedentary time where standing or stepping occurs have shown beneficial effects on metabolic risk markers but the threshold for these effects is ambivalent and may depend on the population. The increasing prevalence of sedentary behaviours due to advances in technology are concerning but there is a lack of large-scale studies from the UK identifying the extent of sedentary behaviour prevalence and where the majority of sedentary time is accumulated in working-aged adults. A number of correlates are associated with sedentary behaviour including individual, social and environmental factors but the extent to which multiple other health behaviours correlate with specific sedentary behaviours is unknown. Interventions to reduce sedentary time have focused on the workplace where office workers spend large amounts of time sedentary. Multicomponent workplace interventions have reported reductions in sedentary time but there is limited research in the UK investigating the long-term effects of these interventions on working and non-working hours sedentary time. Additionally, the use of persuasive technology in the form of a wearable device to reduce sedentary time has rarely been explored as an intervention strategy.Aims: Study One aimed to assess the prevalence of domain-specific sedentary behaviour in a large sample of office workers from the UK and links with multiple other health behaviours. Study Two aimed to investigate the effectiveness of a pilot multicomponent workplace intervention to reduce sedentary time over the short (3 months) and long-term (12 months). Study Three aimed to explore the feasibility of a self-monitoring and prompting device to reduce sedentary time in a sample of office workers who have sit-stand desks.Methods: Study One performed a secondary data analysis on a large sample of office workers (n=7,170) who self-reported their domain-specific sitting time, physical activity level, smoking status, alcohol consumption, and fruit and vegetable intake in a 2012 and/or 2014 survey. Multiple logistic regression models explored the association between sedentary behaviours and multiple other health behaviours. A separate analysis was performed to investigate how these associations tracked over time (n=806). Study Two implemented a multicomponent workplace intervention in a sample of office workers (baseline n=30) and measured the effects 3 and 12-months post-baseline compared to a control group (baseline n=30). activPAL sedentary time was the primary outcome with accelerometer-determined physical activity and markers of health measured as secondary outcomes. Study Three provided a sample of office workers who had sit-stand desks (n=19 baseline, n=17 follow-up) with a wearable device to self-monitor their sedentary time through an application and prompt reductions in prolonged sedentary time through haptic feedback (LUMO). Feasibility and acceptability of the 4-week intervention were measured through wear time, engagement with application, questionnaire and interview feedback. The effect on sedentary time was measured with the LUMO and activPAL in addition to health and work-related measures.Results: Study One found that 643±160 minutes on a workday and 491±210 minutes on a non-workday were spent sitting. The majority of workday sitting took place at work (383±95 minutes/day) and whilst TV viewing on a non-workday (173±101 minutes/day). ≥7 hours sitting at work and ≥2 hours TV viewing on a workday both more than doubled the odds of partaking in ≥3 unhealthy behaviours [Odds ratio, OR=2.03, 95% CI, (1.59-2.61); OR=2.19 (1.71-2.80)] and ≥3 hours of TV viewing on a non-workday nearly tripled the odds [OR= 2.96 (2.32-3.77)]. No associations between domain-specific sitting time at baseline and change in unhealthy behaviour score were found over two years with the majority of participants maintaining baseline levels of all behaviours. Study Two found a trend towards reduced sedentary time at work by -7.9±25.1% and -18.4±12.4% per day at 3- (n=25 intervention, n=18 control group) and 12-months (n=11 intervention, n=7 control group) post-baseline in addition to overall workday by -4.6±13.8% and -8.0±8.3%. The intervention group showed an increase in sedentary time outside of work on a workday (4.2±9.5%) and overall on a non-workday (3.5±10.8%) after 12 months compared to baseline. However, the results found at the 3-month follow-up were not statistically significant and no significant differences in physical activity or health measures between groups were observed. Furthermore, due to the reduced sample size at the 12-month follow-up, no statistical testing was performed. Study Three found that the LUMO was a feasible intervention device in the short-term demonstrating high wear time (mean=60.6% of measurement days) and application engagement (mean=26.2±33.2 sessions, 30.3±26.5 minutes per week) with sedentary time being the most engaged with aspect of the application. The acceptability of the LUMO depended on the task undertaken, experience of problems with the device and preference towards the application or the prompt but overall, it increased awareness of behaviour. A trend towards reductions in sedentary time (-4%) and prolonged bouts of sedentary time >60 mins (-3%) on a workday were observed. Improvements were found in fat percentage and mass, blood pressure, job performance, work engagement, need for recovery and job satisfaction. Non-workday sedentary time >60 min bouts increased (4.8%) and increases in non-working hours sedentary time were apparent in weeks 3 and 4.Conclusions: Office workers are highly sedentary at work and whilst TV viewing which is associated with partaking in other multiple unhealthy behaviours. Multicomponent workplace interventions result in a trend towards reductions in occupational sedentary behaviour over the short and long-term. However, compensation during non-working hours could attenuate overall sedentary behaviour reductions resulting from workplace interventions. Wearable technology as an intervention strategy to reduce sedentary time shows promise and further research is needed in fully-powered studies. Future interventions should target multiple unhealthy behaviours in addition to sedentary time during work and non-working hours.</div

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject