Real-Time Fatigue Evaluation Using Ecological Momentary Assessment and Smartwatch Data: An Observational Field Study on Construction Workers

Managing the fatigue of construction workers is crucial to productivity, quality of work, and accident risk reduction. However, the current practice for assessing fatigue is limited when applied to construction sites. This study proposed a framework to objectively and subjectively evaluate construction workers' fatigue in real-time using an ecological momentary assessment (EMA) application and smartwatch data. Fatigue data were collected from 100 construction workers over three days. The results revealed that objective fatigue factors (heart rate and physical activity) were easily affected by the characteristics of the construction field (i.e., starting early, changing and demanding schedules, and overworking hours), whereas subjective fatigue steadily increased with working time. Most workers were aware of physical fatigue at the end of work for the day, when the EMA scores were the highest in a day. However, objective and subjective fatigue did not completely concur throughout the work period. Our findings are expected to improve the management of construction site health and safety with priority given to construction workers. The proposed framework, which utilizes EMA and wearable devices as a fatigue assessment method, reflects the comprehensive aspect of work-related fatigue. © 2023 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.ope

Behavioral Privacy Risks and Mitigation Approaches in Sharing of Wearable Inertial Sensor Data

Wrist-worn inertial sensors in activity trackers and smartwatches are increasingly being used for daily tracking of activity and sleep. Wearable devices, with their onboard sensors, provide appealing mobile health (mHealth) platform that can be leveraged for continuous and unobtrusive monitoring of an individual in their daily life. As a result, an adaptation of wrist-worn devices in many applications (such as health, sport, and recreation) increases. Additionally, an increasing number of sensory datasets consisting of motion sensor data from wrist-worn devices are becoming publicly available for research. However, releasing or sharing these wearable sensor data creates serious privacy concerns of the user. First, in many application domains (such as mHealth, insurance, and health provider), user identity is an integral part of the shared data. In such settings, instead of identity privacy preservation, the focus is more on the behavioral privacy problem that is the disclosure of sensitive behaviors from the shared sensor data. Second, different datasets usually focus on only a select subset of these behaviors. But, in the event that users can be re-identified from accelerometry data, different databases of motion data (contributed by the same user) can be linked, resulting in the revelation of sensitive behaviors or health diagnoses of a user that was neither originally declared by a data collector nor consented by the user. The contributions of this dissertation are multifold. First, to show the behavioral privacy risk in sharing the raw sensor, this dissertation presents a detailed case study of detecting cigarette smoking in the field. It proposes a new machine learning model, called puffMarker, that achieves a false positive rate of 1/6 (or 0.17) per day, with a recall rate of 87.5%, when tested in a field study with 61 newly abstinent daily smokers. Second, it proposes a model-based data substitution mechanism, namely mSieve, to protect behavioral privacy. It evaluates the efficacy of the scheme using 660 hours of raw sensor data collected and demonstrates that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors. Finally, it analyzes the risks of user re-identification from wrist-worn sensor data, even after applying mSieve for protecting behavioral privacy. It presents a deep learning architecture that can identify unique micro-movement pattern in each wearer\u27s wrists. A new consistency-distinction loss function is proposed to train the deep learning model for open set learning so as to maximize re-identification consistency for known users and amplify distinction with any unknown user. In 10 weeks of daily sensor wearing by 353 participants, we show that a known user can be re-identified with a 99.7% true matching rate while keeping the false acceptance rate to 0.1% for an unknown user. Finally, for mitigation, we show that injecting even a low level of Laplace noise in the data stream can limit the re-identification risk. This dissertation creates new research opportunities on understanding and mitigating risks and ethical challenges associated with behavioral privacy

Detecting Agitation Onset in Individuals With Dementia Using Smart Phone Sensors

Individuals living with dementia (ILWD) often experience problematic agitated behaviors, this occurs in up to 80% of ILWD. These behaviors lead to stress for caregivers and increased frequency of institutionalization. There are many proven methods to intervene during agitated behavior outburst and the earlier these methods are used the better the results. Technology has been used successfully to monitor many aspects of health monitoring for older adults. Technology is now being investigated to evaluate the effectiveness of predicting the onset of problem behaviors, especially escalating agitation in ILWD. Off the shelf technology, smart watches and android phones, are being tested to measure limb movements, vocalizations, heart rate and location in facility, to evaluate their ability to provide data that is helpful in predicting agitated behaviors about to occur. This project is a collaboration between nursing and computer science in a major university setting. Currently, work has been completed on volunteers acting as patients to evaluate the ability of this technology to measure the desired parameters. Positive results have been obtained; the goal is to trial this technology on ILWD that have documented history of agitation in an assisted living environment

Smart technologies and beyond: exploring how a smart band can assist in monitoring children’s independent mobility & well-being

The problem which is being investigated through this thesis is not having a device(s) or method(s) which are appropriate for monitoring a child’s vital and tracking a child’s location. This aspect is being explored by other researchers which are yet to find a viable solution. This work focuses on providing a solution that would consider using the Internet of Things for measuring and improving children’s health. Additionally, the focus of this research is on the use of technology for health and the needs of parents who are concerned about their child’s physical health and well-being. This work also provides an insight into how technology is used during the pandemic. This thesis will be based on a mixture of quantitative and qualitative research, which will have been used to review the following areas covering key aspects and focuses of this study which are (i) Children’s Independent Mobility (ii) Physical activity for children (iii) Emotions of a child (iv) Smart Technologies and (v) Children’s smart wearables. This will allow a review of the problem in detail and how technology can help the health sector, especially for children. The deliverable of this study is to recommend a suitable smart band device that enables location tracking of the child, activity tracking as well as monitoring the health and wellbeing of the child. The research also includes an element of practical research in the form of (i) Surveys, the use of smart technology and a perspective on the solution from parents. (ii) Focus group, in the form of a survey allowing opinions and collection of information on the child and what the parents think of smart technology and how it could potentially help with their fears. (iii) Observation, which allows the collection of data from children who were given six activities to conduct while wearing the Fitbit Charge HR. The information gained from these elements will help provide guidelines for a proposed solution. In this thesis, there are three frameworks which are about (i) Research process for this study (ii) Key Performance Indicators (KPIs) which are findings from the literature review and (iii) Proposed framework for the solution, all three combined frameworks can help health professionals and many parents who want an efficient and reliable device, also deployment of technologies used in the health industry for children in support of independent mobility. Current frameworks have some considerations within the technology and medical field but were not up to date with the latest elements such as parents fears within today’s world and the advanced features of technology

Classification of Frailty among Community Dwelling Older Adults Using Parameters of Physical Activity Obtained Independently and Unsupervised

The global population is ageing at an unprecedented rate, with the percentage of those aged over 65 years expected to double and those aged over 80 years expected to treble by the year 2050. With ageing comes biological and physiological changes that affect functional capacity. Frailty is a potentially avoidable, reversible biopsychosocial condition associated with biological but not chronological age, affecting a quarter of all community-dwelling older adults. Frailty results in disability, increased dependency and institutionalisation. Screening for frailty could help reduce its prevalence and mitigate the adverse outcomes however, traditional screening tools are time-consuming to perform, require clinician input and by their subjective nature are flawed. The use of wearable sensors has been proposed as a means of screening for frailty and parameters of mobility and physical activity have been identified as being associated with frailty. The goal of this thesis was to examine if community-dwelling older adults could capture parameters of mobility and physical activity independently in their own home and if these parameters could discriminate between frail and non-frail status. This work provides evidence that a single parameter of mobility and physical activity obtained from a single body-worn sensor correlates with frailty. It also provides evidence that community-dwelling older adults can independently capture parameters of mobility and physical activity, unsupervised in their own home using a consumer-grade wearable device, and that these data can predict pre-frailty and frailty with acceptable accuracy. Thresholds for parameters of physical activity predictive of frailty have been identified. The results of this thesis will guide future work to focus community-dwelling older adults on the importance of frailty screening and guide the development of a user-friendly device or sensor system suitable for use by older adults for continuous data collection relevant to frailty

Smartphone Loss Prevention System Using BLE and GPS Technology

Being an all-in-one gadget, smartphones play a vital role in our everyday lives. However, millions of people suffer every year by losing their phones. A lost phone creates a huge security threat and data loss possibility to the users. Some preventive measures are available to protect from unauthorized access. Moreover, there are some post-loss solutions to track down, retrieve data from a lost locked phone, and protect the privacy and security of lost phone data, but those have some drawbacks as well. Considering the situation, our proposed system offers a preventive solution which will protect the smartphones from getting lost. Our system involves a smartwatch which will be connected to smartphone via Bluetooth Low Energy (BLE) and keep track of the distance between the smartphone and the smartwatch worn by user in real-time. The system will be able to identify if the distance goes beyond 20 feet or a customizable distance given by the user and immediately raise an alert in the smartwatch, creating vibration and sound in public places. The system allows users to mark their safe location (e.g., house, office) and radius where their smartphone will be safe, and they don’t need alerts. We have developed 3 different models to implement this system with different approaches using Ranging, Haversine formula and Geofencing. For our work, we aim to perceive how accurate our models are in terms of calculating distance and safe location tracking as well as alert response time and models impact on battery life of both smartphone and smartwatch. We have developed an Android application and a smartwatch application that run on both smartwatch and smartphone for each model and compared their performances based on our evaluation parameters. We conducted experiments under various real-world conditions and the system incorporated with Model 1 can generate alert with 96% accuracy when user is away from the smartphone beyond the threshold distance in an unsafe location. This affordable solution will ensure prevention from smartphones getting lost in public places in an effective way securing confidentiality and data protection to users

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Studies on Neuromuscular Blocking Agents and Their Antagonists During Anaesthesia

Neuromuscular blocking agents (NMBAs) are widely used in clinical anaesthesia and emergency medicine. Main objectives are to facilitate endotracheal intubation and to allow surgery by reducing muscle tone and eliminating sudden movements, which may otherwise lead to trauma and complications. The most commonly used NMBAs are non-depolarizing agents with a medium duration of action, such as rocuronium and cisatracurium. They bind to the acetylcholine receptors in the neuromuscular junction, thus inhibiting the depolarization of the postsynaptic (muscular) membrane, which is a prerequisite for muscle contraction to take place. Previously, it has been assumed that nitrous oxide (N2O), which is commonly used in combination with volatile or intravenous anaesthetics during general anaesthesia, has no effect on NMBAs. Several studies have since claimed that N2O in fact does increase the effect of NMBAs when using bolus administration of the relaxant. The effect of N2O on the infusion requirements of two NMBAs (rocuronium and cisatracurium) with completely different molecular structure and pharmacological properties was assessed. A closed-loop feedback controlled infusion of NMBA with duration of at least 90 minutes at a 90% level of neuromuscular block was used. All patients received total intravenous anaesthesia (TIVA) with propofol and remifentanil. In both studies the study group (n=35) received N2O/Oxygen and the control group (n=35) Air/Oxygen. There were no significant differences in the mean steady state infusion requirements of NMBA (rocuronium in Study I; cisatracurium in Study II) between the groups in either study. In Study III the duration of the unsafe period of recovery after reversal of rocuronium-induced neuromuscular block by using neostigmine or sugammadex as a reversal agent was analyzed. The unsafe period of recovery was defined as the time elapsed from the moment of no clinical (visual) fade in the train-of-four (TOF) sequence until an objectively measured TOF-ratio of 0.90 was achieved. The duration of these periods were 10.3 ± 5.5 and 0.3 ± 0.3 min after neostigmine and sugammadex, respectively (P < 0.001). Study IV investigated the possible effect of reversal of a rocuronium NMB by sugammadex on depth of anaesthesia as indicated by the bispectral index and entropy levels in thirty patients. Sugammadex did not affect the level of anaesthesia as determined by EEG-derived indices of anaesthetic depth such as the bispectral index and entropy.Siirretty Doriast