Personal Healthcare Agents for Monitoring and Predicting Stress and Hypertension from Biosignals

We live in exciting times. The fast paced growth in mobile computers has put powerful computational devices in the palm of our hands. Blazing fast connectivity has made human-human, human-machine, and machine-machine communication effortless. Wearable devices and the internet of things have made monitoring every aspect of our lives easier. This has given rise to the domain of quantified self where we can continuous record and quantify the various signals generated in everyday life. Sensors on smartphones can continuously record our location and motion profile. Sensors on wearable devices can track changes in our bodies’ physiological responses. This monitoring also has the capability to revolutionise the health care domain by creating more informed and involved patients. This has the potential to shift care-management from a physician-centric approach to a patient-centric approach allowing individuals to create more empowered patients and individuals who are in better control of their health. However, the data deluge from all these sources can sometimes be overwhelming. There is a need for intelligent technology that can help us navigate the data and take informed decisions. The goal of this work is to develop a mobile, personal intelligent agent platform that can become a digital companion to live with the user. It can monitor the covert and overt signal streams of the user, identify activity and stress levels to help the users’ make healthy choices regarding their lives. This thesis particularly targets patients suffering from or at-risk of essential hypertension since its a difficult condition to detect and manage. This thesis delivers the following contributions: 1) An intelligent personal agent platform for on-the-go continuous monitoring of covert and overt signals. 2) A machine learning algorithm for accurate recognition of activities using smartphone signals recorded from in-the-wild scenarios. 3) A machine learning pipeline to combine various physiological signal streams, motion profiles, and user annotations for on-the-go stress recognition. 4) We design and train a complete signal processing and classification system for hypertension prediction. 5) Through a small pilot study we demonstrate that this system can distinguish between hypertensive and normotensive subjects with high accuracy

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders

An adaptive real-time intelligent system to enhance self-care of chronic disease (ARISES)

Diabetes mellitus is an increasingly prevalent chronic metabolic condition characterised by impaired glucose homeostasis and raised blood glucose levels (hyperglycaemia). Broadly categorised as either type 1 (T1DM) or type 2 diabetes (T2DM), people with diabetes are largely responsible for self-managing their blood glucose levels. Despite the development of diabetes technologies such as real time continuous glucose monitoring (RT-CGM), many individuals are frequently exposed to iatrogenic low blood glucose levels (hypoglycaemia). Severe hypoglycaemia is associated with an increased risk of recurrent hypoglycaemia, impaired symptomatic awareness of hypoglycaemia, and potentially death if left untreated. This thesis affirmed the existing clinical impact of severe hypoglycaemia and its recurrent risk in a six-month analysis of severe hypoglycaemia attended by the London Ambulance Service NHS Trust (LAS). Fewer incidents of severe hypoglycaemia observed in a date matched repeat analysis during the 2020 COVID-19 lockdown suggested improved self-management possibly motivated by a proximal fear of hospitalisation and improved structure at home. Finally, a 12-week randomised control trial demonstrating a significant difference in time spent in hypoglycaemia <3mmol/L, is the first study to prove the immediate provision of RT-CGM significantly reduces the risk of recurrent hypoglycaemia. Moreover, it highlighted the impact of socioeconomic disparity as a barrier to effective hypoglycaemia risk modification. This guided the design of an adaptive real time intelligent system to enhance self-care of chronic disease (ARISES) aimed to deliver therapeutic and lifestyle decision support for people with T1DM. The ARISES graphic user interface (GUI) design was a collaborative process conceived in a series of focus group meetings including people with T1DM. Finally, a 12-week observational study using RT-CGM, a physiological sensor wristband, and a mobile diary app, allowed for a sub-analysis identifying measurable physiological parameters associated with current and impending hypoglycaemia in people with T1DM.Open Acces

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete?s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomark ers and/or other performance indicators that are crucial to evaluate the evolution of the athlete?s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete?s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more?and more effective?biomarkers regarding the athlete?s internal workload, which limits its wider adoption by the sports community.4811-99FE-2ECD | Luis Paulo RodriguesN/

Analysis and use of the emotional context with wearable devices for games and intelligent assistants

In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

Wearable Biosensors to Understand Construction Workers' Mental and Physical Stress

Occupational stress is defined as harmful physical and mental responses when job requirements are greater than a worker's capacity. Construction is one of the most stressful occupations because it involves physiologically and psychologically demanding tasks performed in a hazardous environment this stress can jeopardize construction safety, health, and productivity. Various instruments, such as surveys and interviews, have been used for measuring workers’ perceived mental and physical stress. However valuable, such instruments are limited by their invasiveness, which prevents them from being used for continuous stress monitoring. The recent advancement of wearable biosensors has opened a new door toward the non-invasive collection of a field worker’s physiological signals that can be used to assess their mental and physical status. Despite these advancements, challenges remain: acquiring physiological signals from wearable biosensors can be easily contaminated from diverse sources of signal noise. Further, the potential of these devices to assess field workers’ mental and physical status has not been examined in the naturalistic work environment. To address these issues, this research aims to propose and validate a comprehensive and efficient stress-measurement framework that recognizes workers mental and physical stress in a naturalistic environment. The focus of this research is on two wearable biosensors. First, a wearable EEG headset, which is a direct measurement of brain waves with the minimal time lag, but it is highly vulnerable to various artifacts. Second, a very convenient wristband-type biosensor, which may be used as a means for assessing both mental and physical stress, but there is a time lag between when subjects are exposed to stressors and when their physiological signals change. To achieve this goal, five interrelated and interdisciplinary studies were performed to; 1) acquire high-quality EEG signals from the job site; 2) assess construction workers’ emotion by measuring the valence and arousal level by analyzing the patterns of construction workers’ brainwaves; 3) recognize mental stress in the field based on brain activities by applying supervised-learning algorithms;4) recognize real-time mental stress by applying Online Multi-Task Learning (OMTL) algorithms; and 5) assess workers’ mental and physical stress using signals collected from a wristband biosensor. To examine the performance of the proposed framework, we collected physiological signals from 21 workers at five job sites. Results yielded a high of 80.13% mental stress-recognition accuracy using an EEG headset and 90.00% physical stress-recognition accuracy using a wristband sensor. These results are promising given that stress recognition with wired physiological devices within a controlled lab setting in the clinical domain has, at best, a similar level of accuracy. The proposed wearable biosensor-based, stress-recognition framework is expected to help us better understand workplace stressors and improve worker safety, health, and productivity through early detection and mitigation of stress at human-centered, smart and connected construction sites.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149965/1/hjebelli_1.pd

ELMOS “Elderly Health Monitor System” as An Android Smartphone-Based Elderly Health Monitor Service

Elderly Health Monitor system named ELMOS as an android smartphone-based elderly health monitor service is essential in preventing elderly health problems, as well as providing a framework or basis for maintaining health awareness. This device comes with three functions which are sensing body temperature, heart rate and fall detection using Arduino. DS18B20 is used for the sense of body temperature. Body temperature could be a basic parameter for monitoring and identification human health. Heartbeat sensor was used for sensing heart rate. Accelerometer MPU 6050 was used to detect a senior citizen falling in real- time and to use the bluetooth communication to notify the administrator of such an event. As a result, we found that the system can be used to measure physiological parameters, such as body temperature, heart rate and fall detection