Fatigue is a broad, multifactorial concept that includes the subjective perception of reduced physical and mental energy levels. It is also one of the key factors that strongly affect patients' health-related quality of life. To date, most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, in this work, we recorded multi-modal physiological data (including ECG, accelerometer, skin temperature and respiratory rate, as well as demographic information such as age, BMI) in free-living environments and developed automated fatigue assessment models. Specifically, we extracted features from each modality and employed the random forest-based mixed-effects models, which can take advantage of the demographic information for improved performance. We conducted experiments on our collected dataset, and very promising preliminary results were achieved. Our results suggested ECG played an important role in the fatigue assessment tasks.
Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This paper primarily focuses on building an experimental setup that induces cognitive fatigue (CF) and physical fatigue (PF) through…
Within the last decade, running has become one of the most popular physical activities in the world. Although the benefits of running are numerous, there is a risk of Running Related Injuries (RRI) of the lower extremities. Electromyography (EMG) techniques have previously been used to study causes of RRIs, but the complexity of this technology limits its use to a laboratory setting. As running is primarily an outdoors activity, this lack of technology acts as a barrier to the study of RRIs in natural environments. This study presents a minimally invasive wearable muscle sensing device consist…
CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
Department of Computer Science, College of AI, Cyber and Computing, The University of Texas at San Antonio（计算机科学系，人工智能、网络与计算学院，德克萨斯大学圣安东尼奥分校）；Department of Educational Psychology, College of Education and Human Development, The University of Texas at San Antonio（教育心理学系，教育与人类发展学院，德克萨斯大学圣安东尼奥分校）
Aalto University（阿尔托大学）；Helsinki University Hospital（赫尔辛基大学医院）；University of Helsinki（赫尔辛基大学）；Tampere University（坦佩雷大学）
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time-series modelling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and non-ideal recording conditions. The ex…
Human memory prioritizes the storage and recall of information that is emotionally-arousing and/or important in a process known as value-directed memory. When experiencing a stream of information (e.g. conversation, book, lecture, etc.), the individual makes conscious and subconscious value assessments of the incoming information and uses this as a metric to determine what to remember. In order to improve automatic recall of memory, previous memory augmentation systems have sensed users' physiological state to determine which sensory media should be prioritized. Here, we propose to prioritize …
Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques
Fatima Jinnah Women University（法蒂玛·真纳女子大学）；University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；Bahria University（巴哈里亚大学）；Children’s National Hospital（国家儿童医院）；George Washington University（乔治华盛顿大学）；Electronics and Telecommunications Research Institute (ETRI)（电子与电信研究所（ETRI））
Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases
VTT Technical Research Centre of Finland（芬兰技术研究中心）；Newcastle upon Tyne Hospitals NHS Foundation Trust（纽卡斯尔 upon Tyne 医院 NHS 信托基金会）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；Kiel University（基尔大学）；George Huntington Institute（乔治·亨廷顿研究所）；Erasmus University Medical Centre（伊拉斯姆斯大学医学中心）
Modeling biological rhythms helps understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms. The first step in modeling biological rhythms is to identify their characteristics, such as cyclic periods, phase, and amplitude. However, human rhythms are susceptible to external events, which cause irregular fluctuations in waveforms and affect the characterization of each rhythm. In this paper, we present our exploratory work …
Newcastle University（纽卡斯尔大学）；Great North Childrens Hospital（大北方儿童医院）；Georgia Institute of Technology（佐治亚理工学院）
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and…
Hainan Rural Credit Union（海南省农村信用社联合社）；University of Warwick（华威大学）；Nanjing Normal University（南京师范大学）；Newcastle University（纽卡斯尔大学）
Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we develop an automated system that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data is collected from 59 stroke surv…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Maryland, Baltimore（马里兰大学巴尔的摩分校）
Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal process…
AktivTalk: Digitizing the Talk Test for Voice-Based Exercise Intensity Self-Assessment and Exploring Automated Classification from Speech
Ludwig Boltzmann Institute for Digital Health and Prevention（路德维希·玻尔兹曼数字健康与预防研究所）；University of Innsbruck（因斯布鲁克大学）；University of Southern Denmark（南丹麦大学）
Coaching technology, wearables and exergames can provide quantitative feedback based on measured activity, but there is little evidence of qualitative feedback to aid technique improvement. To achieve personalised qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique utilising three-dimensional tennis motion data acquired from multi-camera video. Expert data labelling relied on virtual 3D stick figure replay. Diverse assessment criteria for novice to intermediate skill levels and …
