AIMC Topic: Fitness Trackers
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Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
Medicine and science in sports and exercise
May 15, 2024
PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices agains...
Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning.
Sensors (Basel, Switzerland)
Apr 14, 2023
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of str...
Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability.
IEEE journal of biomedical and health informatics
Aug 11, 2022
The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, ...
Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances.
Sensors (Basel, Switzerland)
Feb 9, 2022
Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these ...
Deep Learning to Predict Energy Expenditure and Activity Intensity in Free Living Conditions using Wrist-specific Accelerometry.
Journal of sports sciences
Oct 30, 2020
Wrist-worn accelerometers are more comfortable and yield greater compliance than hip-worn devices, making them attractive for free-living activity assessments. However, intricate wrist movements may require more complex predictive models than those a...
Monitoring behavioral symptoms of dementia using activity trackers.
Journal of biomedical informatics
Aug 9, 2020
Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) and their caregivers is hampered by the presence of behavioral and psychological symptoms of dementia ...
Improving energy expenditure estimates from wearable devices: A machine learning approach.
Journal of sports sciences
Apr 6, 2020
A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of act...
Bicycling
Galvanic Skin Response
Jogging
Humans
Female
Adult
Machine Learning
Algorithms
Heart Rate
Body Mass Index
Male
Energy Metabolism
Walking
Middle Aged
Body Composition
Calorimetry, Indirect
Body Temperature
Accelerometry
Activities of Daily Living
Exercise
Sedentary Behavior
Fitness Trackers
Profiling movement behaviours in pre-school children: A self-organised map approach.
Journal of sports sciences
Nov 7, 2019
Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC...
A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.
IEEE journal of biomedical and health informatics
Jun 11, 2019
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...
Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning.
Journal of sports sciences
Dec 13, 2018
Fast bowlers are at a high risk of overuse injuries. There are specific bowling frequency ranges known to have negative or protective effects on fast bowlers. Inertial measurement units (IMUs) can classify movements in sports, however, some commercia...
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