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Fitness Trackers

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Improving energy expenditure estimates from wearable devices: A machine learning approach.

Journal of sports sciences
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...

Monitoring behavioral symptoms of dementia using activity trackers.

Journal of biomedical informatics
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 ...

Deep Learning to Predict Energy Expenditure and Activity Intensity in Free Living Conditions using Wrist-specific Accelerometry.

Journal of sports sciences
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...

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances.

Sensors (Basel, Switzerland)
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 ...

Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability.

IEEE journal of biomedical and health informatics
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, ...

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning.

Sensors (Basel, Switzerland)
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...

Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Studies in health technology and informatics
Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes...

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Medicine and science in sports and exercise
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...

MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the t...