A5027684402- Physical Activity and Health
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Balance, Gait, and Falls Prevention
- Sleep and related disorders
- Sleep and Wakefulness Research
- Diabetic Foot Ulcer Assessment and Management
- Context-Aware Activity Recognition Systems
- Energy Efficient Wireless Sensor Networks
- Cerebral Palsy and Movement Disorders
- Mobile Health and mHealth Applications
- Indoor and Outdoor Localization Technologies
- Health, Environment, Cognitive Aging
- Obstructive Sleep Apnea Research
- Cardiovascular and exercise physiology
The objective of this paper was to develop an end-to-end algorithm that would improve the step counting accuracy in regular walking/running data and also meet ANSI/CTA-2056 standards. standards are achieve error rate less than 10% on treadmill at least 20 participants. Our UWF-algorithm (UWFv1) has improved performs well below acceptable rate, using both non-treadmill data, hence our UWFv1 meets ANSI-CTA-2056 For algorithm, random forest model, trained a feature engineered dataset, chosen as...
Abstract Background Digital clinical measures based on data collected by wearable devices have seen rapid growth in both trials and healthcare. The widely-used wearables are epoch-based physical activity counts using accelerometer data. Even though been the backbone of thousands epidemiological studies, there large variations algorithms that compute their associated parameters – many which often kept proprietary device providers. This lack transparency has hindered comparability between...
Abstract Step count is one of the most used real-world (RW) outcomes for understanding physical functioning, activity, and overall quality life. In current investigation, we systematically evaluated performances modern wrist-accelerometry-based algorithms based on peak detection, autocorrelation, template matching, movement frequency machine learning a common dataset that included continuous walking trials varying speeds regularities. The accuracies were computed with respect to ground truth...
Abstract Step count is one of the most used real-world (RW) outcomes for understanding physical functioning, activity, and overall quality life. In current investigation, we systematically evaluated performances modern wrist-accelerometry-based algorithms based on peak detection, autocorrelation, moving-average vector magnitude (MAVM), template matching, movement frequency machine learning a common dataset that included continuous walking trials varying speeds regularities. The accuracies...
Abstract For the last 40 years, actigraphy or wearable accelerometry has provided an objective, low- burden and ecologically valid approach to assess real-world sleep circadian patterns, contributing valuable data epidemiological clinical insights on disorders. The proper use of technology in research requires validated algorithms that can derive outcomes from sensor data. Since publication first automated scoring algorithm by Webster 1982, a variety have been developed contributed research,...