Yonatan E Brand
A5043771615- Gait Recognition and Analysis
- Balance, Gait, and Falls Prevention
- Context-Aware Activity Recognition Systems
- Diabetic Foot Ulcer Assessment and Management
- Indoor and Outdoor Localization Technologies
- Cerebral Palsy and Movement Disorders
Tel Aviv University
2022-2024
Tel Aviv Sourasky Medical Center
2022-2024
Background Wrist-worn inertial sensors are used in digital health for evaluating mobility real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, detection is an important step to identify regions interest where occurs, which requires robust algorithms due complexity arm movements. While exist other sensor positions, a comparative validation applied wrist position on data sets across different disease populations missing. Furthermore,...
Remote assessment of the gait older adults (OAs) during daily living using wrist-worn sensors has potential to augment clinical care and mobility research. However, hand movements can degrade detection from wrist-sensor recordings. To address this challenge, we developed an anomaly algorithm compared its performance four previously published algorithms. Multiday accelerometer recordings a lower-back sensor (i.e., “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s...
Progressive gait impairment is common in aging adults. Remote phenotyping of during daily living has the potential to quantify alterations and evaluate effects interventions that may prevent disability population. Here, we developed ElderNet, a self-supervised learning model for detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without labels, as well 83 with labeled data: older adults Parkinson's disease, proximal...
<sec> <title>BACKGROUND</title> Wrist-worn inertial sensors are used in digital health for evaluating mobility real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, detection is an important step to identify regions interest where occurs, which requires robust algorithms due complexity arm movements. While exist other sensor positions, a comparative validation applied wrist position on data sets across different disease populations...