JOURNAL ARTICLE

Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities

Abstract

This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. Algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities.

Keywords:
Accelerometer Waist Algorithm Computer science Sensitivity (control systems) Artificial intelligence Set (abstract data type) Machine learning Medicine Body mass index Engineering

Metrics

55
Cited By
4.48
FWCI (Field Weighted Citation Impact)
20
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Balance, Gait, and Falls Prevention
Health Sciences →  Health Professions →  Physical Therapy, Sports Therapy and Rehabilitation
Non-Invasive Vital Sign Monitoring
Physical Sciences →  Engineering →  Biomedical Engineering
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