JOURNAL ARTICLE

PerFallD: A pervasive fall detection system using mobile phones

Abstract

Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging "baby boomers", fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency.

Keywords:
Computer science Ubiquitous computing Mobile computing Computer security Human–computer interaction Computer network

Metrics

235
Cited By
10.56
FWCI (Field Weighted Citation Impact)
21
Refs
0.99
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
Non-Invasive Vital Sign Monitoring
Physical Sciences →  Engineering →  Biomedical Engineering
Gait Recognition and Analysis
Physical Sciences →  Engineering →  Biomedical Engineering

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