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Sensors 2017, 17(2), 307; doi:10.3390/s17020307

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

Department of Biomedical Engineering, National Yang-Ming University, Taipei 112, Taiwan
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Author to whom correspondence should be addressed.
Academic Editors: Subhas Chandra Mukhopadhyay, Hemant Ghayvat and Nagender Kumar Suryadevara
Received: 24 November 2016 / Revised: 26 January 2017 / Accepted: 3 February 2017 / Published: 8 February 2017
(This article belongs to the Special Issue Sensors for Home Automation and Security)
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Abstract

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. View Full-Text
Keywords: fall detection algorithm; multiphase fall model; wearable sensor fall detection algorithm; multiphase fall model; wearable sensor
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MDPI and ACS Style

Hsieh, C.-Y.; Liu, K.-C.; Huang, C.-N.; Chu, W.-C.; Chan, C.-T. Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model. Sensors 2017, 17, 307.

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