A machine learning approach to fall detection algorithm using wearable sensor

Falls are the primary cause of accidents for the elderly in living environment. Falls frequently cause fatal and non-fatal injuries that are associated with a large amount of medical costs. Reduction hazards in living environment and doing exercise for training balance and muscle are the common stra...

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Veröffentlicht in:2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE) S. 707 - 710
Hauptverfasser: Chia-Yeh Hsieh, Chih-Ning Huang, Kai-Chun Liu, Woei-Chyn Chu, Chia-Tai Chan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2016
Schlagworte:
Acceleration
Detection algorithms
fall detection
Feature extraction
machine learning
Sensitivity
Support vector machines
Training
wearable sensor
Wearable sensors
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Zusammenfassung:Falls are the primary cause of accidents for the elderly in living environment. Falls frequently cause fatal and non-fatal injuries that are associated with a large amount of medical costs. Reduction hazards in living environment and doing exercise for training balance and muscle are the common strategies for fall prevention. But falls cannot be avoided completely; fall detection provides the alarm in time that can decrease the injuries or death caused by no rescue. We propose machine learning-based fall detection algorithm using multi-SVM with linear, quadratic or polynomial kernel function, and k-NN classifier. Eight kinds of falling postures and seven types of daily activities arranged in the experiment are used to explore the performance of the machine learning-based fall detection algorithm. The emulated falls were performed on a soft mat by ten healthy young subjects wearing protectors. The k-nearest neighbor method with 0.1 second window size has the highest accuracy, which is 96.26%. The results show that the proposed machine learning fall detection algorithm can fulfill the requirements of adaptability and flexibility for the individual differences.
DOI:10.1109/ICAMSE.2016.7840209