Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

Kai Chun Liu, Chia Yeh Hsieh, Steen Jun Ping Hsu, Chia Tai Chan*

*此作品的通信作者

研究成果: Article同行評審

42 引文 斯高帕斯(Scopus)

摘要

Falls are a leading health risk for the elderly. Various wearable-based fall detection systems based on machine learning models have been developed to provide emergency alarms and services to improve safety and health-related quality of life. To support long-term healthcare services, the appropriate sampling rate, which plays a vital role in a fall detection system, must be investigated to guarantee the performance accuracy and energy efficiency. Intuitively, decreasing the sampling rate of sensor nodes can provide computing and energy savings. However, there is a lack of research exploring the performance accuracy of fall detection systems in terms of sampling rate, especially for machine learning models. In this paper, the effects of decreasing sampling rates (ranging from 200/128 to 3 Hz) on wearable-based fall detection systems are investigated based on four machine learning models: support vector machine (SVM), k -nearest neighbor, Naïve Bayes, and decision tree. Two emulated fall data sets, the SisFall public data set and the proposed data set of this paper, are used to allow an objective investigation of sampling rates. The findings show that fall detection systems based on SVM modeling and a radial basis function could achieve at least 98% and 97% accuracy, with sampling rates of 11.6 and 5.8 Hz, respectively. Overall, the experimental results demonstrate that a sampling rate of 22 Hz is sufficient for most machine learning models to support wearable-based fall detection systems (accuracy ≥97%).

原文English
文章編號8478181
頁(從 - 到)9882-9890
頁數9
期刊IEEE Sensors Journal
18
發行號23
DOIs
出版狀態Published - 1 12月 2018

指紋

深入研究「Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models」主題。共同形成了獨特的指紋。

引用此