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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

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%).

Original languageEnglish
Article number8478181
Pages (from-to)9882-9890
Number of pages9
JournalIEEE Sensors Journal
Volume18
Issue number23
DOIs
StatePublished - 1 Dec 2018

Keywords

  • Fall detection systems
  • machine learning models
  • sampling rates
  • wearable sensors

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