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Sensors and Materials, Volume 32, Number 4(1) (2020)
Copyright(C) MYU K.K.
pp. 1209-1221
S&M2171 Research Paper of Special Issue
Published: April 10, 2020

Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment [PDF]

Junsuo Qu, Chen Wu, Qian Li, Ting Wang, and Abdel Hamid Soliman

(Received July 15, 2019; Accepted October 18, 2019)

Keywords: fall detection, eigenvalues, support vector, SVM fall model

The use of a single method of acceleration threshold discrimination cannot fully characterize the change in human fall behavior, which can easily result in misjudgment. In this paper, we propose a human fall detection algorithm that combines human posture, support vector machine (SVM), and quadratic threshold decision. Firstly, a large number of human posture data are collected through a six-axis inertial measurement module (MPU6050). A fall detection model is established through filtering preprocessing, eigenvalue extraction, classification, and SVM training. Secondly, a first-level threshold determination is performed through a wearable wristband device. When a suspected fall occurs, six eigenvalues will be captured and uploaded to a cloud platform to trigger second-level SVM fall judgments. By matching the eigenvalues with the fall detection model, it can be determined accurately whether a fall has taken place. The experimental results show that the fall detection has a recognition rate of 92.2%, a false rate of 3.593%, and missing rate of 2.187%, which can better distinguish other nonfall actions.

Corresponding author: Junsuo Qu

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Cite this article
Junsuo Qu, Chen Wu, Qian Li, Ting Wang, and Abdel Hamid Soliman, Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment, Sens. Mater., Vol. 32, No. 4, 2020, p. 1209-1221.

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