Feasibility of Using Floor Vibration to Detect Human Falls

Yu Shao, Xinyue Wang, Wenjie Song, Sobia Ilyas, Haibo Guo and Wen-Shao Chang
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Yu Shao: School of Architecture, Harbin Institute of Technology, Harbin 150001, China
Xinyue Wang: School of Architecture, Harbin Institute of Technology, Harbin 150001, China
Wenjie Song: School of Architecture, Harbin Institute of Technology, Harbin 150001, China
Sobia Ilyas: School of Architecture, The University of Sheffield, Sheffield S10 2TN, UK
Haibo Guo: School of Architecture, Harbin Institute of Technology, Harbin 150001, China
Wen-Shao Chang: School of Architecture, The University of Sheffield, Sheffield S10 2TN, UK

IJERPH, 2020, vol. 18, issue 1, 1-22

Abstract: With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Keywords: fall detection; floor vibrations; machine learning; elderly; health and wellbeing; intelligent system (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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