Feasibility of Using Floor Vibration to Detect Human Falls
Journal article, 2021

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.

fall detection

health and wellbeing

floor vibrations

elderly

machine learning

intelligent system

Author

Yu Shao

Harbin Institute of Technology

Xinyue Wang

Harbin Institute of Technology

wenjie song

Harbin Institute of Technology

Sobia Ilyas

University of Sheffield

Haibo Guo

Harbin Institute of Technology

Wen-Shao Chang

University of Sheffield

International Journal of Environmental Research and Public Health

1661-7827 (ISSN) 1660-4601 (eISSN)

Vol. 18 1

Subject Categories

Geriatrics

Occupational Therapy

Environmental Health and Occupational Health

DOI

10.3390/ijerph18010200

More information

Latest update

10/23/2023