Feasibility of Using Floor Vibration to Detect Human Falls

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.

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

“…The algorithm tracked the distance between the neck of the user and the ground, and if the distance was found to decrease with a situation lasting greater than a minute, then the algorithm interpreted the situation as a fall. Shao et al [25] proposed an approach that analyzed the characteristics of floor vibrations to detect falls. The authors performed experiments with objects and humans falling on the ground to study the characteristics of floor vibrations.…”

“…Several fall detection systems are dependent on external parameters that cannot be controlled and could affect the performance characteristics. For instance, Shao et al [25] proposed a fall detection methodology based on measuring the vibrations of the floor. Several factors such as the weight of the user, the material of the floor, the condition of the floor, and other objects placed on the floor can impact the intensity of vibrations that could affect the performance of the system.…”