S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System

Seung Baek Hong,Yu Hwan Kim,Se Hyun Nam,Kang Ryoung Park

doi:10.3390/math10030328

Abstract

Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the accuracy of FDS depending on various environmental changes. Given these facts, in this study, we newly propose an FDS method based on the squeeze and excitation (SE) 3D convolutional neural networks (S3D). In our method, keyframes are extracted from input thermal videos using the optical flow vectors, and the fall detection is carried out based on the output of the proposed S3D, using the extracted keyframes as input. Comparative experiments were carried out on three open databases of thermal videos with different image resolutions, and our proposed method obtained F1 scores of 97.14%, 95.30%, and 98.89% in the Thermal Simulated Fall, Telerobotics and Control Lab fall detection, and eHomeSeniors datasets, respectively (the F1 score is a harmonic mean of recall and precision; it was confirmed that these are superior results to those obtained using the state-of-the-art methods of a thermal camera-based FDS.

Highlights

With the progress of the aging population across the globe, the number of falling accidents is increasing, and the injury rate in the case of falling accidents has reached20–30%
In this study, we newly propose an fall detection system (FDS) method based on the squeeze and excitation (SE) 3D convolutional neural networks (CNNs) (S3D), which can improve the accuracy of the fall detection by applying a revised SE block considering 3D CNN
We newly proposed an S3D-based FDS method

Summary

Introduction

With the progress of the aging population across the globe, the number of falling accidents is increasing, and the injury rate in the case of falling accidents has reached20–30%. In the case of existing FDS methods using wearable devices, their wide utilization is impeded by the disadvantage that the device must be attached to the body or be carried at all times. Ambient device-based FDSs that operate in a sensor-embedded environment, such that the sensor does not need to be attached to the body, are more convenient. Systems that use several sensors suffer from the disadvantage of sensitivity to the location or angle of the sensors [4]. To this end, vision-based FDS studies have been conducted, and vision-based

Methods

Discussion

Conclusion