Fall Detection System With Artificial Intelligence-Based Edge Computing

Bor-Shing Lin,Hung-Kai Hsu,Chueh-Ho Lin,Chih-Wei Peng,Zhao Zhang,I-Jung Lee,Tiku Yu

doi:10.1109/access.2021.3140164

Abstract

Falls are the second leading cause of death from unintentional injuries in older adults. Although many systems have been used to detect falls, they are limited by the computational complexity of the algorithm. The images taken by the camera must be transmitted through a network to the back-end server for calculation. As the demand for Internet of Things increases, this architecture faces such problems as high bandwidth costs and server computing overload. Emerging methods reduce the workload of servers by transferring certain computing tasks from cloud servers to edge computing platforms. To this end, this study developed a fall detection system based on neuromorphic computing hardware, which streamlines and transplants the neural network model of the back-end computer to the edge computing platform. Through the neural network model with integer 8 bit precision deployed on the edge computing platform, the object photos obtained by the camera are converted into human motion features, and a support vector machine is then used for classification. After experimental evaluation, an accuracy of 96% was reached, the detection speed of the overall system was 11.5 frames per second, and the power consumption was 0.3 W. This system can monitor the fall events of older adults in real time and over a long period. All data were calculated on the edge computing platform. The system only reports fall events via Wi-Fi, thereby protecting the privacy of the user.

Highlights

The older adult population is expected to reach 1.4 billion by 2030 and 2.1 billion by 2050 [1]
To address the aforementioned problems, this study proposed a fall detection system based on edge computing, which combined a camera and neuromorphic computing hardware based on an application-specific integrated circuit
The artificial intelligence (AI) model used for the bounding human body in this system was scheduled to be implemented with You Only Look Once (YOLO) v2-tiny, YOLO v3-tiny, or an improved version, because the algorithm of the YOLO system is effective for human body detection and its small size facilitates transplantation to the edge computing platform

Summary

Introduction

The older adult population is expected to reach 1.4 billion by 2030 and 2.1 billion by 2050 [1]. Older adults experience more impairment in vision, balance, and cognition, all of which increase the chances of a fall. Thirty percent of elderly people over 65 years fall at least once every year, causing severe or even fatal damage. Only one-third of people received medical assistance following a fall. Medical cost of fatal older adult falls was an estimated US 754 million dollars in 2015 [2]. In traditional fall detection systems for older adults, sensors and cameras are used to track the motion of individuals, and the sensor data and image data are sent to servers for analysis [3]–[9]. When a fall event is detected by the system, the server immediately notifies medical staff of the emergency. The main disadvantage of uploading a large amount of data to

Methods

Results

Discussion

Conclusion