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Fall Detection System With Artificial Intelligence-Based Edge Computing
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AI
DOI：10.1109/ACCESS.2021.3140164.png)
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摘要
En 中文
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.
Keyword:
Edge computing
Fall detection
Servers
Cloud computing
Cameras
Neural networks
Older adults
Deep learning
edge computing
fall detection
neuromorphic computing hardware
the IoT
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate Algorithm使用可穿戴传感器的老年人跌倒检测: 一种低成本高精度算法.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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