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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51479

Title: Development of Home Intelligent Falling Detection IoT System based on Feedback Optical Flow Convolutional Neural Network
Authors: Yi-Zeng Hsieh
Yu-Lin Jeng
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Senior citizens
Neural networks
Streaming media
Wearable sensors
Shape
Date: 2017-11-09
Issue Date: 2018-11-30T07:47:53Z
Publisher: IEEE Access
Abstract: Abstract: Fall events are important health issues in elderly living environments such as homes. Hence, a confident and real-time video surveillance device that pays attention could better their everyday lives. We proposed an optical flow feedback convolutional neural network according to the video stream in a home environment. Our proposed model uses rule-based filters before an input convolutional layer and the recorded optical flow for supervising the optical flow of variation. Detecting human posture is a key factor, while fall events are like a falling posture. By sequencing frames of action, it is possible to recognize a fall. Our system can clearly detect the normal lying posture and lying after falling. Our proposed method can efficiently detect action motion and recognize the action posture. We compared the performance with other standard benchmark data sets and deployed our model to simulate a real-home situation, and the correct ratio achieved 82.7% and 98% separately.
Relation: 6 pp.6048 - 6057
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51479
Appears in Collections:[電機工程學系] 期刊論文

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