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Open AccessArticle

Fall Detection Based on Key Points of Human-Skeleton Using OpenPose

Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
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Symmetry 2020, 12(5), 744; https://doi.org/10.3390/sym12050744
Received: 25 March 2020 / Revised: 16 April 2020 / Accepted: 16 April 2020 / Published: 5 May 2020
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies II)
According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall.The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking, and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by OpenPose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to-height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior. View Full-Text
Keywords: fall detection; openpose; skeleton extraction fall detection; openpose; skeleton extraction
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Chen, W.; Jiang, Z.; Guo, H.; Ni, X. Fall Detection Based on Key Points of Human-Skeleton Using OpenPose. Symmetry 2020, 12, 744.

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