Affiliation:
1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang China
2. State Key Laboratory of Public Big Data Guizhou University Guiyang China
3. School of Mechanical Engineering Guizhou University Guiyang China
4. Guizhou Communication Industry Service Co., Ltd Guiyang China
Abstract
AbstractNon‐suicide self‐injury (NSSI) can be dangerous and difficult for guardians or caregivers to detect in time. NSSI refers to when people hurt themselves even though they have no wish to cause critical or long‐lasting hurt. To timely identify and effectively prevent NSSI in order to reduce the suicide rates of patients with a potential suicide risk, the detection of NSSI based on the spatiotemporal features of indoor activities is proposed. Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. Secondly, an NSSI detection algorithm based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection calculates the human bounding box by using an object detection model and employs a behaviour detection model to extract the temporal and spatial features of NSSI behaviour. Thirdly, the optimal combination schemes of NssiDetection is investigated by checking its performance with different behaviour detection methods and training strategies. Lastly, a case study is performed by implementing an NSSI behaviour detection prototype system. The prototype system has a recognition accuracy of 84.18% for NSSI actions with new backgrounds, persons, or camera angles.
Funder
National Natural Science Foundation of China
Science and Technology Foundation of Guizhou Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Computer Vision and Pattern Recognition,Signal Processing,Software
Cited by
16 articles.
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