Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities
Abstract Non‐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 th...
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12110 |
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| author | Guanci Yang Siyuan Yang Kexin Luo Shangen Lan Ling He Yang Li |
| author_facet | Guanci Yang Siyuan Yang Kexin Luo Shangen Lan Ling He Yang Li |
| author_sort | Guanci Yang |
| collection | DOAJ |
| description | Abstract Non‐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. |
| format | Article |
| id | doaj-art-af2bd0343d2d4dc59d1d16493e4b2892 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-af2bd0343d2d4dc59d1d16493e4b28922025-02-03T06:47:18ZengWileyIET Biometrics2047-49382047-49462023-03-011229110110.1049/bme2.12110Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activitiesGuanci Yang0Siyuan Yang1Kexin Luo2Shangen Lan3Ling He4Yang Li5Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaAbstract Non‐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.https://doi.org/10.1049/bme2.12110behavioural sciences computingcomputer visionconvolutional neural netsfeature extractionobject detection |
| spellingShingle | Guanci Yang Siyuan Yang Kexin Luo Shangen Lan Ling He Yang Li Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities IET Biometrics behavioural sciences computing computer vision convolutional neural nets feature extraction object detection |
| title | Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities |
| title_full | Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities |
| title_fullStr | Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities |
| title_full_unstemmed | Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities |
| title_short | Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities |
| title_sort | detection of non suicidal self injury based on spatiotemporal features of indoor activities |
| topic | behavioural sciences computing computer vision convolutional neural nets feature extraction object detection |
| url | https://doi.org/10.1049/bme2.12110 |
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