在工作室和现实世界数据集中对自我伤害检测的行为识别模型进行基准测试
Kanghee Lee1,2, Doohee Lee1,2, Hyun-Sik Ham1
1Department of Research and Development, Ziovision Co., Ltd., Chuncheon, 24341, Gangwon, Republic of Korea.
Scientific reports
|January 31, 2026
概括
开发自动化系统以防止患者自我伤害需要更好的数据. 这项研究创建了一个新的数据集,并发现当前的模型难以从模拟到现实世界的精神病房行为进行概括.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 临床心理学 临床心理学
背景情况:
- 预防精神病患者自我伤害的自动化系统至关重要,但缺乏足够的现实训练数据.
- 现有的数据集往往无法捕捉在真实临床环境中观察到的行为多样性和复杂性.
研究的目的:
- 引入一套新的数据集,用于培训和评估自动化自我伤害预防系统.
- 在模拟和现实世界的患者行为上对最先进的动作识别模型进行基准测试.
- 分析模拟和现实数据之间的性能差距,并确定局限性.
主要方法:
- 创建了一个新的数据集,包括1120个模拟切割视频和118个现实视频 (包括和).
- 对基于卷积和基于变压器的动作识别模型进行了全面的基准测试.
- 绩效评估使用了对模拟数据的7倍Leave-One-Out-Across-Subjects (LOAO) 交叉验证和对真实数据的直接测试.
主要成果:
- 顶级模型VideoMAEv2在模拟数据上获得了0.65的F1得分,但在真实数据上下降到0.61,这表明了显著的"模拟与真实"差距.
- 由于模拟行为与真实环境中的多样化,封闭行为相比,模拟行为的统一性,模型难以泛化.
- 定性分析揭示了模型概括中的特定失败点.
结论:
- 目前的动作识别模型在将患者自伤行为从模拟到现实世界的概括方面存在局限性.
- 开发的数据集和基准为未来在这一关键领域的研究提供了基础.
- 迫切需要更多多样化的数据集和先进的AI方法来增强患者安全技术.
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