在存在阻塞时识别人类活动
Ioannis Vernikos1, Theodoros Spyropoulos2, Evaggelos Spyrou1
1Department of Informatics and Telecommunications, University of Thessaly, 35131 Lamia, Greece.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
闭塞显著影响人类活动识别 (HAR) 性能. 使用人工封闭数据训练卷积神经网络 (CNN) 在现实场景中提高了HAR准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 阻塞是人类活动识别 (HAR) 中的一个重大挑战,导致关键运动数据的丢失.
- 大多数HAR研究都忽略了遮蔽,通常使用在理想的,非遮蔽条件下收集的数据集.
- 现实世界的环境经常涉及封闭,影响HAR系统的可靠性.
研究的目的:
- 开发和评估一种策略,以改善在存在闭塞的HAR性能.
- 研究使用人工遮蔽数据进行训练对HAR准确性的影响.
- 在不同的评估环境中评估拟议方法的稳定性.
主要方法:
- 使用卷积神经网络 (CNN) 进行HAR,处理3D骨运动的2D表示.
- 通过模拟识别一个或两个身体部位来人工生成封闭数据样本.
- 用干净和人为封闭的数据集训练了CNN.
主要成果:
- 拟议的训练策略在存在闭塞时显著提高了HAR性能.
- 评估是在单一视图,交叉视图和跨主题场景中进行的.
- 实验使用了两个大规模的人类运动数据集来验证研究结果.
结论:
- 用人工封闭的数据训练HAR模型是一种有效的策略,可以减轻性能退化.
- 该方法在具有挑战性的,封闭的环境中表现出稳健性和改进的准确性.
- 这项工作解决了当前HAR研究的关键局限性,为更实用的应用铺平了道路.
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