通过嵌入个体差异性提升可穿戴传感器的活动识别泛化能力
Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition
- 在对抗学习中引入个体间差异性约束，生成跨个体不变特征
- 在三个公开数据集上实现比现有方法更高的分类准确率
- 适合关注可穿戴设备个性化差异问题的研究者
本文研究基于可穿戴惯性传感器的人体活动识别（HAR）问题。主要挑战在于不同个体执行相同动作时存在显著差异，导致模型泛化能力受限。为此，提出一种新型深度对抗框架，将个体间差异性作为对抗任务的核心目标，促使模型学习对个体不敏感的特征表示，从而提升分类性能。在三个标准HAR数据集上采用留一主体交叉验证（LOSO）测试，结果表明该方法优于已有方法。进一步分析显示，所提对抗任务能有效降低特征空间中不同用户间的个体差异，且在相同框架下表现优于以往对抗任务。代码已开源：https://github.com/FranciscoCalatrava/EmbeddedSubjectVariability.git
原文摘要 · Abstract (English)
This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabilities to new unseen individuals due to inter-subject variability, i.e., the same activity is performed differently by different individuals. To address this problem, we propose a novel deep adversarial framework that integrates the concept of inter-subject variability in the adversarial task, thereby encouraging subject-invariant feature representations and enhancing the classification performance in the HAR problem. Our approach outperforms previous methods in three well-established HAR datasets using a leave-one-subject-out (LOSO) cross-validation. Further results indicate that our proposed adversarial task effectively reduces inter-subject variability among different users in the feature space, and it outperforms adversarial tasks from previous works when integrated into our framework. Code: https://github.com/FranciscoCalatrava/EmbeddedSubjectVariability.git
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