用对抗学习解决不同人做同一动作差异大的问题，提升动作识别准确率。
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition
- 设计基于动作的对抗性区分任务，捕捉个体差异
- 在三个数据集上均超越已有方法，LOOCV下表现更优
- 适合研究个性化动作识别或传感器数据建模的人
我们提出一种新的对抗深度学习框架，用于基于可穿戴惯性传感器的人体动作识别（HAR）问题。该框架引入了一种新颖的对抗性动作区分任务，以应对个体间差异——即不同人执行相同动作的方式存在差异。在三个HAR数据集上，采用留一人外交叉验证（LOOCV）基准测试，所提框架整体性能优于先前方法。额外实验表明，在相同对抗框架内，本区分任务相比以往任务能获得更好的分类效果。
原文摘要 · Abstract (English)
We present a new adversarial deep learning framework for the problem of human activity recognition (HAR) using inertial sensors worn by people. Our framework incorporates a novel adversarial activity-based discrimination task that addresses inter-person variability-i.e., the fact that different people perform the same activity in different ways. Overall, our proposed framework outperforms previous approaches on three HAR datasets using a leave-one-(person)-out cross-validation (LOOCV) benchmark. Additional results demonstrate that our discrimination task yields better classification results compared to previous tasks within the same adversarial framework.
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