论文标题
学习基于可穿戴的人类活动识别的学习解开行为模式
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition
论文作者
论文摘要
在基于可穿戴的人类活动识别（HAR）研究中，主要的挑战之一是类内部的可变性问题。收集的活动信号通常（即使不是总是）以及由个人，环境或其他因素引起的噪音或偏见，因此很难学习HAR任务的有效功能，尤其是在数据不足时。为了解决这个问题，在这项工作中，我们提出了一个行为模式解开（BPD）框架，该框架可以将行为模式与无关的噪声（例如个人风格或环境噪音等无关的声音等）中的差异，我们基于分离网络等，我们设计了几个损失功能，并使用了一些损失功能，并使用了对对抗性训练策略的优化，而不是依赖依赖的依赖性（依赖性），而不是依赖（依赖性）。我们的BPD框架是灵活的，可以在现有深度学习（DL）方法的基础上用于功能改进。在四个公共HAR数据集上进行了广泛的实验，我们提出的BPD计划的有希望的结果表明其灵活性和有效性。这是一个开源项目，可以在http://github.com/jie-su/bpd上找到该代码
In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD
