论文标题
使用神经网络的代表性分析进行人类活动识别的转移学习
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks
论文作者
论文摘要
由于其在移动健康监测，活动识别和患者康复中的应用，近年来人类活动认可（HAR）研究已有所增加。典型的方法是与已知用户介绍HAR分类器离线，然后为新用户使用相同的分类器。但是，如果新用户的活动模式与培训数据中的活动不同，则新用户的准确性可能会很低。同时，由于较高的计算成本和培训时间，从头开始对新用户的培训对于移动应用程序是不可行的。为了解决这个问题，我们建议使用两个组件的HAR转移学习框架。首先，代表性分析揭示了可以在用户和需要自定义的用户特定功能转移的常见功能。使用此洞察力，我们将离线分类器的可重复使用部分传输到新用户，而仅将其剩下。与基线相比，我们使用五个数据集的实验提高了43％的精度和66％的训练时间，而无需使用转移学习。此外，NVIDIA JETSON XAVIER-NX硬件平台的测量表明，功率和能耗分别降低了43％和68％，同时与从头开始的培训相同或更高的精度。
Human activity recognition (HAR) research has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the Nvidia Jetson Xavier-NX hardware platform reveal that the power and energy consumption decrease by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch.
