摘要： 针对基于可穿戴设备的健身动作识别方法收集标注的传感器数据可能造成隐私泄露风险,以及传统中心化模型训练方法在新用户适应性方面存在局限的问题,提出一种基于可穿戴运动护膝和个性化联邦学习(FL)的健身动作识别方法。该方法实现了健身动作类型识别和用户表现水平识别两项任务,帮助用户了解自身健身表现,提高锻炼效果。首先,将每位用户视为独立的任务,以联邦的方式元训练一个全局嵌入网络,学习跨用户的共享表征,此表征可以有效地泛化到任意用户;然后,通过一个适应化流程,在全局嵌入网络的基础上对本地分类网络进行两阶段微调,使每位用户获得个性化模型;最后,在来自真实世界的健身数据集上进行大量实验,以验证该方法的性能表现。实验结果表明,所提方法实现了100%的健身动作类型识别准确率和95.94%的用户表现水平识别准确率,显著优于现有先进方法。所提系统能够保护用户隐私,同时具备良好的新用户泛化能力。

关键词: 健身动作识别, 可穿戴运动护膝, 个性化联邦学习, 隐私保护, 全局嵌入网络

Abstract: To address the privacy leakage risks associated with collecting and annotating sensor data for wearable device-based fitness action recognition as well as the limitations of traditional centralized model training in adapting to new users, this study proposes a fitness action recognition method based on a wearable sports knee sleeve and personalized Federated Learning (FL). The method accomplishes two tasks, namely fitness action type recognition and user performance level recognition, to help users understand their fitness performance and improve exercise effectiveness. First, each user is treated as an independent task, and a global embedding network is meta-trained in a federated manner to learn user-agnostic shared representations that can be effectively generalized for any user. Subsequently, through an adaptation procedure, the local classification network is fine-tuned in two stages on top of the global embedding network, enabling each user to obtain a personalized model. Finally, extensive experiments are conducted on a real-world fitness dataset to validate the performance of the proposed method. The experimental results demonstrate that the proposed method achieves 100% accuracy in fitness action type recognition and 95.94% accuracy in user performance level recognition, significantly outperforming existing state-of-the-art methods. The proposed system can protect user privacy while exhibiting a strong generalization ability for new users.

Key words: fitness action recognition, wearable sports knee sleeve, personalized Federated Learning (FL), privacy protection, global embedding network

中图分类号: 

• TP391