The rapid growth of wearable sensor technologies holds substantial promise for the field of personalized and context-aware Human Activity Recognition. Given the inherently decentralized nature of data sources within this domain, the utilization of multi-agent systems with their inherent decentralization capabilities presents an opportunity to facilitate the development of scalable, adaptable, and privacy-conscious methodologies. This paper introduces a collaborative distributed learning approach rooted in multi-agent principles, wherein individual users of sensor-equipped devices function as agents within a distributed network, collectively contributing to the comprehensive process of learning and classifying human activities. In this proposed methodology, not only is the privacy of activity monitoring data upheld for each individual, eliminating the need for an external server to oversee the learning process, but the system also exhibits the potential to surmount the limitations of conventional centralized models and adapt to the unique attributes of each user. The proposed approach has been empirically tested on two publicly accessible human activity recognition datasets, specifically PAMAP2 and HARTH, across varying settings. The provided empirical results conclusively highlight the efficacy of inter-individual collaborative learning when contrasted with centralized configurations, both in terms of local and global generalization.

翻译：可穿戴传感器技术的快速发展为个性化和情境感知的人体活动识别领域带来了巨大潜力。鉴于该领域数据源固有的分散特性，利用多智能体系统及其固有的去中心化能力，为开发可扩展、自适应且注重隐私的方法提供了契机。本文提出了一种基于多智能体原理的协同分布式学习方法，其中配备传感器的设备个体用户作为分布式网络中的智能体，共同参与人体活动学习与分类的完整流程。在该方法中，不仅每个个体的活动监测数据隐私得到保护，无需外部服务器监督学习过程，而且系统还展现出克服传统集中式模型局限性、适应每位用户独特属性的潜力。所提方法已在两个公开的人体活动识别数据集（PAMAP2和HARTH）上，针对不同设置进行了实证测试。提供的实证结果明确表明，与集中式配置相比，跨个体协同学习在局部和全局泛化方面均具有显著优势。