Distributed Agent-Based Collaborative Learning in Cross-Individual
  Wearable Sensor-Based Human Activity Recognition
Ahmad Esmaeili Zahra Ghorrati Eric T. Matson
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.