Lightweight human activity recognition system for resource constrained environments

Abstract

As the elderly population in need of assisted living arrangements continues to grow, the imperative to ensure their safety is paramount. Though effective, traditional surveillance methods, notably RGB cameras, raise significant privacy concerns. This paper highlights the advantages of a surveillance system addressing these issues by utilizing skeleton joint sequences extracted from depth data. The focus on non-intrusive parameters aims to mitigate ethical and privacy concerns. Moreover, the proposed work prioritizes resource efficiency, acknowledging the often limited computing resources in assisted living environments. We strive for a method that can run efficiently even in the most resource-constrained environments. Performance evaluation and a prototypical implementation of our method on a resource-constraint device confirm the efficacy and suitability of the proposed method in real-world applications.