A BSN-Enabled Collaborative Edge-Cloud Architecture for Multi-User Activity Recognition

The expansion of Internet if Things (IoT) technology has led to the widespread use of sensors in various everyday environments, including healthcare. Body Sensor Networks (BSNs) enable continuous monitoring of human physiological signals and activities, benefiting healthcare and well-being. However, existing BSN systems primarily focus on single-user activity recognition, disregarding multi-user scenarios. Therefore, this paper introduces a collaborative BSN-based architecture for multi-user activity recognition to identify group collaborations among nearby users. We discuss first the general problem of multi-user activity recognition, the associated challenges along with potential solutions (such as data processing, mining techniques, sensor noise, and the complexity of multi-user activities) and, then, the software abstractions and the components of our architecture. This represents an innovative solution of collective intelligence and it holds significant potential for enhancing healthcare and well-being applications by enabling real-time detection of group activities and behaviors.