A framework of energy efficient mobile sensing for automatic user state recognition

TLDR

Urban and participatory sensing can enrich mobile applications, yet continuous data capture on mobile devices consumes substantial energy. The paper proposes a novel framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS employs a hierarchical sensor management strategy that activates only a minimal set of sensors with duty cycles, and is implemented and evaluated on an off‑the‑shelf high‑end smartphone to recognize daily activities in real time. EEMSS increases device battery life by over 75% while maintaining high accuracy and low latency in detecting activity transitions, as shown in a one‑week evaluation with ten users.

Abstract

Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we present a novel design framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and using appropriate sensor duty cycles EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.