Power-Aware Activity Monitoring Using Distributed Wearable Sensors

Monitoring human movements using wireless wearable sensors finds applications in a variety of domains including healthcare and wellness. In these systems, sensory devices are tightly integrated with the human body and infer status of the user through signal and information processing. Typically, highly accurate observations can be made at the cost of deploying a sufficiently large number of sensors, which in turn results in increased energy consumption of the system and reduced adherence to using the system. Therefore, optimizing power consumption of the system while maintaining acceptable accuracy plays a crucial role in realizing these stringent resource constraint systems. In this paper, we present an activity monitoring approach that minimizes power consumption of the system subject to a lower bound on the classification accuracy. The system utilizes computationally simple template-matching blocks that perform classifications on individual sensor nodes. The system further employs a boosting approach to enhance accuracy of the distributed classifier by selecting a subset of sensors optimized in terms of power consumption and capable of achieving a given lower bound accuracy criterion. A proof-of-concept evaluation with three participants performing 14 transitional actions was conducted, where collected signals were segmented and labeled manually for each action. The results indicated that the proposed approach provides more than a 65% reduction in the power consumption of the signal processing, while maintaining 80% sensitivity in classifying human movements.