Abstract
In this paper, we present a distributed action recognition framework 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. A boosting approach is employed to enhance accuracy by activating only a subset of sensors optimized in terms of power consumption and can achieve a given lower bound accuracy criterion. Our experimental results on real data shows more than 85% power saving while maintaining 80% sensitivity to detected actions.
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