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Wearable devices have strict power and memory limitations. As a result, thereis a need to optimize the power consumption on those devices withoutsacrificing the accuracy. This paper presents AdaSense: a sensing, featureextraction and classification co-optimized framework for Human ActivityRecognition. The proposed techniques reduce the power consumption bydynamically switching among different sensor configurations as a function ofthe user activity. The framework selects configurations that represent thepareto-frontier of the accuracy and energy trade-off. AdaSense also useslow-overhead processing and classification methodologies. The introducedapproach achieves 69% reduction in the power consumption of the sensor withless than 1.5% decrease in the activity recognition accuracy.

AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices