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

TL;DR

AdaSense is a framework that dynamically optimizes sensor configurations on wearable devices to significantly reduce power consumption while maintaining high activity recognition accuracy.

Contribution

It introduces a co-optimized sensing, feature extraction, and classification framework that adaptively balances energy use and accuracy in wearable activity recognition.

Findings

Achieves 69% reduction in sensor power consumption

Maintains less than 1.5% decrease in recognition accuracy

Uses low-overhead processing and adaptive sensor configurations

Abstract

Wearable devices have strict power and memory limitations. As a result, there is a need to optimize the power consumption on those devices without sacrificing the accuracy. This paper presents AdaSense: a sensing, feature extraction and classification co-optimized framework for Human Activity Recognition. The proposed techniques reduce the power consumption by dynamically switching among different sensor configurations as a function of the user activity. The framework selects configurations that represent the pareto-frontier of the accuracy and energy trade-off. AdaSense also uses low-overhead processing and classification methodologies. The introduced approach achieves 69% reduction in the power consumption of the sensor with less than 1.5% decrease in the activity recognition accuracy.