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

arXiv: https://arxiv.org/abs/2006.05884 · HTML: https://mlsanity.com/paper/2006.05884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild](https://mlsanity.com/paper/2512.06721/) (2025)

## Abstract (arXiv)

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.