Sensor-Classifier Co-Optimization for Wearable Human Activity Recognition Applications
| Autor: | Jaehyun Park, Hyung Gyu Lee, Ganapati Bhat, Anish Nk, Umit Y. Ogras |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Real-time computing Wearable computer 020206 networking & telecommunications 02 engineering and technology Energy consumption Accelerometer 01 natural sciences 0104 chemical sciences Activity recognition 0202 electrical engineering electronic engineering information engineering Electronics business Classifier (UML) Wearable technology Efficient energy use |
| Zdroj: | ICESS |
| DOI: | 10.1109/icess.2019.8782506 |
| Popis: | Advances in integrated sensors and low-power electronics have led to an increase in the use of wearable devices for health and activity monitoring applications. These devices have severe limitations on weight, form-factor, and battery size since they have to be comfortable to wear. Therefore, they must minimize the total platform energy consumption while satisfying functionality (e.g., accuracy) and performance requirements. Optimizing the platform-level energy efficiency requires considering both the sensor and processing subsystems. To this end, this paper presents a sensor-classifier co-optimization technique with human activity recognition as a driver application. The proposed technique dynamically powers down the accelerometer sensors and controls their sampling rate as a function of the user activity. It leads to a 49% reduction in total platform energy consumption with less than 1% decrease in activity recognition accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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