Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices.

Automated summary

The study proposes an optimization framework for energy allocation in wearable devices to maximize human activity recognition accuracy, with offline and online algorithms provided. Experimental results demonstrate that the proposed energy allocation approach can enhance energy allocation utility and improve energy efficiency of wearable devices.