An Experimental Study on the Energy Efficiency of Feature Selection for Human Activity Recognition with Wrist-worn Devices

Incorporating machine and deep learning methodologies into wearable devices has enhanced the capacity to accurately recognize human activity, thus enabling a range of applications including healthcare monitoring and fitness tracking. However, machine and deep learning can be costly in terms of the computational resources and energy consumption required. In this work, we study how a feature selection decision impacts the energy consumption of an ESP32 wearable device by evaluating the best trade-off between classification performance and energy expenditure. Experimental results, conducted on publicly available datasets, demonstrate that the best trade-off between energy consumption and accuracy is reached by selecting between 20 and 25 features, with an accuracy ranging between 73.56% and 87.44%, and an energy consumption between 2340.945 μJ and 3759.270 μJ.