Eco-Friendly Sensing for Human Activity Recognition

TL;DR

This paper introduces an energy-harvesting, eco-friendly human activity recognition system using photovoltaic sensors and deep learning, achieving high accuracy in diverse environments without consuming sensing energy.

Contribution

The novel system combines photovoltaic energy harvesting with deep transformer models for energy-free activity recognition, advancing sustainable IoT applications.

Findings

Achieved 91.7% activity recognition accuracy.

Operates without sensing energy, harvesting energy instead.

Robust performance across various conditions.

Abstract

With the increasing number of IoT devices, there is a growing demand for energy-free sensors. Human activity recognition holds immense value in numerous daily healthcare applications. However, the majority of current sensing modalities consume energy, thus limiting their sustainable adoption. In this paper, we present a novel activity recognition system that not only operates without requiring energy for sensing but also harvests energy. Our proposed system utilizes photovoltaic cells, attached to the wrist and shoes, as eco-friendly sensing devices for activity recognition. By capturing photovoltaic readings and employing a deep transformer model with powerful learning capabilities, the system effectively recognizes user activities. To ensure robust performance across various subjects, time periods, and lighting conditions, the system incorporates feature extraction and different processing modules. The evaluation of the proposed system on realistic indoor and outdoor environments demonstrated its ability to recognize activities with an accuracy of 91.7%.