KineticWear: Battery-Free On-Device Human Activity Recognition for Wearables

Wearable devices are becoming more prevalent in people’s daily lives, particularly in applications such as activity recognition, health monitoring and fitness management. However, the majority of existing wearable devices remain heavily dependent on battery-based power sources, which introduces several practical and sustainability challenges. Frequent battery replacement or recharging imposes inconvenience on users, increases long-term operational costs, and contributes to environmental concerns associated with battery disposal and resource consumption. To address these issues, we present KineticWear, the first battery-free wearable system that utilises kinetic energy harvested from human activities both as the sole energy source and as a sensing signal for on-device human activity recognition (HAR). Based on a careful end-to-end design of all hardware and software components, KineticWear achieves real-time HAR on an ultra low-power microcontroller unit (MCU) including on-board classification and transmission of the inferred activity over a wireless link. Using empirical data, we find that decision tree (DT) and convolutional neural network (CNN) models offer activity recognition accuracies of 87 % and 99.5 % respectively. Systematic real-world experiments demonstrate that KineticWear harvests sufficient energy to operate the wearable device up to 95.2 % of the time, and that the device can infer and report an ongoing activity within 8 seconds using DT classification algorithm, taking three orders of magnitude shorter classification time than CNN. Thus, KineticWear offers significantly enhanced performance compared to state-of-the-art off-device activity recognition systems powered by kinetic energy harvesting.