FusedAR: Energy-Positive Human Activity Recognition Using Kinetic and Solar Signal Fusion

Abstract

Today's wearable Internet of Things (IoT) devices, which have numerous practical applications, suffer from the limited lifetime of batteries due to the high power consumption of conventional inertial activity sensors. Recently, kinetic energy harvesters have been employed as a source of energy as well as context information to replace conventional activity sensors. However, the harvested power from human movements using miniaturized kinetic transducers may not be sufficient to enable a perpetual and self-powered activity recognition system. In this article, we propose a novel mechanism of fused signal-based human activity recognition (FusedAR), which employs miniaturized wearable solar and kinetic energy harvesters simultaneously as an energy source as well as an activity sensor. As human activities engender distinct movement patterns and interact and interfere with the ambient light differently, the kinetic and solar energy harvesting (SEH) signals incorporate unique information about the underlying activities while generating sufficient power. After detailed experiments, we find that the FusedAR, which employs both solar and kinetic energy signals, achieves superior activity recognition performance by up to 10%, particularly in outdoor and night-time contexts, and can recognize not only activities but also contexts, compared with the individual energy harvesting signals. Furthermore, our analysis demonstrates that FusedAR, in addition to significant energy generation, consumes up to 22% less power than the highly optimized conventional three-axis accelerometer-based mechanisms, achieving energy-positive human activity recognition (HAR) leading toward perpetual, uninterrupted, and autonomous operation of wearable IoT devices.