SolarTrack: Exploring the Continuous Tracking Capabilities of Wearable Solar Harvesters

Continuous tracking is often thought to require specialised, actively powered sensors. Yet energy harvesters already embedded in commercial devices, such as Garmin solar-powered smartwatches, generate energy signals that inherently carry continuous variations linked to user motion and environment. Prior studies have shown that these signals are sufficient for classification tasks such as human activity and gesture recognition by exploiting class-distinguishing cues. However, whether they can support continuous trajectory tracking has remained an open question-until now.In this paper, we present the first fundamental study of continuous hand trajectory tracking with wearable solar harvesters. Through a novel radiometric model, we analytically link photovoltaic (PV) power to the solar cell’s geometric configuration, exposing both the promise of energy signals for tracking and their core limitations: the positional ambiguities and distortions that arise when power is used directly for positioning. To resolve this ambiguity, we propose SolarTrack, a framework that embeds a radiometric model as a physical backbone within a sequence-learning pipeline, enforcing cycle consistency between data-driven predictions and physical feasibility. This yields the first standalone solar-based tracker capable of estimating continuous hand motion directly from harvested energy signals.To validate this, we built a wearable prototype with a solar panel and IMU and collected the first dataset pairing motion-capture ground truth with harvested energy from 15 participants (700k samples). Results show that solar signals alone achieve subdecimeter tracking accuracy, outperforming purely data-driven baselines and only 1.6 cm worse compared to the IMU tracker despite having access to only 1D power signal. Furthermore, when fused with IMU, it further boosts IMU performance by 13%.