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Creators/Authors contains: "Huang, Tianda"

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  1. ; ; (, IEEE)
    Wearable IoT devices rely on batteries, which pose challenges for long-term sustainable health monitoring due to the need for recharging or replacement. Batteryless sensing approaches, which harvest energy from the environment, offer an appealing alternative. However, given the discontinuous supply of harvested energy, it is unclear how to leverage sparse, asynchronous data from batteryless sensors for machine learning (ML) tasks such as human activity recognition (HAR). To this end, we present and profile a prototype of a system to simulate data acquisition from a set of kinetic energy harvesting devices. Our results demonstrate that there is a need to jointly optimize (1) when sensors should spend energy to communicate data, and (2) the training of the ML model that will receive the data. 
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