Low-power lessons from designing a wearable logger for long-term deployments

The advent of a range of wearable products for monitoring one's healthcare and fitness has pushed decades of research into the market over the past years. These units record motion and detect common physical activities to assist the wearer in monitoring fitness, general state of health, and sleeping trends. Most of the detection algorithms on board of these devices however are closed-source and the devices do not allow the recording of raw inertial data. This paper presents a project that, faced by these limitations of commercial wearable products, set out to create an open-source recording platform for activity recognition research that (1) is sufficiently power-efficient, and (2) remains small and comfortable enough to wear, to be able to record raw inertial data for extended periods of time. We study especially, via high-resolution power profiling, several trade-offs present in the choice for the basic hardware components of our prototype, and contribute with three key design areas that have had a significant impact on our prototype design.