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This work devises an optimized machine learning approach for human arm poseestimation from a single smartwatch. Our approach results in a distribution ofpossible wrist and elbow positions, which allows for a measure of uncertaintyand the detection of multiple possible arm posture solutions, i.e., multimodalpose distributions. Combining estimated arm postures with speech recognition,we turn the smartwatch into a ubiquitous, low-cost and versatile robot controlinterface. We demonstrate in two use-cases that this intuitive controlinterface enables users to swiftly intervene in robot behavior, to temporarilyadjust their goal, or to train completely new control policies by imitation.Extensive experiments show that the approach results in a 40% reduction inprediction error over the current state-of-the-art and achieves a mean error of2.56cm for wrist and elbow positions. The code is available athttps://github.com/wearable-motion-capture.

Anytime, Anywhere: Human Arm Pose from Smartwatch Data for Ubiquitous Robot Control and Teleoperation