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Predicting Grasps with a Wearable Inertial and EMG Sensing Unit for Low-Power Detection of In-Hand Objects

Detecting the task at hand can often be improved when it is also known what object the user is holding. Several sensing modalities have been suggested to identify handheld objects, from wrist-worn RFID readers to cameras. A critical obstacle to using such sensors, however, is that they tend to be too power hungry for continuous usage. This paper proposes a system that detects grasping using first inertial sensors and then Electromyography (EMG) on the forearm, to then selectively activate the object identification sensors. This three-tiered approach would therefore only attempt to identify in-hand objects once it is known a grasping has occurred. Our experiments show that high recall can be obtained for grasp detection, 95% on average across participants, with the grasping of lighter and smaller objects clearly being more difficult.