We demonstrate rich inferences about unaugmented everyday objects and hand object interactions by measuring minute skin surface deformations at the wrist using a sensing technique based on capacitance. The wristband prototype infers muscle and tendon tension, pose, and motion, which we then map to force (9 users, 13.66 +/- 9.84 N regression error on classes 0-49.1 N), grasp (9 users, 81 +/- 7 % classification accuracy on 6 grasps), and continuous interaction (10 users, 99 +/- 1 % discrimination accuracy between 6 interactions, 89-97 % accuracy on 3 states within each interaction) using basic machine learning models. We wrapped these sensing capabilities into a proof-of-concept end-to-end system, Ubiquitous Controls, that enables virtual range inputs by sensing continuous interactions with unaugmented objects. Eight users leveraged our system to control UI widgets (like sliders and dials) with object interactions (like "cutting with scissors'' and "squeezing a ball"). Finally, we discuss the implications and opportunities of using hands as a ubiquitous sensor of our surroundings.
Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography
2022-01-01
Rudolph , J C R , Holman , D , De Araujo , B , Jota , R , Wigdor , D & Savage , V 2022 , Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography . in TEI 2022 - Proceedings of the 16th International Conference on Tangible, Embedded, and Embodied Interaction . , 3501320 , Association for Computing Machinery, Inc , ACM International Conference Proceeding Series , 16th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2022 , Virtual, Online , Korea, Republic of , 13/02/2022 . https://doi.org/10.1145/3490149.3501320
Article (Journal)
Electronic Resource
English
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