Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography. / Rudolph, Julius Cosmo Romeo; Holman, David; De Araujo, Bruno; Jota, Ricardo; Wigdor, Daniel; Savage, Valkyrie.
TEI 2022 - Proceedings of the 16th International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, Inc, 2022. 3501320 (ACM International Conference Proceeding Series).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography
AU - Rudolph, Julius Cosmo Romeo
AU - Holman, David
AU - De Araujo, Bruno
AU - Jota, Ricardo
AU - Wigdor, Daniel
AU - Savage, Valkyrie
N1 - Publisher Copyright: © 2022 ACM.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - affordances
KW - capacitive sensing
KW - everyday objects
KW - wrist topography
KW - wristband
UR - http://www.scopus.com/inward/record.url?scp=85124935190&partnerID=8YFLogxK
U2 - 10.1145/3490149.3501320
DO - 10.1145/3490149.3501320
M3 - Article in proceedings
AN - SCOPUS:85124935190
T3 - ACM International Conference Proceeding Series
BT - TEI 2022 - Proceedings of the 16th International Conference on Tangible, Embedded, and Embodied Interaction
PB - Association for Computing Machinery, Inc
T2 - 16th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2022
Y2 - 13 February 2022 through 16 February 2022
ER -
ID: 300918096