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dc.contributor.authorKyu, Alexander
dc.contributor.authorMao, Hongyu
dc.contributor.authorZhu, Junyi
dc.contributor.authorGoel, Mayank
dc.contributor.authorAhuja, Karan
dc.date.accessioned2024-06-04T19:32:07Z
dc.date.available2024-06-04T19:32:07Z
dc.date.issued2024-05-11
dc.identifier.isbn979-8-4007-0330-0
dc.identifier.urihttps://hdl.handle.net/1721.1/155185
dc.descriptionCHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems May 11–16, 2024, Honolulu, HI, USAen_US
dc.description.abstractReal-time hand pose estimation has a wide range of applications spanning gaming, robotics, and human-computer interaction. In this paper, we introduce EITPose, a wrist-worn, continuous 3D hand pose estimation approach that uses eight electrodes positioned around the forearm to model its interior impedance distribution during pose articulation. Unlike wrist-worn systems relying on cameras, EITPose has a slim profile (12 mm thick sensing strap) and is power-efficient (consuming only 0.3 W of power), making it an excellent candidate for integration into consumer electronic devices. In a user study involving 22 participants, EITPose achieves with a within-session mean per joint positional error of 11.06 mm. Its camera-free design prioritizes user privacy, yet it maintains cross-session and cross-user accuracy levels comparable to camera-based wrist-worn systems, thus making EITPose a promising technology for practical hand pose estimation.en_US
dc.publisherACMen_US
dc.relation.isversionof10.1145/3613904.3642663en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleEITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimationen_US
dc.typeArticleen_US
dc.identifier.citationKyu, Alexander, Mao, Hongyu, Zhu, Junyi, Goel, Mayank and Ahuja, Karan. 2024. "EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2024-06-01T07:52:36Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2024-06-01T07:52:36Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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