Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference
UbiComp Companion ’24, October 5–9, 2024, Melbourne, VIC, Australia
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing
2024
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| Online Access: | https://hdl.handle.net/1721.1/157621 |
| _version_ | 1824457905976377344 |
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| author | Seong, Minwoo Kim, Gwangbin Lee, Jaehee DelPreto, Joseph Matusik, Wojciech Rus, Daniela Kim, SeungJun |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Seong, Minwoo Kim, Gwangbin Lee, Jaehee DelPreto, Joseph Matusik, Wojciech Rus, Daniela Kim, SeungJun |
| author_sort | Seong, Minwoo |
| collection | MIT |
| description | UbiComp Companion ’24, October 5–9, 2024, Melbourne, VIC, Australia |
| first_indexed | 2025-02-19T04:17:26Z |
| format | Article |
| id | mit-1721.1/157621 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:17:26Z |
| publishDate | 2024 |
| publisher | ACM|Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
| record_format | dspace |
| spelling | mit-1721.1/1576212024-12-23T06:11:04Z Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference Seong, Minwoo Kim, Gwangbin Lee, Jaehee DelPreto, Joseph Matusik, Wojciech Rus, Daniela Kim, SeungJun Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory UbiComp Companion ’24, October 5–9, 2024, Melbourne, VIC, Australia Owing to people spending a large portion of their day sitting while working, commuting, or relaxing, monitoring their sitting posture is crucial for the development of adaptive interventions that respond to the user's pose, state, and behavior. This is because posture is closely linked to actions, health, attention, and engagement levels. The existing systems for posture estimation primarily use computer vision-based measurements or body-attached sensors; however, they are plagued by challenges such as privacy concerns, occlusion issues, and user discomfort. To address these drawbacks, this study proposed a posture-inference system that uses high-density piezoresistive sensors for joint reconstruction. Tactile pressure data were collected from six individuals, each performing seven different postures 20 times. The proposed system achieved an average L2 distance of 20.2 cm in the joint position reconstruction with a posture classification accuracy of 96.3%. Future research will focus on the development of a system capable of providing real-time feedback to help users maintain the correct sitting posture. 2024-11-20T22:01:53Z 2024-11-20T22:01:53Z 2024-10-05 2024-11-01T07:52:31Z Article http://purl.org/eprint/type/JournalArticle 979-8-4007-1058-2 https://hdl.handle.net/1721.1/157621 Seong, Minwoo, Kim, Gwangbin, Lee, Jaehee, DelPreto, Joseph, Matusik, Wojciech et al. 2024. "Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference." PUBLISHER_CC en https://doi.org/10.1145/3675094.3678374 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The author(s) application/pdf ACM|Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing Association for Computing Machinery |
| spellingShingle | Seong, Minwoo Kim, Gwangbin Lee, Jaehee DelPreto, Joseph Matusik, Wojciech Rus, Daniela Kim, SeungJun Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title | Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title_full | Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title_fullStr | Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title_full_unstemmed | Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title_short | Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference |
| title_sort | intelligent seat tactile signal based 3d sitting pose inference |
| url | https://hdl.handle.net/1721.1/157621 |
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