2026-09-18T23:23:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1584782025-04-07T08:58:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Matusik, Wojciech
Palacios, Tomás
Luo, Yiyue
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-03-12T16:54:39Z
2025-03-12T16:54:39Z
2024-09
2025-03-04T18:32:18.363Z
https://hdl.handle.net/1721.1/158478
Human-environment interaction is a fundamental aspect of our daily lives, involving the constant use of our sensory and motor systems to extract, process, and communicate information. However, capturing, analyzing, and reproducing these interactions pose significant challenges due to their pervasive, variable, and prolonged nature, as well as their unique character for each individual. Despite these challenges, it is essential to develop systems that can accurately capture and reproduce human-environment interactions for a wide range of applications, including human behavior studies, health monitoring, human-computer interactions, and robot imitation learning. This thesis focuses on developing seamlessly integrated, scalable manufactured sensing and actuating systems, as well as advanced computational pipelines to capture, analyze, and reproduce adaptive ubiquitous physical human-environment interactions.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Intelligent Textiles for Physical Interactions
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>Human-environment interaction is a fundamental aspect of our daily lives, involving the constant use of our sensory and motor systems to extract, process, and communicate information. However, capturing, analyzing, and reproducing these interactions pose significant challenges due to their pervasive, variable, and prolonged nature, as well as their unique character for each individual. Despite these challenges, it is essential to develop systems that can accurately capture and reproduce human-environment interactions for a wide range of applications, including human behavior studies, health monitoring, human-computer interactions, and robot imitation learning. This thesis focuses on developing seamlessly integrated, scalable manufactured sensing and actuating systems, as well as advanced computational pipelines to capture, analyze, and reproduce adaptive ubiquitous physical human-environment interactions.</Abstract>
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