Abstract

Therapists and trainers often utilize training and exercise programs as a critical step within the rehabilitation process. Due to the inherent danger of injury while performing these exercises, they must often be performed under the supervision of skilled trainers to observe and provide feedback on body compliance during the motion. To address this safety risk, we’ve developed the Smart-Gym: an intelligent modification to the Total Gym Pro that monitors a user’s body during exercise and provides feedback to anticipate non-compliance. The system was built to provide multimodal feedback for various body adjustments in the same way that a trainer would. Results from an initial case study involving an individual undergoing exercise rehabilitation for hemiparetic cerebral palsy are discussed.

Original languageEnglish (US)
Title of host publicationHealthMedia 2018 - Proceedings of the 3rd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2018
PublisherAssociation for Computing Machinery, Inc
Pages67-71
Number of pages5
ISBN (Electronic)9781450359825
DOIs
StatePublished - Oct 15 2018
Event3rd International Workshop for Multimedia for Personal Health and Health Care, HealthMedia 2018, in conjunction with ACM Multimedia, MM 2018 - Seoul, Korea, Republic of
Duration: Oct 22 2018 → …

Other

Other3rd International Workshop for Multimedia for Personal Health and Health Care, HealthMedia 2018, in conjunction with ACM Multimedia, MM 2018
CountryKorea, Republic of
CitySeoul
Period10/22/18 → …

Keywords

  • Anticipatory computing
  • Exercise compliance
  • Motion capture
  • Mul-timodal feedback

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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    Tadayon, A., McDaniel, T., & Panchanathan, S. (2018). Functional case study evaluation of the smartgym: An anticipatory system to detect body compliance. In HealthMedia 2018 - Proceedings of the 3rd International Workshop on Multimedia for Personal Health and Health Care, co-located with MM 2018 (pp. 67-71). Association for Computing Machinery, Inc. https://doi.org/10.1145/3264996.3265004