Motion parameterization and adaptation strategies for virtual therapists

Carlo Camporesi, Anthony Popelar, Marcelo Kallmann, Jay Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We propose in this paper new techniques for correction and parameterization of motion capture sequences containing upper-body exercises for physical therapy. By relying on motion capture sequences we allow therapists to easily record new patient-customized exercises intuitively by direct demonstration. The proposed correction and parameterization techniques allow the modification of recorded sequences in order to 1) correct and modify properties such as alignments and constraints, 2) customize prescribed exercises by modifying parameterized properties such as speed, wait times and exercise amplitudes, and 3) to achieve real-time adaptation by monitoring user performances and updating the parameters of each exercise for improving the therapy delivery. The proposed techniques allow autonomous virtual therapists to improve the whole therapy process, from exercise definition to delivery.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages99-108
Number of pages10
Volume8637 LNAI
ISBN (Print)9783319097664
DOIs
Publication statusPublished - 2014
Event14th International Conference on Intelligent Virtual Agents, IVA 2014 - Boston, MA, United States
Duration: Aug 27 2014Aug 29 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8637 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th International Conference on Intelligent Virtual Agents, IVA 2014
CountryUnited States
CityBoston, MA
Period8/27/148/29/14

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Keywords

  • motion capture
  • virtual humans
  • virtual therapists

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Camporesi, C., Popelar, A., Kallmann, M., & Han, J. (2014). Motion parameterization and adaptation strategies for virtual therapists. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8637 LNAI, pp. 99-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8637 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-09767-1_13