Physical rehabilitation exercises assessment based on Hidden Semi-Markov Model by Kinect v2

M. Capecci, M. G. Ceravolo, F. Ferracuti, S. Iarlori, V. Kyrki, S. Longhi, L. Romeo, F. Verdini

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

18 Citations (Scopus)

Abstract

This work investigates how Hidden Semi-Markov Model (HSMM) can be used to monitor and evaluate physical rehabilitation exercises by Kinect v2 to support medical personnel and patients during rehabilitation at home. Authors developed an exercises assessment method based on the extraction of motion features determined by clinicians. Five different rehabilitation exercises are modeled using a HSMM to provide an assessment score. The scores are compared with those obtained using the Dynamic Time Warping to discriminate which, between these two methods, best correlates doctors and physiotherapists' evaluation. Results show that HSMM can be used to evaluate exercise performances and give a feedback to physiotherapists and patients about exercise execution.

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PublisherIEEE
Pages256-259
Number of pages4
ISBN (Electronic)978-1-5090-2455-1
DOIs
Publication statusPublished - 18 Apr 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Biomedical and Health Informatics - Las Vegas, United States
Duration: 24 Feb 201627 Feb 2016
Conference number: 3

Conference

ConferenceIEEE International Conference on Biomedical and Health Informatics
Abbreviated titleBHI
CountryUnited States
CityLas Vegas
Period24/02/201627/02/2016

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  • Cite this

    Capecci, M., Ceravolo, M. G., Ferracuti, F., Iarlori, S., Kyrki, V., Longhi, S., Romeo, L., & Verdini, F. (2016). Physical rehabilitation exercises assessment based on Hidden Semi-Markov Model by Kinect v2. In 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 (pp. 256-259). [7455883] IEEE. https://doi.org/10.1109/BHI.2016.7455883