Change detection technique for muscle tone during static stretching by continuous muscle viscoelasticity monitoring using wearable indentation tester

Naomi Okamura, Yo Kobayashi, Shigeki Sugano, Masakatsu G. Fujie

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

1 Citation (Scopus)

Abstract

Static stretching is widely performed to decrease muscle tone as a part of rehabilitation protocols. Finding out the optimal duration of static stretching is important to minimize the time required for rehabilitation therapy and it would be helpful for maintaining the patient's motivation towards daily rehabilitation tasks. Several studies have been conducted for the evaluation of static stretching; however, the recommended duration of static stretching varies widely between 15-30 s in general, because the traditional methods for the assessment of muscle tone do not monitor the continuous change in the target muscle's state. We have developed a method to monitor the viscoelasticity of one muscle continuously during static stretching, using a wearable indentation tester. In this study, we investigated a suitable signal processing method to detect the time required to change the muscle tone, utilizing the data collected using a wearable indentation tester. By calculating a viscoelastic index with a certain time window, we confirmed that the stretching duration required to bring about a decrease in muscle tone could be obtained with an accuracy in the order of 1 s.

Original languageEnglish
Title of host publication2017 International Conference on Rehabilitation Robotics, ICORR 2017
EditorsArash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang
PublisherIEEE Computer Society
Pages1686-1691
Number of pages6
ISBN (Electronic)9781538622964
DOIs
Publication statusPublished - 2017 Aug 11
Event2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom
Duration: 2017 Jul 172017 Jul 20

Publication series

NameIEEE International Conference on Rehabilitation Robotics
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Other

Other2017 International Conference on Rehabilitation Robotics, ICORR 2017
CountryUnited Kingdom
CityLondon
Period17/7/1717/7/20

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Rehabilitation
  • Electrical and Electronic Engineering

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