Literature DB >> 33807691

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

Octavio Marin-Pardo1, Coralie Phanord2, Miranda Rennie Donnelly2, Christopher M Laine2, Sook-Lei Liew1,2.   

Abstract

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Entities:  

Keywords:  biofeedback; electromyography; human-computer interface; stroke; telerehabilitation

Mesh:

Year:  2021        PMID: 33807691      PMCID: PMC7961888          DOI: 10.3390/s21051806

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  34 in total

1.  Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates.

Authors:  J R Potvin; S H M Brown
Journal:  J Electromyogr Kinesiol       Date:  2004-06       Impact factor: 2.368

2.  Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography.

Authors:  Tjeerd W Boonstra; Michael Breakspear
Journal:  J Neurophysiol       Date:  2011-11-09       Impact factor: 2.714

3.  Cortical entrainment of human hypoglossal motor unit activities.

Authors:  Christopher M Laine; Laura A Nickerson; E Fiona Bailey
Journal:  J Neurophysiol       Date:  2011-11-02       Impact factor: 2.714

4.  "Look, Your Muscles Are Firing!": A Qualitative Study of Clinician Perspectives on the Use of Surface Electromyography in Neurorehabilitation.

Authors:  Heather A Feldner; Darrin Howell; Valerie E Kelly; Sarah Westcott McCoy; Katherine M Steele
Journal:  Arch Phys Med Rehabil       Date:  2018-10-28       Impact factor: 3.966

5.  Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association.

Authors:  Salim S Virani; Alvaro Alonso; Emelia J Benjamin; Marcio S Bittencourt; Clifton W Callaway; April P Carson; Alanna M Chamberlain; Alexander R Chang; Susan Cheng; Francesca N Delling; Luc Djousse; Mitchell S V Elkind; Jane F Ferguson; Myriam Fornage; Sadiya S Khan; Brett M Kissela; Kristen L Knutson; Tak W Kwan; Daniel T Lackland; Tené T Lewis; Judith H Lichtman; Chris T Longenecker; Matthew Shane Loop; Pamela L Lutsey; Seth S Martin; Kunihiro Matsushita; Andrew E Moran; Michael E Mussolino; Amanda Marma Perak; Wayne D Rosamond; Gregory A Roth; Uchechukwu K A Sampson; Gary M Satou; Emily B Schroeder; Svati H Shah; Christina M Shay; Nicole L Spartano; Andrew Stokes; David L Tirschwell; Lisa B VanWagner; Connie W Tsao
Journal:  Circulation       Date:  2020-01-29       Impact factor: 29.690

6.  Observation of amounts of movement practice provided during stroke rehabilitation.

Authors:  Catherine E Lang; Jillian R Macdonald; Darcy S Reisman; Lara Boyd; Teresa Jacobson Kimberley; Sheila M Schindler-Ivens; T George Hornby; Sandy A Ross; Patricia L Scheets
Journal:  Arch Phys Med Rehabil       Date:  2009-10       Impact factor: 3.966

Review 7.  What is the evidence for physical therapy poststroke? A systematic review and meta-analysis.

Authors:  Janne Marieke Veerbeek; Erwin van Wegen; Roland van Peppen; Philip Jan van der Wees; Erik Hendriks; Marc Rietberg; Gert Kwakkel
Journal:  PLoS One       Date:  2014-02-04       Impact factor: 3.240

8.  Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients.

Authors:  Athanasios Vourvopoulos; Octavio Marin Pardo; Stéphanie Lefebvre; Meghan Neureither; David Saldana; Esther Jahng; Sook-Lei Liew
Journal:  Front Hum Neurosci       Date:  2019-06-19       Impact factor: 3.169

9.  "It's All Sort of Cool and Interesting…but What Do I Do With It?" A Qualitative Study of Stroke Survivors' Perceptions of Surface Electromyography.

Authors:  Heather A Feldner; Christina Papazian; Keshia Peters; Katherine M Steele
Journal:  Front Neurol       Date:  2020-09-17       Impact factor: 4.003

10.  Latent Factors Limiting the Performance of sEMG-Interfaces.

Authors:  Sergey Lobov; Nadia Krilova; Innokentiy Kastalskiy; Victor Kazantsev; Valeri A Makarov
Journal:  Sensors (Basel)       Date:  2018-04-06       Impact factor: 3.576

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  1 in total

1.  Myoelectric interface training enables targeted reduction in abnormal muscle co-activation.

Authors:  Marc W Slutzky; Jinsook Roh; Gang Seo; Ameen Kishta; Emily Mugler
Journal:  J Neuroeng Rehabil       Date:  2022-07-01       Impact factor: 5.208

  1 in total

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