VestAid: A Tablet-Based Technology for Objective Exercise Monitoring in Vestibular Rehabilitation.

VORx1 exercises dizziness exercise monitoring telemedicine vestibular rehabilitation

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
15 Dec 2021
Historique:
received: 24 11 2021
revised: 10 12 2021
accepted: 11 12 2021
entrez: 28 12 2021
pubmed: 29 12 2021
medline: 30 12 2021
Statut: epublish

Résumé

(1) Background: Current vestibular rehabilitation therapy is an exercise-based approach aimed at promoting gaze stability, habituating symptoms, and improving balance and walking in patients with mild traumatic brain injury (mTBI). A major component of these exercises is the adaptation of the vestibulo-ocular reflex (VOR) and habituation training. Due to acute injury, the gain of the VOR is usually reduced, resulting in eye movement velocity that is less than head movement velocity. There is a higher chance for the success of the therapy program if the patient (a) understands the exercise procedure, (b) performs the exercises according to the prescribed regimen, (c) reports pre- and post-exercise symptoms and perceived difficulty, and (d) gets feedback on performance. (2) Methods: The development and laboratory evaluation of VestAid, an innovative, low-cost, tablet-based system that helps patients perform vestibulo-ocular reflex (VORx1) exercises correctly at home without therapist guidance, is presented. VestAid uses the tablet camera to automatically assess patient performance and compliance with exercise parameters. The system provides physical therapists (PTs) with near real-time, objective (head speed and gaze fixation compliance), and subjective (perceived difficulty and pre- and post- exercise symptoms) metrics through a web-based provider portal. The accuracy of the head-angle and eye-gaze compliance metrics was evaluated. The accuracy of estimated head angles calculated via VestAid's low-complexity algorithms was compared to the state-of-the-art deep-learning method on a public dataset. The accuracy of VestAid's metric evaluation during the VORx1 exercises was assessed in comparison to the output of an inertial measurement unit (IMU)-based system. (3) Results: There are low mean interpeak time errors (consistently below 0.1 s) across all speeds of the VORx1 exercise, as well as consistently matching numbers of identified peaks. The spatial comparison (after adjusting for the lag measured with the cross-correlation) between the VestAid and IMU-based systems also shows good matching, as shown by the low mean absolute head angle error, in which for all speeds, the mean is less than 10 degrees. (4) Conclusions: The accuracy of the system is sufficient to provide therapists with a good assessment of patient performance. While the VestAid system's head pose evaluation model may not be perfectly accurate as a result of the occluded facial features when the head moves further towards an extreme in pitch and yaw, the head speed measurements and associated compliance measures are sufficiently accurate for monitoring patients' VORx1 exercise compliance and general performance.

Identifiants

pubmed: 34960480
pii: s21248388
doi: 10.3390/s21248388
pmc: PMC8706065
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : United States Army Medical Command
ID : W81XWH-19-C-0006

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Auteurs

Pedram Hovareshti (P)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Shamus Roeder (S)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Lisa S Holt (LS)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Pan Gao (P)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Lemin Xiao (L)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Chad Zalkin (C)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Victoria Ou (V)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Devendra Tolani (D)

NxtHealth Team, Intelligent Automation, Rockville, MD 20855, USA.

Brooke N Klatt (BN)

School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15219, USA.

Susan L Whitney (SL)

School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15219, USA.

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Classifications MeSH