Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training

June 8, 2023 updated by: Rami Abbas, Beirut Arab University

Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training In Healthy And Post COVID19 Subjects

The goal of this observational predicted study is to predict muscle fatigue using a specific AI algorithm in healthy vs post Covid-19 infected individuals. The main question it aims to answer is:

Can Artificial Intelligence be used as a reliable source of predicting localized muscle fatigue in healthy vs post Covid-19 infected individuals?

Participants will be divided into two groups: A healthy group and a post Covid-19 group.

  • Each group will undergo a familiarization process before the start of the exercises.
  • Then, each group will perform squatting exercises guided by the kynpasis virtual reality apparatus.
  • sEMG for the vastus lateralis and rectus femories, chest expansion, and goniometric measurements of the knee will be taken during different reported fatigue levels using the Biopac system.
  • Groups will continue squatting while recording their subjective fatigue levels using the Borg scale.
  • Data will then be run through machine learning processes to produce an AI algorithm capable of predicting isolated muscle fatigue.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Participants were divided into two groups, one consisting of healthy individuals and another consisting of Covid-19 subjects. Both groups received a familiarization training for the exercise to be performed with 15 minutes of rest afterwards, before the start of the data collection.

Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.

Additional variables were considered, including chest expansion, and the range of motion using an electric goniometer, all being measured and recorded using the Biopac (BIOPAC Systems, Inc., Santa Barbara, CA) that, according to evidence, possess a high-pass frequency filter and bipolar electrode system.

The muscles tested are the 3 heads of the QF muscle RF, VM, and VL. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.

The Borg (C-10) scale was explained to the participants and was present in front of them while performing the exercise as an outcome measure to assess the subjective muscle fatigue that once reached will end the exercise.

Study Type

Observational

Enrollment (Actual)

90

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Lebanon
      • Beirut, Lebanon
        • Ahmad ElMelhat

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

The study population consisted of two groups.

  • Non-athletic healthy individuals that did not perform any intense activities for the past 3 days and had not contacted Covid-19 previously.
  • Non-athletic healthy individuals that did not perform any intense activities for the past 3 days but have a confirmed positive PCR test done within a 1 year interval.

Description

Inclusion Criteria:

  • Non-athletic healthy individuals.
  • Avoided intense activities in the past 3 days.
  • Confirmed positive PCR test done within an interval of 1 year for Covid-19 group subjects.

Exclusion Criteria:

  • Being old age geriatrics (more than 50 years old).
  • Having any respiratory, cardiac, renal, neuromuscular, orthopedic, and musculoskeletal disorders.
  • Smokers and some medicinal drug users must be taken into consideration because it affects the performance and increases the fatigue levels.
  • Subjects not meeting any of the inclusion criteria.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Healthy Group
  • Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached
  • Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac.
Other: Squatting with the aid of Kynapsis Virtual Training apparatus.
Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.
Post Covid-19 Group
  • Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached
  • Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac.
Other: Squatting with the aid of Kynapsis Virtual Training apparatus.
Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Surface electromyography
Time Frame: During the squatting exercise.
non-invasive technique where electrodes were placed on the vastus lateralis and rectus femoris heads of the quadriceps femoris muscle, assessing it's myoelectric output. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.
During the squatting exercise.
The Borg Rating of Perceived Exertion (RPE) scale
Time Frame: During the squatting exercise.
A tool for measuring an individual's effort and exertion, breathlessness and fatigue during physical work and so is highly relevant for occupational health and safety practice. It ranges from 6 as a minimum to 20 as a maximum with 6 signifying no exertion and 20 signifying extreme maximal exertion
During the squatting exercise.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Chest Expansion.
Time Frame: During the squatting exercise.
Using a respiration transducer wrapped around the subject's chest using a velcro strap that transmits expansion data to the main receiver module of the Biopac, that will be recorded on the computer.
During the squatting exercise.
Range of motion.
Time Frame: During the squatting exercise.
Using an electric goniometer wired on the subject's knee that will transmit signals of range of motion to the receiver module of the Biopac that will be recorded on the computer.
During the squatting exercise.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Beirut Arab University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 15, 2023

Primary Completion (Actual)

June 1, 2023

Study Completion (Actual)

June 7, 2023

Study Registration Dates

First Submitted

April 3, 2023

First Submitted That Met QC Criteria

April 13, 2023

First Posted (Actual)

April 14, 2023

Study Record Updates

Last Update Posted (Actual)

June 9, 2023

Last Update Submitted That Met QC Criteria

June 8, 2023

Last Verified

June 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI in Prediciting Fatigue

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Fatigue

Clinical Trials on Squatting with the aid of Kynapsis Virtual Training apparatus.

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Djibouti

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe