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

NCT05813613 | Completed

• 1 other identifier: AI in Prediciting Fatigue
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Fatigue

Keywords

Electromyography; Fatigue; Squats; Borg scale; Musculoskeletal disorders

Outcome Measures

Primary Outcomes (2)

• Surface electromyography

  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

  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 Outcomes (2)

• Chest Expansion.

  During the squatting exercise.

• Range of motion.

  During the squatting exercise.

Study Arms (2)

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.

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.

Interventions

Squatting with the aid of Kynapsis Virtual Training apparatus. OTHER

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.

Healthy Group; Post Covid-19 Group

Eligibility Criteria

• Age: 18 Years - 49 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Beirut Arab University: lead

Study Sites (1)

Ahmad ElMelhat

Beirut, Lebanon

Location

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MeSH Terms

Conditions

Fatigue; Musculoskeletal Diseases

Condition Hierarchy (Ancestors)

Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate. Prof. Rami Abbas

Study Record Dates

• First Submitted: April 3, 2023
• First Posted: April 14, 2023
• Study Start: April 15, 2023
• Primary Completion: June 1, 2023
• Study Completion: June 7, 2023
• Last Updated: June 9, 2023

Record last verified: 2023-06

Locations

LE (1)