Ignite Creation Date:
2024-05-06 @ 6:52 PM
Last Modification Date:
2024-10-26 @ 2:56 PM
Study NCT ID:
NCT05813613
Status:
COMPLETED
Last Update Posted:
2023-06-09
First Post:
2023-04-03
Brief Title:
Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training
Sponsor:
Beirut Arab University
Organization:
Beirut Arab University
Study Overview
Official Title:
Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training In Healthy And Post COVID19 Subjects
Status:
COMPLETED
Status Verified Date:
2023-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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
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 25cm 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
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 25cm 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None