Recognizing Full-Body Exercise Execution Errors Using the Teslasuit

Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermor...

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Published inSensors (Basel, Switzerland) Vol. 21; no. 24; p. 8389
Main Authors Caserman, Polona, Krug, Clemens, Göbel, Stefan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 15.12.2021
MDPI
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ISSN1424-8220
1424-8220
DOI10.3390/s21248389

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Abstract Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.
AbstractList Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.
Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.
Author Krug, Clemens
Caserman, Polona
Göbel, Stefan
AuthorAffiliation Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany; clemens.krug@stud.tu-darmstadt.de (C.K.); stefan_peter.goebel@tu-darmstadt.de (S.G.)
AuthorAffiliation_xml – name: Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany; clemens.krug@stud.tu-darmstadt.de (C.K.); stefan_peter.goebel@tu-darmstadt.de (S.G.)
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full-body motion capture
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Snippet Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise...
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StartPage 8389
SubjectTerms Accelerometers
COVID-19
Exercise
Feedback
full-body motion capture
haptic feedback
Haptics
human exercise assessment
Humans
imitation learning
inertial measurement units
Machine learning
Motion
Motion capture
Motor Skills
Movement
Physical fitness
Rehabilitation
Sensors
Training
transcutaneous electrical nerve stimulation
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Title Recognizing Full-Body Exercise Execution Errors Using the Teslasuit
URI https://www.ncbi.nlm.nih.gov/pubmed/34960481
https://www.proquest.com/docview/2612857075
https://www.proquest.com/docview/2615112824
https://pubmed.ncbi.nlm.nih.gov/PMC8703589
https://doaj.org/article/fa42ec41d2064604bc708633acccfc1c
Volume 21
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