Latent Space Representation of Human Movement: Assessing the Effects of Fatigue
Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to le...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7775 |
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| author | Thomas Rousseau Gentiane Venture Vincent Hernandez |
| author_facet | Thomas Rousseau Gentiane Venture Vincent Hernandez |
| author_sort | Thomas Rousseau |
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| description | Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of Adversarial AutoEncoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants’ motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue’s impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs. |
| format | Article |
| id | doaj-art-3b055c2ead664cdfb9fc9c8dfc053880 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-3b055c2ead664cdfb9fc9c8dfc0538802024-12-13T16:32:43ZengMDPI AGSensors1424-82202024-12-012423777510.3390/s24237775Latent Space Representation of Human Movement: Assessing the Effects of FatigueThomas Rousseau0Gentiane Venture1Vincent Hernandez2Faculty of Odontology, University of Reims Champagne-Ardenne, 51100 Reims, FranceDepartment of Mechanical Engineering, The University of Tokyo, Tokyo 113-8654, JapanDepartment of Mechanical Engineering, The University of Tokyo, Tokyo 113-8654, JapanFatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of Adversarial AutoEncoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants’ motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue’s impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs.https://www.mdpi.com/1424-8220/24/23/7775fatiguehuman activity recognitiondeep learningadversarial autoencoderinertial measurement unitground reaction force |
| spellingShingle | Thomas Rousseau Gentiane Venture Vincent Hernandez Latent Space Representation of Human Movement: Assessing the Effects of Fatigue Sensors fatigue human activity recognition deep learning adversarial autoencoder inertial measurement unit ground reaction force |
| title | Latent Space Representation of Human Movement: Assessing the Effects of Fatigue |
| title_full | Latent Space Representation of Human Movement: Assessing the Effects of Fatigue |
| title_fullStr | Latent Space Representation of Human Movement: Assessing the Effects of Fatigue |
| title_full_unstemmed | Latent Space Representation of Human Movement: Assessing the Effects of Fatigue |
| title_short | Latent Space Representation of Human Movement: Assessing the Effects of Fatigue |
| title_sort | latent space representation of human movement assessing the effects of fatigue |
| topic | fatigue human activity recognition deep learning adversarial autoencoder inertial measurement unit ground reaction force |
| url | https://www.mdpi.com/1424-8220/24/23/7775 |
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