Predicting net joint moments during a weightlifting exercise with a neural network model |
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| Affiliation: | 1. Department of Engineering, Faculty of Science and Technology, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark;2. Department of Cardiothoracic and Vascular Surgery, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark;3. Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark;1. School of Mechanical Engineering, National Technical University of Athens, Greece;2. Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Greece;3. School of Electrical and Computer Engineering, National Technical University of Athens, Greece;4. Department of Orthopedics and Trauma, KAT Hospital, Athens, Greece;1. Graduate School of Engineering, Tohoku University, 6-6-01 Aramaki-Aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan;2. Graduate School of Biomedical Engineering, Tohoku University, 6-6-01 Aramaki-Aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan;3. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada;4. Lyndhurst Centre, Toronto Rehabilitation Institute–University Health Network, Toronto, Ontario, Canada;2. Key Laboratory for Biomechanics and Mechanobiology of the Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, People’s Republic of China;3. SuperComputing Applications and Innovation Department – SCAI, CINECA, Milan, Italy;4. IBFM, Research National Council, Milan, Italy |
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| Abstract: | The purpose of this study was to develop and train a Neural Network (NN) that uses barbell mass and motions to predict hip, knee, and ankle Net Joint Moments (NJM) during a weightlifting exercise. Seven weightlifters performed two cleans at 85% of their competition maximum while ground reaction forces and 3-D motion data were recorded. An inverse dynamics procedure was used to calculate hip, knee, and ankle NJM. Vertical and horizontal barbell motion data were extracted and, along with barbell mass, used as inputs to a NN. The NN was then trained to model the association between the mass and kinematics of the barbell and the calculated NJM for six weightlifters, the data from the remaining weightlifter was then used to test the performance of the NN – this was repeated 7 times with a k-fold cross-validation procedure to assess the NN accuracy. Joint-specific predictions of NJM produced coefficients of determination (r2) that ranged from 0.79 to 0.95, and the percent difference between NN-predicted and inverse dynamics calculated peak NJM ranged between 5% and 16%. The NN was thus able to predict the spatiotemporal patterns and discrete peaks of the three NJM with reasonable accuracy, which suggests that it is feasible to predict lower extremity NJM from the mass and kinematics of the barbell. Future work is needed to determine whether combining a NN model with low cost technology (e.g., digital video and free digitising software) can also be used to predict NJM of weightlifters during field-testing situations, such as practice and competition, with comparable accuracy. |
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| Keywords: | Biomechanics Machine learning Neural network Sports |
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