Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate
Several low-cost force plates have been proposed as alternatives for laboratory-grade force plates. Nevertheless, the inability to quantify bilateral ground reaction force (GRF) prevents these inexpensive force plates from being used for biomechanical analysis and certain clinical metric acquisitio...
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| Format: | Article |
| Language: | English |
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IIUM Press, International Islamic University Malaysia
2025-01-01
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| Series: | International Islamic University Malaysia Engineering Journal |
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| Online Access: | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3379 |
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| author | Ying Heng Yeo Muhammad Fauzinizam Razali Zaidi Mohd Ripin Nur-Akasyah J. Mohamad Ikhwan Zaini Ridzwan Alexander Wai Teng Tan Jia Yi Tay |
| author_facet | Ying Heng Yeo Muhammad Fauzinizam Razali Zaidi Mohd Ripin Nur-Akasyah J. Mohamad Ikhwan Zaini Ridzwan Alexander Wai Teng Tan Jia Yi Tay |
| author_sort | Ying Heng Yeo |
| collection | DOAJ |
| description |
Several low-cost force plates have been proposed as alternatives for laboratory-grade force plates. Nevertheless, the inability to quantify bilateral ground reaction force (GRF) prevents these inexpensive force plates from being used for biomechanical analysis and certain clinical metric acquisition. This study developed deep-learning models, such as autoencoder and U-net, to predict bilateral GRF from vertical GRF measured using a low-cost custom force plate during sit-to-stand, gait initialization, and gait. Results indicated that the U-net model, which utilized STFT vertical GRF as input, performed the best. In addition to predicting the mediolateral GRF measured during sit-to-stand, the model accurately predicted the anterior-posterior and mediolateral GRF for sit-to-stand, gait initialization, and gait in the test dataset, achieving high Pearson's correlation coefficient, coefficient of determination, and intraclass correlation coefficient values of over 0.90, 0.79, and 0.89, respectively. The model demonstrated a higher Pearson's correlation coefficient compared to three related previous studies that utilized different methods to predict anterior-posterior GRF and six studies in inferring mediolateral GRF. The results demonstrated the potential of TFU and custom force plate as a GRF measurement tool to perform bio-mechanical analysis.
ABSTRAK: Beberapa plat daya kos rendah telah dicadangkan sebagai alternatif kepada plat daya berkualiti makmal. Walau bagaimanapun, ketidakmampuan untuk mengukur daya reaksi tanah (GRF) secara bilateral menghalang plat daya yang murah ini daripada digunakan untuk analisis biomekanik dan pengambilan metrik klinikal tertentu. Kajian ini membangunkan model pembelajaran mendalam, seperti autoencoder dan U-net, untuk meramalkan GRF bilateral daripada GRF menegak yang diukur menggunakan plat daya khas kos rendah semasa pergerakan duduk-ke-berdiri, permulaan berjalan, dan berjalan. Hasil menunjukkan bahawa model U-net, yang menggunakan GRF menegak STFT sebagai input, memberikan prestasi terbaik. Selain meramalkan GRF mediolateral yang diukur semasa duduk-ke-berdiri, model ini juga meramalkan dengan tepat GRF anterior-posterior dan mediolateral untuk duduk-ke-berdiri, permulaan berjalan, dan berjalan dalam set data ujian, mencapai nilai koefisien korelasi Pearson, koefisien penentuan, dan koefisien korelasi intrakelas yang tinggi melebihi 0.90, 0.79, dan 0.89, masing-masing. Model ini menunjukkan koefisien korelasi Pearson yang lebih tinggi berbanding tiga kajian terdahulu yang berkaitan yang menggunakan kaedah berbeza untuk meramalkan GRF anterior-posterior dan enam kajian dalam menyimpulkan GRF mediolateral. Hasil kajian menunjukkan potensi TFU dan plat daya khas sebagai alat pengukuran GRF untuk melakukan analisis biomekanik.
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| format | Article |
| id | doaj-art-c6381cb2787b4b2aa552c1245682af48 |
| institution | Kabale University |
| issn | 1511-788X 2289-7860 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IIUM Press, International Islamic University Malaysia |
| record_format | Article |
| series | International Islamic University Malaysia Engineering Journal |
| spelling | doaj-art-c6381cb2787b4b2aa552c1245682af482025-01-10T12:40:40ZengIIUM Press, International Islamic University MalaysiaInternational Islamic University Malaysia Engineering Journal1511-788X2289-78602025-01-0126110.31436/iiumej.v26i1.3379Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force PlateYing Heng Yeo0https://orcid.org/0000-0003-1282-7497Muhammad Fauzinizam Razali1https://orcid.org/0000-0001-9181-455XZaidi Mohd Ripin2https://orcid.org/0000-0001-9770-1409Nur-Akasyah J.3https://orcid.org/0000-0002-6416-4421Mohamad Ikhwan Zaini Ridzwan4https://orcid.org/0000-0002-3826-2642Alexander Wai Teng Tan5Jia Yi Tay6Universiti Sains Malaysia Universiti Sains Malaysia Universiti Sains Malaysia Universiti Sains Malaysia Universiti Sains Malaysia Universiti Sains MalaysiaUniversiti Sains Malaysia Several low-cost force plates have been proposed as alternatives for laboratory-grade force plates. Nevertheless, the inability to quantify bilateral ground reaction force (GRF) prevents these inexpensive force plates from being used for biomechanical analysis and certain clinical metric acquisition. This study developed deep-learning models, such as autoencoder and U-net, to predict bilateral GRF from vertical GRF measured using a low-cost custom force plate during sit-to-stand, gait initialization, and gait. Results indicated that the U-net model, which utilized STFT vertical GRF as input, performed the best. In addition to predicting the mediolateral GRF measured during sit-to-stand, the model accurately predicted the anterior-posterior and mediolateral GRF for sit-to-stand, gait initialization, and gait in the test dataset, achieving high Pearson's correlation coefficient, coefficient of determination, and intraclass correlation coefficient values of over 0.90, 0.79, and 0.89, respectively. The model demonstrated a higher Pearson's correlation coefficient compared to three related previous studies that utilized different methods to predict anterior-posterior GRF and six studies in inferring mediolateral GRF. The results demonstrated the potential of TFU and custom force plate as a GRF measurement tool to perform bio-mechanical analysis. ABSTRAK: Beberapa plat daya kos rendah telah dicadangkan sebagai alternatif kepada plat daya berkualiti makmal. Walau bagaimanapun, ketidakmampuan untuk mengukur daya reaksi tanah (GRF) secara bilateral menghalang plat daya yang murah ini daripada digunakan untuk analisis biomekanik dan pengambilan metrik klinikal tertentu. Kajian ini membangunkan model pembelajaran mendalam, seperti autoencoder dan U-net, untuk meramalkan GRF bilateral daripada GRF menegak yang diukur menggunakan plat daya khas kos rendah semasa pergerakan duduk-ke-berdiri, permulaan berjalan, dan berjalan. Hasil menunjukkan bahawa model U-net, yang menggunakan GRF menegak STFT sebagai input, memberikan prestasi terbaik. Selain meramalkan GRF mediolateral yang diukur semasa duduk-ke-berdiri, model ini juga meramalkan dengan tepat GRF anterior-posterior dan mediolateral untuk duduk-ke-berdiri, permulaan berjalan, dan berjalan dalam set data ujian, mencapai nilai koefisien korelasi Pearson, koefisien penentuan, dan koefisien korelasi intrakelas yang tinggi melebihi 0.90, 0.79, dan 0.89, masing-masing. Model ini menunjukkan koefisien korelasi Pearson yang lebih tinggi berbanding tiga kajian terdahulu yang berkaitan yang menggunakan kaedah berbeza untuk meramalkan GRF anterior-posterior dan enam kajian dalam menyimpulkan GRF mediolateral. Hasil kajian menunjukkan potensi TFU dan plat daya khas sebagai alat pengukuran GRF untuk melakukan analisis biomekanik. https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3379Force plateDeep learning modelGround reaction forcePredictionValidation |
| spellingShingle | Ying Heng Yeo Muhammad Fauzinizam Razali Zaidi Mohd Ripin Nur-Akasyah J. Mohamad Ikhwan Zaini Ridzwan Alexander Wai Teng Tan Jia Yi Tay Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate International Islamic University Malaysia Engineering Journal Force plate Deep learning model Ground reaction force Prediction Validation |
| title | Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate |
| title_full | Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate |
| title_fullStr | Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate |
| title_full_unstemmed | Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate |
| title_short | Bilateral Ground Reaction Force Prediction Using Deep Learning Models and Custom Force Plate |
| title_sort | bilateral ground reaction force prediction using deep learning models and custom force plate |
| topic | Force plate Deep learning model Ground reaction force Prediction Validation |
| url | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3379 |
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