A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells
Ground reaction force (GRF) is essential for estimating muscle strength and joint torque in inverse dynamic analysis. Typically, it is measured using a force plate. However, force plates have spatial limitations, and studies of gaits involve numerous steps and thus require a large number of force pl...
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3428 |
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| author | Junggil Kim Hyeon Kang Seulgi Lee Jinseung Choi Gyerae Tack |
| author_facet | Junggil Kim Hyeon Kang Seulgi Lee Jinseung Choi Gyerae Tack |
| author_sort | Junggil Kim |
| collection | DOAJ |
| description | Ground reaction force (GRF) is essential for estimating muscle strength and joint torque in inverse dynamic analysis. Typically, it is measured using a force plate. However, force plates have spatial limitations, and studies of gaits involve numerous steps and thus require a large number of force plates, which is disadvantageous. To overcome these challenges, we developed a deep learning model for estimating three-axis GRF utilizing shoes with three uniaxial load cells. GRF data were collected from 81 people as they walked on two force plates while wearing shoes with three load cells. The three-axis GRF was calculated using a seq2seq approach based on long short-term memory (LSTM). To conduct the learning, validation, and testing, random selection was performed based on the subjects. The 60 selected participants were divided as follows: 37 were in the training set, 12 were in the validation set, and 11 were in the test set. The estimated GRF matched the force plate-measured GRF with correlation coefficients of 0.97, 0.96, and 0.90 and root mean square errors of 65.12 N, 15.50 N, and 9.83 N for the vertical, anterior–posterior, and medial–lateral directions, respectively, and there was a mid-stance timing error of 5.61% in the test dataset. A Bland–Altman analysis showed good agreement for the maximum vertical GRF. The proposed shoe with three uniaxial load cells and seq2seq LSTM can be utilized for estimating the 3D GRF in an outdoor environment with level ground and/or for gait research in which the subject takes several steps at their preferred walking speed, and hence can supply crucial data for a basic inverse dynamic analysis. |
| first_indexed | 2024-03-11T05:25:28Z |
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| id | doaj.art-16ad8434b8f34f0da32e861858b6713f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:25:28Z |
| publishDate | 2023-03-01 |
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| series | Sensors |
| spelling | doaj.art-16ad8434b8f34f0da32e861858b6713f2023-11-17T17:32:23ZengMDPI AGSensors1424-82202023-03-01237342810.3390/s23073428A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load CellsJunggil Kim0Hyeon Kang1Seulgi Lee2Jinseung Choi3Gyerae Tack4Department of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of KoreaDepartment of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of KoreaDepartment of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of KoreaDepartment of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of KoreaDepartment of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of KoreaGround reaction force (GRF) is essential for estimating muscle strength and joint torque in inverse dynamic analysis. Typically, it is measured using a force plate. However, force plates have spatial limitations, and studies of gaits involve numerous steps and thus require a large number of force plates, which is disadvantageous. To overcome these challenges, we developed a deep learning model for estimating three-axis GRF utilizing shoes with three uniaxial load cells. GRF data were collected from 81 people as they walked on two force plates while wearing shoes with three load cells. The three-axis GRF was calculated using a seq2seq approach based on long short-term memory (LSTM). To conduct the learning, validation, and testing, random selection was performed based on the subjects. The 60 selected participants were divided as follows: 37 were in the training set, 12 were in the validation set, and 11 were in the test set. The estimated GRF matched the force plate-measured GRF with correlation coefficients of 0.97, 0.96, and 0.90 and root mean square errors of 65.12 N, 15.50 N, and 9.83 N for the vertical, anterior–posterior, and medial–lateral directions, respectively, and there was a mid-stance timing error of 5.61% in the test dataset. A Bland–Altman analysis showed good agreement for the maximum vertical GRF. The proposed shoe with three uniaxial load cells and seq2seq LSTM can be utilized for estimating the 3D GRF in an outdoor environment with level ground and/or for gait research in which the subject takes several steps at their preferred walking speed, and hence can supply crucial data for a basic inverse dynamic analysis.https://www.mdpi.com/1424-8220/23/7/3428three-axis ground reaction force estimationseq2seq LSTMload cellgait |
| spellingShingle | Junggil Kim Hyeon Kang Seulgi Lee Jinseung Choi Gyerae Tack A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells Sensors three-axis ground reaction force estimation seq2seq LSTM load cell gait |
| title | A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells |
| title_full | A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells |
| title_fullStr | A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells |
| title_full_unstemmed | A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells |
| title_short | A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells |
| title_sort | deep learning model for 3d ground reaction force estimation using shoes with three uniaxial load cells |
| topic | three-axis ground reaction force estimation seq2seq LSTM load cell gait |
| url | https://www.mdpi.com/1424-8220/23/7/3428 |
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