Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model
Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/22/9039 |
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| author | Fanjie Wang Wenqi Liang Hafiz Muhammad Rehan Afzal Ao Fan Wenjiong Li Xiaoqian Dai Shujuan Liu Yiwei Hu Zhili Li Pengfei Yang |
| author_facet | Fanjie Wang Wenqi Liang Hafiz Muhammad Rehan Afzal Ao Fan Wenjiong Li Xiaoqian Dai Shujuan Liu Yiwei Hu Zhili Li Pengfei Yang |
| author_sort | Fanjie Wang |
| collection | DOAJ |
| description | Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios. |
| first_indexed | 2024-03-09T16:28:28Z |
| format | Article |
| id | doaj.art-693c3303d2184e54a33267d72c3a14c9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:28Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-693c3303d2184e54a33267d72c3a14c92023-11-24T15:05:06ZengMDPI AGSensors1424-82202023-11-012322903910.3390/s23229039Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning ModelFanjie Wang0Wenqi Liang1Hafiz Muhammad Rehan Afzal2Ao Fan3Wenjiong Li4Xiaoqian Dai5Shujuan Liu6Yiwei Hu7Zhili Li8Pengfei Yang9Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaKey Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaKey Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaKey Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaNational Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, ChinaNational Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, ChinaNational Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, ChinaKey Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaNational Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, ChinaKey Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ChinaUsing inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.https://www.mdpi.com/1424-8220/23/22/9039joint angle estimationjoint moment estimationdeep learninginertial measurement unit |
| spellingShingle | Fanjie Wang Wenqi Liang Hafiz Muhammad Rehan Afzal Ao Fan Wenjiong Li Xiaoqian Dai Shujuan Liu Yiwei Hu Zhili Li Pengfei Yang Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model Sensors joint angle estimation joint moment estimation deep learning inertial measurement unit |
| title | Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model |
| title_full | Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model |
| title_fullStr | Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model |
| title_full_unstemmed | Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model |
| title_short | Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model |
| title_sort | estimation of lower limb joint angles and joint moments during different locomotive activities using the inertial measurement units and a hybrid deep learning model |
| topic | joint angle estimation joint moment estimation deep learning inertial measurement unit |
| url | https://www.mdpi.com/1424-8220/23/22/9039 |
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