Learning based lower limb joint kinematic estimation using open source IMU data
Abstract This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. To meet this demand, we lev...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-89716-4 |
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| _version_ | 1850198041486688256 |
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| author | Benjamin Hur Sunin Baek Inseung Kang Daekyum Kim |
| author_facet | Benjamin Hur Sunin Baek Inseung Kang Daekyum Kim |
| author_sort | Benjamin Hur |
| collection | DOAJ |
| description | Abstract This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. To meet this demand, we leveraged an open-source dataset to develop and evaluate three training approaches. The first involved training a model exclusively on data from a single user, resulting in high accuracy for that individual only. The second approach trained a model on data from multiple users to generalize across individuals; however, demonstrated lower accuracy due to variations in gait patterns across users. The third approach added transfer learning to the second, improving estimation accuracy for new users through fine-tuning with a small portion of their data. This model overcame the limitations of the previous methods’ dependency on extensive data collection, and achieved comparable performance to inverse kinematics, making it an effective solution for diverse populations. Additionally, our analysis on IMU combinations suggests that IMUs placed on the femur and calcaneus are the best for most cases. This framework not only reduces the need for extensive data collection but also enhances personalized gait analysis, enabling more efficient and accessible applications in both clinical assessments and real-world environments for broader use. |
| format | Article |
| id | doaj-art-1534608d3e8647749abea72c38cc4eee |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1534608d3e8647749abea72c38cc4eee2025-08-20T02:12:58ZengNature PortfolioScientific Reports2045-23222025-02-0115111210.1038/s41598-025-89716-4Learning based lower limb joint kinematic estimation using open source IMU dataBenjamin Hur0Sunin Baek1Inseung Kang2Daekyum Kim3Korea University, School of Mechanical EngineeringKorea University, School of Mechanical EngineeringDepartment of Mechanical Engineering, Carnegie Mellon UniversityKorea University, School of Mechanical EngineeringAbstract This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. To meet this demand, we leveraged an open-source dataset to develop and evaluate three training approaches. The first involved training a model exclusively on data from a single user, resulting in high accuracy for that individual only. The second approach trained a model on data from multiple users to generalize across individuals; however, demonstrated lower accuracy due to variations in gait patterns across users. The third approach added transfer learning to the second, improving estimation accuracy for new users through fine-tuning with a small portion of their data. This model overcame the limitations of the previous methods’ dependency on extensive data collection, and achieved comparable performance to inverse kinematics, making it an effective solution for diverse populations. Additionally, our analysis on IMU combinations suggests that IMUs placed on the femur and calcaneus are the best for most cases. This framework not only reduces the need for extensive data collection but also enhances personalized gait analysis, enabling more efficient and accessible applications in both clinical assessments and real-world environments for broader use.https://doi.org/10.1038/s41598-025-89716-4 |
| spellingShingle | Benjamin Hur Sunin Baek Inseung Kang Daekyum Kim Learning based lower limb joint kinematic estimation using open source IMU data Scientific Reports |
| title | Learning based lower limb joint kinematic estimation using open source IMU data |
| title_full | Learning based lower limb joint kinematic estimation using open source IMU data |
| title_fullStr | Learning based lower limb joint kinematic estimation using open source IMU data |
| title_full_unstemmed | Learning based lower limb joint kinematic estimation using open source IMU data |
| title_short | Learning based lower limb joint kinematic estimation using open source IMU data |
| title_sort | learning based lower limb joint kinematic estimation using open source imu data |
| url | https://doi.org/10.1038/s41598-025-89716-4 |
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