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Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables
Quantifying hip angles/moments during gait is critical for improving hip pathology diagnostic and treatment methods. Recent work has validated approaches combining wearables with artificial neural networks (ANNs) for cheaper, portable hip joint angle/moment computation. This study developed a Wearab...
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| Published in: | Computer methods in biomechanics and biomedical engineering 2023, Vol.26 (1), p.1-11 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c441t-6be5ebd6b9ebbbea3f903aea8feed713ee3067b3ffb13371acbdd71aeffb3bdc3 |
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| cites | cdi_FETCH-LOGICAL-c441t-6be5ebd6b9ebbbea3f903aea8feed713ee3067b3ffb13371acbdd71aeffb3bdc3 |
| container_end_page | 11 |
| container_issue | 1 |
| container_start_page | 1 |
| container_title | Computer methods in biomechanics and biomedical engineering |
| container_volume | 26 |
| creator | McCabe, Megan V. Van Citters, Douglas W. Chapman, Ryan M. |
| description | Quantifying hip angles/moments during gait is critical for improving hip pathology diagnostic and treatment methods. Recent work has validated approaches combining wearables with artificial neural networks (ANNs) for cheaper, portable hip joint angle/moment computation. This study developed a Wearable-ANN approach for calculating hip joint angles/moments during walking in the sagittal/frontal planes with data from 17 healthy subjects, leveraging one shin-mounted inertial measurement unit (IMU) and a force-measuring insole for data capture. Compared to the benchmark approach, a two hidden layer ANN (n = 5 nodes per layer) achieved an average rRMSE = 15% and R
2
=0.85 across outputs, subjects and training rounds. |
| doi_str_mv | 10.1080/10255842.2022.2044028 |
| format | article |
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2
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| identifier | ISSN: 1025-5842 |
| ispartof | Computer methods in biomechanics and biomedical engineering, 2023, Vol.26 (1), p.1-11 |
| issn | 1025-5842 1476-8259 |
| language | eng |
| recordid | cdi_pubmed_primary_35238719 |
| source | Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list) |
| subjects | Artificial neural networks Biomechanical Phenomena Data capture Gait Hip Hip Joint Humans inertial measurement units Inertial platforms Insoles instrumented insoles Knee Joint Neural networks Neural Networks, Computer Walking wearable Wearable Electronic Devices |
| title | Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables |
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