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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Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering 2023, Vol.26 (1), p.1-11
Main Authors: McCabe, Megan V., Van Citters, Douglas W., Chapman, Ryan M.
Format: Article
Language:English
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
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Summary: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.
ISSN:1025-5842
1476-8259
DOI:10.1080/10255842.2022.2044028