Strike index estimation using a convolutional neural network with a single, shoe-mounted inertial sensor

Strike index is a measurement of the center of pressure position relative to the foot length, and it is regarded as a gold standard in classifying strike pattern in runners. However, strike index requires sophisticated laboratory equipment, e.g., force plates and optical motion capture. We present a...

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Bibliographic Details
Published in:Journal of biomechanics 2022-06, Vol.139, p.111145-111145, Article 111145
Main Authors: Tan, Tian, Strout, Zachary A., Cheung, Roy T.H., Shull, Peter B.
Format: Article
Language:English
Subjects:
Artificial neural networks
Athletic shoes
Center of pressure
Estimation
Footstrike
Footwear
Force plates
Gait
Inertial platforms
Inertial sensing devices
Injury prevention
Landing pattern
Machine learning
Motion capture
Neural networks
Position measurement
Running
Sensors
Shoes & boots
Treadmills
Velocity
Wearable sensor
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Summary:Strike index is a measurement of the center of pressure position relative to the foot length, and it is regarded as a gold standard in classifying strike pattern in runners. However, strike index requires sophisticated laboratory equipment, e.g., force plates and optical motion capture. We present a method of estimating strike index using data from a shoe-mounted inertial measurement unit (IMU) analyzed by a participant-independent convolutional neural network (CNN), which consists of convolutional, max-pooling, and fully-connected layers. To promote data variability, 16 participants were required to land with three strike patterns (rearfoot, midfoot, and forefoot strike) while running on an instrumented treadmill in four conditions i.e., two footwear types and two running speeds. Using the proposed approach, strike index was estimated with a root mean square error of 6.9% and a R2 of 0.89. Training and testing the model with different variations of the data collected showed that the model was robust to changes in speed. The proposed approach enables accurate estimation of strike index outside of traditional gait laboratories. This solution potentially improves running performance and reduces injury risk in distance runners.
ISSN:0021-9290
1873-2380
DOI:10.1016/j.jbiomech.2022.111145