Altai, Zainab and Boukhennoufa, Issam and Zhai, Xiaojun and Phillips, Andrew and Moran, Jason and Liew, Bernard XW (2023) Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors. Frontiers in Bioengineering and Biotechnology, 11. 1215770-. DOI https://doi.org/10.3389/fbioe.2023.1215770
Altai, Zainab and Boukhennoufa, Issam and Zhai, Xiaojun and Phillips, Andrew and Moran, Jason and Liew, Bernard XW (2023) Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors. Frontiers in Bioengineering and Biotechnology, 11. 1215770-. DOI https://doi.org/10.3389/fbioe.2023.1215770
Altai, Zainab and Boukhennoufa, Issam and Zhai, Xiaojun and Phillips, Andrew and Moran, Jason and Liew, Bernard XW (2023) Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors. Frontiers in Bioengineering and Biotechnology, 11. 1215770-. DOI https://doi.org/10.3389/fbioe.2023.1215770
Abstract
<jats:p>Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard’ to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.</jats:p>
Item Type: | Article |
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Uncontrolled Keywords: | machine learning; wearable sensors; joint moments; motion capture; musculoskeletal modelling |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Aug 2025 13:44 |
Last Modified: | 05 Aug 2025 14:28 |
URI: | http://repository.essex.ac.uk/id/eprint/41377 |
Available files
Filename: Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.pdf
Licence: Creative Commons: Attribution 4.0