Distance running related injuries are common, and many ailments have been associated with faulty posture. Conventional measurement of running kinematics requires sophisticated motion capture system in laboratory. In this study, we developed a wearable solution to accurately predict lower limb running kinematics using a single inertial measurement unit placed on the left lower leg. The running data collected from participants was used to train a model using long short-term memory (LSTM) neural networks with an inter-subject approach that predicted lower limb kinematics with an average accuracy of 80.2%, 85.8%, and 69.4% for sagittal hip, knee and ankle joint angles respectively for the ipsilateral limb. A comparable accuracy range was observed for the contralateral limb. The average RMSE (root mean squared error) of sagittal hip, knee and ankle were 8.76o, 13.13o, and 9.67o respectively for the ipsilateral limb. Analysis of contralateral limb kinematics was performed. The model established in this study can be used as a monitoring device to track essential running kinematics in natural running environments. Besides, the wearable solution can be an integral part of a real-time gait retraining biofeedback system for injury prevention and rehabilitation. Copyright © 2023 by IEEE Engineering in Medicine and Biology Society (EMBC).
|Published - Jul 2023
|The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Sydney, Australia
Duration: 24 Jul 2023 → 27 Jul 2023
|The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
|IEEE EMBC 2023
|24/07/23 → 27/07/23