Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks.

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.