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Motion prediction based on kinematic information such as body segment displacement and joint angle has been widely studied. Because motions originate from forces, it is beneficial to estimate dynamic information, such as the ground reaction force (GRF), in addition to kinematic information for advanced motion prediction. In this study, we proposed a method to estimate GRF and ground reaction moment (GRM) from electromyography (EMG) in combination with and without an inertial measurement unit (IMU) sensor using a machine learning technique. A long short-term memory network, which is suitable for processing long time-span data, was constructed with EMG and IMU as input data to estimate GRF during posture control and stepping motion. The results demonstrate that the proposed method can provide the GRF estimation with a root mean square error (RMSE) of 8.22 ± 0.97% (mean ± SE) for the posture control motion and 11.17 ± 2.16% (mean ± SE) for the stepping motion. We could confirm that EMG input is essential especially when we need to predict both GRF and GRM with limited numbers of sensors attached under knees. In addition, we developed a GRF visualization system integrated with ongoing motion in a Unity environment. This system enabled the visualization of the GRF vector in 3-dimensional space and provides predictive motion direction based on the estimated GRF, which can be useful for human motion prediction with portable sensors.

In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q -matrix. We trained this control algorithm to adapt to several walking speed datasets under one control policy and subsequently compared its performance with that of other control algorithms. The results showed that our proposed reward function performed better than the conventional single reward function in terms of the normalized root mean squared error and also showed a faster convergence trend. Furthermore, our control strategy converged within our desired performance index and could adapt to several walking speeds. Our proposed control structure has also an overall better performance compared to user-adaptive control, while some of its walking speeds performed better than the neural network predictive control from existing studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Hutabarat, Ekkachai, Hayashibe and Kongprawechnon.)