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(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

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The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement's continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson's correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the nondirectional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement's kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.