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Recent developments in dexterous myoelectric prosthetics have established a hardware base for human&#x2013;machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control (SPC) for multiple degrees of freedom (DoFs) movements. In this paper, we introduce an SPC approach for multi-DoF wrist movements using the cumulative spike trains (CSTs) of motor unit pools, merely leveraging single-DoF training. The efficacy of our proposed approach was validated offline against existing methods respectively based on non-negative matrix factorization and motor unit spike trains, using experimental data. The experimental process includes both single-DoF (for training) and multi-DoF (for testing) movements. We evaluated the performance using Pearson correlation coefficient (R) and the normalized root mean square error (nRMSE). The results reveal that our method outperforms comparative approaches in force estimation for both testing datasets (3 and 4). On average, for dataset 3, R and nRMSE of the flexion/extension DoF (the pronation/supination DoF) are 0.923&#x00B1;0.037 (0.901&#x00B1;0.040) and 12.3&#x00B1;3.1% (12.9&#x00B1;2.2%); similarly, those of dataset 4 are 0.865&#x00B1;0.057 (0.837&#x00B1;0.053) and 14.9&#x00B1;2.9% (15.4&#x00B1;2.0%), respectively. The outcomes demonstrate the effectiveness of our method in simultaneous and proportional force estimation for multi-DoF wrist movements, showing a promising potential as a neural-machine interface for SPC of dexterous myoelectric prostheses.

Decoding Multi-DoF Movements Using a CST-based Force Generation Model With Single-DoF Training