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Abstract Electromyogram (EMG)-controlled prosthetic hands have advanced significantly during the past two decades. However, most of the currently available prosthetic hands fail to replicate human hand functionality and controllability. To measure the emulation of the human hand by a prosthetic hand, it is important to evaluate the functional characteristics. Moreover, incorporating feedback from end users during clinical testing is crucial for the precise assessment of a prosthetic hand. The work reported in this manuscript unfolds the functional characteristics of an EMG-CoNtrolled PRosthetIC Hand called ENRICH. ENRICH is a real-time EMG controlled prosthetic hand that can grasp objects in 250.8$$ \pm $$1.1 ms, fulfilling the neuromuscular constraint of a human hand. ENRICH is evaluated in comparison to 26 laboratory prototypes and 10 commercial variants of prosthetic hands. The hand was evaluated in terms of size, weight, operation time, weight lifting capacity, finger joint range of motion, control strategy, degrees of freedom, grasp force, and clinical testing. The box and block test and pick and place test showed ENRICH’s functionality and controllability. The functional evaluation reveals that ENRICH has the potential to restore functionality to hand amputees, improving their quality of life. 

Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration. 

Abstract Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39$$ \pm $$.84% using synergistic features. The pairedt-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.