6 research outputs found

    Hannes Prosthesis Control Based on Regression Machine Learning Algorithms

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    The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications

    Restoring tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands

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    Despite previous studies on the restoration of tactile sensation to the fingers and the hand, there are no examples of use of the routed sensory information to finely control a prosthestic hand in complex grasp and manipulation tasks. Here, it is shown that force and slippage sensations can be elicited in an amputee by means of biologically inspired slippage detection and encoding algorithms, supported by a stick-slip model of the performed grasp. A combination of cuff and intraneural electrodes was implanted for 11 weeks in a young woman with hand amputation and was shown to provide close-to-natural force and slippage sensations, paramount for substantially improving manipulative skills with the prosthesis. Evidence is provided about the improvement of the participant's grasping and manipulation capabilities over time resulting from neural feedback. The elicited tactile sensations enabled the successful fulfillment of fine grasp and manipulation tasks with increasing complexity. Grasp performance was quantitatively assessed by means of instrumented objects and a purposely developed metrics. Closed-loop control capabilities enabled by the neural feedback were compared with those achieved without feedback. Further, the work demonstrates that the described amelioration of motor performance in dexterous tasks had as central neurophysiological correlates changes in motor cortical plasticity and that such changes were not of purely motor origin, but were the effect of a strong and persistent drive of the sensory feedback. \ua9 2019 The Authors, some rights reserved
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