Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of forearm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions. This system was compared to four previously proposed classification postprocessing methods, in both able-bodied and amputee subjects. Various nonstationarities were included in the experimental protocol to account for challenges posed in real-life settings, such as different contraction levels, static and dynamic motion phases, and effects induced by day-to-day transfers, such as electrode shifts, impedance changes, and psychometric user variability. The improvement in classification accuracy with respect to the unprocessed classifier ranged from 4.8% to 31.6%, depending on the scenarios investigated. The system significantly reduced misclassifications to wrong active classes and is thus a promising approach for improving the robustness of hand prosthesis controllability.
Pattern recognition and regression methods applied to the surface EMG have been used for estimating the user intended motor tasks across multiple degrees of freedom (DOF), for prosthetic control. While these methods are effective in several conditions, they are still characterized by some shortcomings. In this study we propose a methodology that combines these two approaches for mutually alleviating their limitations. This resulted in a control method capable of context-dependent movement estimation that switched automatically between sequential (one DOF at a time) or simultaneous (multiple DOF) prosthesis control, based on an online estimation of signal dimensionality. The proposed method was evaluated in scenarios close to real-life situations, with the control of a physical prosthesis in applied tasks of varying difficulties. Test prostheses were individually manufactured for both able-bodied and transradial amputee subjects. With these prostheses, two amputees performed the Southampton Hand Assessment Procedure test with scores of 58 and 71 points. The five able-bodied individuals performed standardized tests, such as the box&block and clothes pin test, reducing the completion times by up to 30%, with respect to using a state-of-the-art pure sequential control algorithm. Apart from facilitating fast simultaneous movements, the proposed control scheme was also more intuitive to use, since human movements are predominated by simultaneous activations across joints. The proposed method thus represents a significant step towards intelligent, intuitive and natural control of upper limb prostheses.
Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem. However, the lack of robustness of the current methods impedes their routine clinical application. In this study, we propose a novel algorithm for controlling multiple degrees of freedom sequentially, inherently proportionally and with high robustness, allowing a good level of prosthetic hand function. The control algorithm is based on the spatial linear combinations of amplitude-related EMG signal features. The weighting coefficients in this combination are derived from the optimization criterion of the common spatial patterns filters which allow for maximal discriminability between movements. An important component of the study is the validation of the method which was performed on both able-bodied and amputee subjects who used physical prostheses with customized sockets and performed three standardized functional tests mimicking daily-life activities of varying difficulty. Moreover, the new method was compared in the same conditions with one clinical/industrial and one academic state-of-the-art method. The novel algorithm outperformed significantly the state-of-the-art techniques in both subject groups for tests that required the activation of more than one degree of freedom. Because of the evaluation in real time control on both able-bodied subjects and final users (amputees) wearing physical prostheses, the results obtained allow for the direct extrapolation of the benefits of the proposed method for the end users. In conclusion, the method proposed and validated in real-life use scenarios, allows the practical usability of multifunctional hand prostheses in an intuitive way, with significant advantages with respect to previous systems.
In recent years, many sophisticated control strategies for multifunctional dexterous hand prostheses have been developed. It was indeed assumed that control mechanisms based on switching between degrees of freedom, which are in use since the 1960's, could not be extended to efficient control of more than two degrees of freedom. However, quantitative proof for this assumption has not been shown. In this study, we adopted the mode switching paradigm available in commercial prostheses for two degree of freedom control and we extended it for the control of seven functions (3.5 degrees of freedom) in a modern robotic hand. We compared the controllability of this scaled version of the standard method to a state of the art pattern recognition based control in an applied online study. The aim was to quantify whether multi-functional prosthetic control with mode switching outperformed pattern recognition in the control of a real prosthetic hand for daily life activities online. Although in simple grasp-release tasks the conventional method performed best, tasks requiring more complex control of multiple degrees of freedom required a more intuitive control method, such as pattern recognition, for achieving high performance.
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