Myoelectric control systems—A survey

Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.

Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

In this paper we describe the novel concept of performance-based progressive robot therapy that uses speed, time, or EMG thresholds to initiate robot assistance. We pioneered the clinical application of robot-assisted therapy focusing on stroke—the largest cause of disability in the US. We have completed several clinical studies involving well over 200 stroke patients. Research to date has shown that repetitive task-specific, goal-directed, robot-assisted therapy is effective in reducing motor impairments in the affected arm after stroke. One research goal is to determine the optimal therapy tailored to each stroke patient that will maximize his/her recovery. A proposed method to achieve this goal is a novel performance-based impedance control algorithm, which is triggered via speed, time, or EMG. While it is too early to determine the effectiveness of the algorithm, therapists have already noted one very strong benefit, a significant reduction in arm tone.

Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible. Machine-accessible metadata file describing the reported data (ISA-Tab format)

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Electromyography data for non-invasive naturally-controlled robotic hand prostheses

This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work.

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A. Otsuka

Papers

A human-assisting manipulator teleoperated by EMG signals and arm motions