On the Classification of Electromyography Signals to Control a Four Degree-Of-Freedom Prosthetic Device.

This study investigates the applicability of Electromyography (EMG) signal classification algorithms with relatively low training time to control prosthetic devices. The perceived quality of control depends on many factors, such as the 1) accuracy of the algorithm, 2) the complexity of the control, and 3) the ability to compensate for the error. The high granularity of control in the time domain reduces the perceived effect of error but also limits the classification accuracy. This work aims to find the borderline for the accuracy of algorithms to be selected as a control strategy for hand prosthetic devices and thus shorten the gap between laboratory devices and commercially available devices. In particular, we compared five classification algorithms and selected one for real-time testing. The results from a test conducted on four subjects showed that the EMG-based control strategy has comparable performances with an IMU-based controller.

Human force discrimination during active arm motion for force feedback design.

The goal of this study was to analyze the human ability of external force discrimination while actively moving the arm. With the approach presented here, we give an overview for the whole arm of the just-noticeable differences (JNDs) for controlled movements separately executed for the wrist, elbow, and shoulder joints. The work was originally motivated in the design phase of the actuation system of a wearable exoskeleton, which is used in a teleoperation scenario where force feedback should be provided to the subject. The amount of this force feedback has to be calibrated according to the human force discrimination abilities. In the experiments presented here, 10 subjects performed a series of movements facing an opposing force from a commercial haptic interface. Force changes had to be detected in a two-alternative forced choice task. For each of the three joints tested, perceptual thresholds were measured as absolute thresholds (no reference force) and three JNDs corresponding to three reference forces chosen. For this, we used the outcome of the QUEST procedure after 70 trials. Using these four measurements we computed the Weber fraction. Our results demonstrate that different Weber fractions can be measured with respect to the joint. These were 0.11, 0.13, and 0.08 for wrist, elbow, and shoulder, respectively. It is discussed that force perception may be affected by the number of muscles involved and the reproducibility of the movement itself. The minimum perceivable force, on average, was 0.04 N for all three joints.

Human-like reflex control for an artificial hand.

In this paper, we illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and stiffness control strategy based on dynamic artificial neurons able to simulate the neurons acting in the human reflex control. The controller has a hierarchical structure. At the lowest level there are the receptors able to convert the analogical signal into a neural impulsive signal appropriate to govern the reflex control neurons. Immediately upon it, the artificial motoneurons set the actuators inner pressure to control the finger joint position and moment. Other auxiliary neurons in combination with the motoneurons are able to set the finger stiffness and emulate the inverse myotatic reflex control. Stiffness modulation is important both to save energy during task execution, and to manage objects made of different materials. The inverse myotatic reflex is able to protect the hand from possible harmful external actions. The paper also presents the dynamic model of the joints and of the artificial muscles actuating Blackfingers, our artificial hand. This new type of neural control has been simulated on the Blackfingers model; the results indicate that the developed control is very flexible and efficient for all kind of joints present in the humanoid hand.