Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses.

Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.

Compliant Prosthetic Wrists Entail More Natural Use Than Stiff Wrists During Reaching, Not (Necessarily) During Manipulation.

Developing an artificial arm with functions equivalent to those of the human arm is one of the challenging goals of bioengineering. State-of-the-artprostheses lack several degrees of freedom and force the individuals to compensate for them by means of compensatory movements, which often result in residual limb pain and overuse syndromes. Passive wristsmay reduce such compensatory actions, nonethelessto date their actual efficacy, associated to conventional myoelectric hands is a matter of debate. We hypothesized that a transradial prosthesiswould allow a simpler operation if its wrist behaved compliant during the reaching and grasping phase, and stiff during the holding andmanipulation phase. To assess this, we compared a stiff and a compliant wrist and evaluating the extent of compensatory movements in the trunk and shoulder, with unimpaired subjects wearing orthoses, while performing nine activities of daily living taken from the southampton hand assessment procedure. Our findings show indeed that the optimal compliance for a prosthetic wrist is specific to the phase of the motor task: the compliant wrist outperforms the stiff wrist during the reaching phase, whereas the stiff wrist exhibits more natural movements during the manipulation phase of heavy objects. Hence, this paper invites rehabilitation engineers to develop wrists with switchable compliance.

Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM).

In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.

Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.

In this paper we present surface electromyo-graphic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies.