Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its off-line performance and analyze their inter-dependencies. EMG data associated with seven practical hand gestures were recorded from partial-hand and trans-radial amputee volunteers as well as able-bodied volunteers. An extensive investigation was conducted to study the effect of analysis window length, window overlap, and the number of electrode channels on the classification accuracy as well as their interactions. Our main discoveries are that the effect of analysis window length on classification accuracy is practically independent of the number of electrodes for all participant groups; window overlap has no direct influence on classifier performance, irrespective of the window length, number of channels, or limb condition; the type of limb deficiency and the existing channel count influence the reduction in classification error achieved by adding more number of channels; partial-hand amputees outperform trans-radial amputees, with classification accuracies of only 11.3% below values achieved by able-bodied volunteers.

This paper presents a new electromyography activity detection technique in which 1-D local binary pattern histograms are used to distinguish between periods of activity and inactivity in myoelectric signals. The algorithm is tested on forearm surface myoelectric signals occurring due to hand gestures. The novel features of the presented method are that: 1) activity detection is performed across multiple channels using few parameters and without the need for majority vote mechanisms, 2) there are no per-channel thresholds to be tuned, which makes the process of activity detection easier and simpler to implement and less prone to errors, 3) it is not necessary to measure the properties of the signal during a quiescent period before using the algorithm. The algorithm is compared to other offline single- and double-threshold activity detection methods and, for the data sets tested, it is shown to have a better overall performance with greater tolerance to the noise in the real data set used.

Abstract-This paper presents a technique to improve the performance of an LDA classifier by determining if the predicted classification output is a misclassification and thereby rejecting it. This is achieved by automatically computing a class specific threshold with the help of ROC curves. If the posterior probability of a prediction is below the threshold, the classification result is discarded. This method of minimizing false positives is beneficial in the control of electromyography (EMG) based upper-limb prosthetic devices. It is hypothesized that a unique EMG pattern is associated with a specific hand gesture. In reality, however, EMG signals are difficult to distinguish, particularly in the case of multiple finger motions, and hence classifiers are trained to recognize a set of individual gestures. However, it is imperative that misclassifications be avoided because they result in unwanted prosthetic arm motions which are detrimental to device controllability. This warrants the need for the proposed technique wherein a misclassified gesture prediction is rejected resulting in no motion of the prosthetic arm. The technique was tested using surface EMG data recorded from thirteen amputees performing seven hand gestures. Results show the number of misclassifications was effectively reduced, particularly in cases with low original classification accuracy.

Introduction Flexible nasendoscopy (FNE) is the principal assessment method for vocal cord movement. Because the procedure is inherently subjective it may not be possible for clinicians to grade the degree of vocal cord movement reliably. The aim of this study was to assess the accuracy and consistency of grading vocal cord movement as viewed via FNE. Methods Thirty FNE videos, without sound or clinical information, were assessed by six consultant head and neck surgeons. The surgeons were asked to assess and grade right and left vocal cord movement independently, based on a five-category scale. This process was repeated three times on separate occasions. Agreement and reliability were assessed. Results Mean overall observed inter-rater agreement was 67.7% (sd 1.9) with the five-category scale, increasing to 91.4% (sd 1.9) when a three-category scale was derived. Mean overall observed intra-rater agreement was 78.3% (sd 9.7) for five categories, increasing to 93.1% (sd 3.3) for three categories. Discriminating vocal cord motion was less reliable using the five-category scale (k = 0.52) than with the three-category scale (k = 0.68). Conclusions This study demonstrates quantitatively that it is challenging to accurately and consistently grade subtle differences in vocal cord movement, as proven by the reduced agreement and reliability when using a five-point scale instead of a three-point scale. The study highlights the need for an objective measure to help in the assessment of vocal cord movement.

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