Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity

Automatic misclassification rejection for LDA classifier using ROC curves

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

An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial

Correlation analysis of electromyogram signals for multiuser myoelectric interfaces

An inability to adapt myoelectric interfaces to a novel user's unique style of hand motion, or even to adapt to the motion style of an opposite limb upon which the interface is trained, are important factors inhibiting the practical application of myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram (EMG) signals generated by the muscles of different limbs. We propose in this paper a multiuser myoelectric interface which easily adapts to novel users and maintains good movement recognition performance. The main contribution is a framework for implementing style-independent feature transformation by using canonical correlation analysis (CCA) in which different users' data is projected onto a unified-style space. The proposed idea is summarized into three steps: 1) train a myoelectric pattern classifier on the set of style-independent features extracted from multiple users using the proposed CCA-based mapping; 2) create a new set of features describing the movements of a novel user during a quick calibration session; and 3) project the novel user's features onto a lower dimensional unified-style space with features maximally correlated with training data and classify accordingly. The proposed method has been validated on a set of eight intact-limbed subjects, left-and-right handed, performing ten classes of bilateral synchronous fingers movements with four electrodes on each forearm. The method was able to overcome individual differences through the style-independent framework with accuracies of >83% across multiple users. Testing was also performed on a set of ten intact-limbed and six below-elbow amputee subjects as they performed finger and thumb movements. The proposed framework allowed us to train the classifier on a normal subject's data while subsequently testing it on an amputee's data after calibration with a performance of >82% on average across all amputees. © 2001-2011 IEEE

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.Comment: Source code and datasets available: https://github.com/Giguelingueling/MyoArmbandDatase

Arduino-based myoelectric control: Towards longitudinal study of prosthesis use

Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications. In this work, we introduce the design of a cost-effective Arduino-based myoelectric control system with wearable electromyogram (EMG) sensors. The design considerations focused on home studies, so the robustness, user-friendly control adjustments, and user supports were the main concerns. Three control algorithms, namely, direct control, abstract control, and linear discriminant analysis (LDA) classification, were implemented in the system. In this paper, we will share our design principles and report the robustness of the system in continuous operation in the laboratory. In addition, we will show a first real-time implementation of the abstract decoder for prosthesis control with an able-bodied participant