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Introduction: Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question. Method: This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder. Result: The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses. [ABSTRACT FROM AUTHOR]

Shoulder disarticulation amputees account for a small portion of upper‐limb amputees, thus little emphasis has been devoted to developing functional prosthesis for this cohort of amputees. In this study, shoulder girdle recognition was investigated with acquired data from electrophysiological (electromyography [EMG]) and low frequency contraction (accelerometer [Acc]) signals from both amputee and non‐amputee participants. The contribution of this study is based around the contrast of the classification accuracy (CA) for different sensor configurations using a unique set of signal features. It was seen that the fusion of the EMG‐Acc produced an enhancement in the CA in the range of 10%–20%, depending on which windowing parameters were considered. From this, it was seen that the best combination of a windowing scheme and classifier would likely be for the 350 ms and spectral regression discriminant analysis, with a fusion of the EMG‐Acc information. The results have thus provided evidence that the two sensors can be combined and used in practice for prosthesis control. Taking a holistic view on the study, the authors conclude by providing a framework on how the shoulder motion recognition could be combined with neuromuscular reprogramming to contribute towards easing the cognitive burden of amputees during the prosthesis control process. [ABSTRACT FROM AUTHOR]

This paper presents a review of a number of bio-sensing methods for gesture intent signal acquisition in control tasks for upper-limb prosthesis. The paper specifically provides a breakdown of the control task in myoelectric prosthesis, and in addition, highlights and describes the importance of the acquisition of a high-quality bio-signal. The paper also describes commonly used invasive and non-invasive brain and muscle machine interfaces such as electroencephalography, electrocorticography, electroneurography, surface electromyography, sonomyography, mechanomyography, near infra-red, force sensitive resistance/pressure, and magnetoencephalography. Each modality is reviewed based on its operating principle and limitations in gesture recognition, followed by respective advantages and disadvantages. Also described within this paper, are multimodal sensing approaches, which involve data fusion of information from various sensing modalities for an enhanced neuromuscular bio-sensing source. Using a semi-systematic review methodology, we are able to derive a novel tabular approach towards contrasting the various strengths and weaknesses of the reviewed bio-sensing methods towards gesture recognition in a prosthesis interface. This would allow for a streamlined method of down selection of an appropriate bio-sensor given specific prosthesis design criteria and requirements. The paper concludes by highlighting a number of research areas that require more work for strides to be made towards improving and enhancing the connection between man and machine as it concerns upper-limb prosthesis. Such areas include classifier augmentation for gesture recognition, filtering techniques for sensor disturbance rejection, feeling of tactile sensations with an artificial limb. [ABSTRACT FROM AUTHOR]

• This study proposed and systematically investigate the capability of optimized filtering techniques in denoising EMG signal towards improving its overall quality to realize adequate EMG-PR based prostheses control. • The performance of the filtering techniques was investigated using myoelectric signals collected from transhumeral amputees across different features- classifier combination under different experimental condition. • Experimental results with statistical test showed that the proposed filtering technique led to improved EMG signal quality that resulted in consistent high decoding performance for the amputees' limb movement intents. • The outcomes of the study could be potential for improving the clinical and commercial robustness of EMG-PR based multifunctional prostheses. Electromyogram (EMG) based pattern recognition (PR) strategies have been well investigated and applied as viable control strategies for upper limb prostheses. Transhumeral amputees often do not have enough residual muscles to produce high-quality signals needed to control the device adequately, so EMG-PR based prostheses for above-elbow amputees have not yet gained widespread acceptance in clinical and commercial settings. The limited acquired signal from these amputees often contains noises that make it challenging to accurately decode their limb movement intent. Thus, this study explored the capability of optimized filtering techniques (including Wiener, Hampel, and 1-Dimensional Median filters) in denoising the EMG signal towards improving its overall quality to realize adequate EMG-PR based control strategies. The performance of the filtering techniques was investigated using myoelectric signals collected from transhumeral amputees. Five different time-domain features and two commonly adopted classifiers were employed to classify the limb movement tasks, while metrics including Accuracy, Recall, and F-score were used for evaluation. Experimental results indicate that the Wiener filter (WF) achieved an increment of up to 6.24%, 6.38%, and 6.95% in accuracy compared to the other filtering methods using the original data, data with random noise, and data with white Gaussian noise, respectively. Also, T-Test statistical analysis results show that WF led to improved performance with statistical significance (p < 0.05) over all other considered methods across metrics and classifiers, with an improvement of up to 18.64% for individual classes of motions in terms of accuracy. Findings from this study indicate that the proposed method enhanced the amputees' EMG signal quality which led to substantial improvement in classification of their limb movement intents and the method may positively impact the EMG-PR based prostheses control schemes. [ABSTRACT FROM AUTHOR]