Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

Asogbon, Mojisola Grace; Samuel, Oluwarotimi Williams; Geng, Yanjuan; Oluwagbemi, Olugbenga; Ning, Jifeng; Chen, Shixiong; Naik, Ganesh R.; Feng, Peng; Li, Guanglin

doi:10.1016/j.cmpb.2019.105278

Cited by 42 publications

(21 citation statements)

References 33 publications

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“…In similar trends with other results, the multi-features outperformed the single features when 2-channels, 4-channels and 6-channels were considered. Finally, by critically analyzing of our results, we discovered that when classification error, computation time, and number of electrodes were considered together, most feature sets achieved good classification performance with optimal windowing parameters of 250 ms/100 ms. Also, discoveries from this study through the systematic approach adopted can facilitate positive development in other areas where optimal features and machine learning driven approaches are required [41][42][43][44][45][46][47][48][49][50]. Last, one limitation of the current work is that the EMG pattern recognition system for movement intent decoding was analyzed in an off-line mode, and we hope to conduct online and real-time analysis in our future work.…”

Section: Discussionmentioning

confidence: 99%

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

et al. 2020

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The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

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Section: Discussionmentioning

confidence: 99%

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

et al. 2020

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“…Extension towards an Adaptive Control Framework Due to factors mentioned in Section 1, external factors could make redundant trained class boundaries, and as a result, cause classifier degradation; this warrants the need for classifier relearning to adapt to the dynamic changes which may be causing performance degradation [4]. Researchers in this area, such as Samuel et al, Chen et al, and Asogbon et al [12][13][14], have proposed extensions to various classifier architectures to allow online adaptation of classifier decision boundaries, as necessary [4]. Here, we propose an extension of the presented self-learning control framework as a means of an adaptive framework for cluster decision cluster reformation with real-time data from current anatomical and acquisition electronic states.…”

Section: Intent Decodingmentioning

confidence: 99%

A Self-Learning and Adaptive Control Scheme for Phantom Prosthesis Control Using Combined Neuromuscular and Brain-Wave Bio-Signals

Nsugbe

Samuel

2020

7th International Electronic Conference on Sensors and Applications

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The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user’s intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller.

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“…Thus, higher DOFs can be achieved for upper-limb prostheses [9]. However, certain critical factors affect the EMG-PR performance, including variation in the muscle contraction force [10]- [13], electrode position shift [14]- [16], hand orientation [17], [18], limb position [17], [19], [20] and nonstationarity of the EMG signal [21]. Among these factors, the variation in the muscle contraction force for a specific gesture occurs frequently in our daily activities.…”

Section: Introductionmentioning

confidence: 99%

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

et al. 2021

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Variation in the electromyogram pattern recognition (EMG-PR) performance with the muscle contraction force is a key limitation of the available prosthetic hand. To alleviate this problem, we propose a scheme to realize electromyogram signal normalization across channels before feature extraction. The proposed signal normalization scheme is validated over a dataset of nine transradial amputees that includes three force levels with six hand gestures. Moreover, we employ three classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM) and k-nearest neighbour (KNN), to evaluate the EMG-PR performance. In addition to the signal normalization scheme, we perform nonlinear transformation of the features by using the logarithm function. Both schemes facilitate merging of the muscle activation patterns of different force levels. The experimental results indicate that the force invariant EMG-PR performance (F1 score of at least 3.24% to 4.34%) of the proposed schemes is significantly enhanced compared to that obtained in recent studies. Therefore, we recommend using these features along with the proposed signal normalization scheme and nonlinear transformation of the features to improve the force invariant EMG-PR performance. The proposed feature extraction method achieves the highest F1 score of 91.28%, 91.39% and 90.56% when using the LDA, SVM and KNN classifiers, respectively.INDEX TERMS EMG Pattern recognition, Force invariant features, Muscle activation pattern, Signal normalization.

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Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

Cited by 42 publications

References 33 publications

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

A Self-Learning and Adaptive Control Scheme for Phantom Prosthesis Control Using Combined Neuromuscular and Brain-Wave Bio-Signals

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

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