Browsing by Author "MacIsaac, Dawn T."
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Item Analyzing the impact of class transitions on the design of pattern recognition-based myoelectric control schemes(Elsevier, 2022-01) Raghu, Shriram Tallam Puranam; MacIsaac, Dawn T.; Scheme, Erik J.Despite continued efforts to improve classification accuracy, it has been reported that offline accuracy is a poor indicator of the usability of pattern recognition-based myoelectric control. One potential source of this disparity is the existence of transitions between contraction classes that happen during regular use and are reported to be problematic for pattern recognition systems. Nevertheless, these transitions are often ignored or undefined during both the training and testing processes. In this work, we propose a set of metrics for analyzing the transitions that occur during the voluntary changes between contraction classes during continuous control. These metrics quantify the common types of errors that occur during transitions and compare them to existing metrics that apply only to the steady-state portions of the data. We then use these metrics to analyze transition characteristics of 6 commonly used classifiers on a novel dataset that includes continuous transitions between all combinations of seven different contraction classes. Results show that a linear discriminant classifier consistently outperforms other conventional classifiers during both transitions and steady-state conditions, despite having an almost identical offline performance. Results also show that, although offline training metrics correlate with steady-state performance, they do not correlate with transition performance. These insights suggest that the proposed set of metrics could provide a shift in perspective on the way pattern recognition systems are evaluated and provide a more representative picture of a classifier’s performance, potentially narrowing the gap between offline performance and online usability.Item Exploiting temporal dynamics to improve the robustness of continuous myoelectric control(University of New Brunswick, 2025-01) Tallam Puranam Raghu, Shriram; Scheme, Erik J.; MacIsaac, Dawn T.Myoelectric control based on Surface electromyography pattern recognition (sEMG-PR) offers intuitive and dexterous control of powered prostheses for people with limb differences. However, conventional sEMG-PR systems often struggle with transitions between movements, impacting online usability. In this thesis, we investigated these transition-specific challenges and proposed novel approaches to enhance the performance and user experience of sEMG-PR systems. We first established a comprehensive framework for evaluating classifier performance during transitions, incorporating transition-specific metrics and continuous dynamic datasets. This framework represents an improvement over conventional evaluation methods, which often focus primarily on steady-state performance and neglect transitions. Our analysis, utilizing this enhanced framework, revealed that classifiers, even with similar steady-state performance, can differ substantially in their ability to handle transitions. This finding underscores the limitations of conventional evaluation methods. Next, we systematically investigated various error-mitigation strategies, including existing and novel post-processing techniques. While some techniques showed promise, particularly those based on rejection, our findings suggest that relying solely on post-hoc error correction may not be sufficient to address the challenges of transitions effectively. Finally, we explored incorporating continuous dynamic data, inclusive of transitions, into the training process. Our results demonstrated the advantages of leveraging Long Short-Term Memory (LSTM) networks, which can effectively capture the dynamic nature of transitions. Furthermore, we pioneered the use of self-supervised learning for sEMG-PR, and demonstrated its effectiveness in learning meaningful and robust representations from unlabeled continuous dynamic data, leading to enhanced performance both offline and online. Our findings underscore the crucial role of temporal information, dynamic training data, and appropriate model selection, particularly temporal models like LSTMs, in achieving robust and reliable sEMG-PR based myoelectric control. The proposed approaches have the potential to significantly enhance the usability and effectiveness of these systems, paving the way for more intuitive and user-friendly prosthetic devices.Item Multivariate approach for assessing electrode positioning in surface electromyography(University of New Brunswick, 2023-08) Nsofor, Chitom Clare; MacIsaac, Dawn T.; Englehart, KevinPlacing electrodes near or over an innervation zone has been shown to affect the quality and integrity of the recorded signals. The aim of this work was to investigate whether using pattern recognition to analyze electromyography data could lead to an automated approach for estimating the graded effect of an innervation zone on surface electromyography signal. Using a set of features from simulated electromyography signals as input, classification and regression algorithms were explored to predict the graded effect of an innervation zone. The regression technique was observed to be best suited for the application. The effects of physiological parameter variability between the training and test data sets were also investigated. Some physiological parameters, especially the innervation zone distribution and conduction velocity, were found to have the most impact on the performance of the regressor. Regression is a promising approach for subsequent research, especially with recorded data.