Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning

Prahm, Cosima; Schulz, Alexander; Paaben, Benjamin; Schoisswohl, Johannes; Kaniušas, Eugenijus; Dorffner, Georg; Hammer, Barbara; Aszmann, Oskar C.

doi:10.1109/tnsre.2019.2907200

Cited by 47 publications

(28 citation statements)

References 34 publications

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“…Some studies attempted to extract an invariant EMG feature of specific motions to strengthen the robustness of the EMG pattern recognition model and improve the classification accuracy in repeated uses (Boostani and Moradi, 2003 ; Tkach et al, 2010 ; Phinyomark et al, 2013 ). Some researchers adopted an unsupervised adaptive classification method, which enables the model to adapt EMG data with different distributions (Liu, 2015 ; Huang et al, 2017 ; Prahm et al, 2019 ). Although extracting an invariant EMG feature or applying an unsupervised adaptive classification method can improve the classification accuracy of schemes to an extent, the classification accuracy achieved by these methods cannot satisfy the requirements under the conditions of changes of electrode positions in repeated uses.…”

Section: Introductionmentioning

confidence: 99%

Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Zhang

et al. 2021

Front. Neurorobot.

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Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p < 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p < 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses.

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Section: Introductionmentioning

confidence: 99%

Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Zhang

et al. 2021

Front. Neurorobot.

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“…Several factors could generate non-stationarities in the EMG signals that fed the system limiting a robust performance. The most common problems are: limitations of EMG signal acquisition process [34][35][36][37], arm positioning [38,39], electrode shifting [39][40][41], skin conditions [25], fatigue [42] or time degradation [43]. These factors affected the reliability of modern prosthesis control methods over time and conditions of use.…”

Section: Introductionmentioning

confidence: 99%

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

et al. 2020

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New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.

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“…Such systems learn from newly acquired data as the old model becomes defective. However, this has mostly been studied with PR-based controllers [21,22,23,24,25,26,27] and there are only a few examples of adaptive regression-based controllers [28,29,30]. In both cases, machine adaptation may occur either concurrently with user adaptation in real-time or can be implemented as incremental steps in learning.…”

Section: Introductionmentioning

confidence: 99%

Directional Forgetting for Stable Co-Adaptation in Myoelectric Control

Yeung

Farina

Vujaklija

2019

Sensors

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Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.

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Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning

Cited by 47 publications

References 34 publications

Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

Directional Forgetting for Stable Co-Adaptation in Myoelectric Control

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