On the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach

Waris, Asim; Mendez, Irene G.; Englehart, Kevin; Jensen, Winnie; Kamavuako, Ernest Nlandu

doi:10.1088/1741-2552/aae9d4

Cited by 22 publications

(23 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although previous studies (e.g. [33]) have made use of the same combination of feature set and acquisitions setup as the LDA benchmark framework of the current study, concerns can be raised over the appropriateness of the selected time-domain features due to the relatively limited sampling rate of the system (200 Hz). This, in turn, could potentially make comparisons between MRL and LDA unbalanced, as the former utilizes high frequency information to a lesser degree than the latter.…”

Section: Calibration Of Modelsmentioning

confidence: 99%

“…Stated succinctly, the statistical relationship connecting measured myoelectric activity X to movement y is not necessarily identical to the relationship which was valid at the time of calibration data acquisition, making the problem a specific instance of model overfitting. Variations in electrode positions; skin conductivity; limb placement and load; and fatigue are all examples of mechanisms which modulate the characteristics of the acquired sEMG [32], making the learned mapping f θ (X) = y obsolete and thus degrading MCI performance over time [33]. Drift of this kind has in the past been mitigated either by including calibration data from a varied set of recording circumstances (although this approach has limitations regarding scalability [10]) or by using adaptive control strategies [34].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

Olsson

Malešević

Björkman

et al. 2021

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background Processing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration. Methods Forearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session. Results Metric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant ($$p<0.05$$ p < 0.05 ) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s $$d$$ d ) separating MRL from LDA ranging from $$\left|d\right|=0.62$$ d = 0.62 to $$\left|d\right|=1.13$$ d = 1.13 . No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission. Conclusions The results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

show abstract

Section: Calibration Of Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

Olsson

Malešević

Björkman

et al. 2021

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…ANN was used as an offline and online training and testing classifier [26]. The network was trained with the Levenberg-Marquardt algorithm.…”

Section: Wampmentioning

confidence: 99%

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

Waris

Rehman

Niazi

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.

show abstract

“…The primary goal of a MEC is to provide natural control of upper limb prostheses with multiple DOF [3]- [7]. The performance of PR-based MEC is dependent on multiple factors like the selection of features and classifiers.…”

Section: Introductionmentioning

confidence: 99%

Determination of Optimum Segmentation Schemes for Pattern Recognition-Based Myoelectric Control: A Multi-Dataset Investigation

et al. 2020

Self Cite

View full text Add to dashboard Cite

Pattern recognition (PR) algorithms have shown promising results for upper limb myoelectric control (MEC). Several studies have explored the efficacy of different pre and post processing techniques in implementing PR-based MECs. This paper explores the effect of segmentation type (disjoint and overlap) and segment size on the performance of PR-based MEC, for multiple datasets recorded with different recording devices. Two PR-based methods; linear discriminant analysis (LDA) and support vector machine (SVM) are used to classify hand gestures. Optimum values of segment size, step size and segmentation type were considered as performance measure for a robust MEC. Statistical analysis showed that optimum values of segment size for disjoint segmentation are between 250ms and 300ms for both LDA and SVM. For overlap segmentation, best results have been observed in the range of 250ms-300ms for LDA and 275ms-300ms for SVM. For both classifiers the step size of 20% achieved highest mean classification accuracy (MCA) on all datasets for overlap segmentation. Overall, there is no significant difference in MCA of disjoint and overlap segmentation for LDA (P-value = 0.15) but differ significantly in the case of SVM (P-value < 0.05). For disjoint segmentation, MCA of LDA is 88.68% and for SVM, it is 77.83%. Statistical analysis showed that LDA outperformed SVM for disjoint segmentation (P-value < 0.05). For overlap segmentation, MCA of LDA is 89.86% and for SVM, it is 89.16%, showing that statistically, there is no significant difference between MCA of both classifiers for overlap segmentation (P-value = 0.45). The indicated values of segment size and overlap size can be used to achieve better performance results, without increasing delay time, for a robust PR-based MEC system.

show abstract

On the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach

Cited by 22 publications

References 39 publications

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

Determination of Optimum Segmentation Schemes for Pattern Recognition-Based Myoelectric Control: A Multi-Dataset Investigation

Contact Info

Product

Resources

About