Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping

Powar, Omkar S; Chemmangat, Krishnan

doi:10.1016/j.bspc.2019.101626

Cited by 11 publications

(11 citation statements)

References 11 publications

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“…Mukhopadhyay et al [153] achieved 98.9%, 98.7%, 90.6%, 91.8%, and 88.4% accuracy across 5 positions using a DNN, SVM, kNN, RF and DT with the TDPSD feature set. Power et al [154] determined dynamic time waping (DTW) of the RMS value of the signal yielded higher accuracy and lower computational cost than the TDPSD feature set . Liu et al [155] used a linear-nonlinear cascade regression to simultaneously estimate shoulder, elbow, and wrist joint angles accounting for 93%, 90%, and 84% of the variance in able-bodied subjects, and 85%, 91% and 85% of the variance in stroke subjects, respectively.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Mukhopadhyay et al [157] achieved 98.9 %, 98.7%, 90.6%, 91.8%, and 88.4% accuracy across 5 positions using a DNN, SVM, kNN, RF and DT with the TDPSD feature set. Power et al [158] determined dynamic time waping (DTW) of the RMS value of the signal yielded higher accuracy and lower computational cost than the TDPSD feature set . Liu et al [159] used a linear-nonlinear cascade regression to simultaneously estimate shoulder, elbow, and wrist joint angles accounting for 93%, 90%, and 84% of the variance in able-bodied subjects, and 85%, 91% and 85% of the variance in stroke subjects, respectively.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Preprint

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Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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“…We presented a new hand-crafted feature extraction algorithm that borrows concepts from deep learning models and mixes these with the spatial information concept implemented by DTW. DTW was previously utilised in the literature of myoelectric control to compare training and testing templates [51][52][53], but it was not used to capture the spatial similarity concurrent with temporal information. The proposed STW feature was achieved by mixing spatial information with a long and short-term memory component with an attention normalization step.…”

Section: Discussionmentioning

confidence: 99%

“…All of this make DTW an attractive algorithmic choice for our analysis in this paper. However, previous research utilising DTW in myoelectric control only considered time-series similarity across training and testing EMG sequences or templates from individual channels [51][52][53]. In comparison, we utilise DTW as a spatial feature extraction step to calculate the warped similarity between multiple EMG channels and then temporally evolve the estimated similarity across time while considering long and short-term memories.…”

Section: Methods 200 Msmentioning

confidence: 99%

Spatio-temporal warping for myoelectric control: an offline, feasibility study

2021

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Objective. The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach. Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals. Main results. Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5%–17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNNs), STW outperformed by 5%–18%. Also, STW showed enhanced performance when compared to the CNN + LSTM model by 2%–14%. All differences were statistically significant with a large effect size. Significance. This feasibility study provides evidence supporting the hypothesis that the STW feature of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

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Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping

Cited by 11 publications

References 11 publications

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Spatio-temporal warping for myoelectric control: an offline, feasibility study

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