Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array

Boschmann, Alexander; Platzner, Marco

doi:10.1109/brc.2013.6487548

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…Statistical models apply probability theory to learn patterns in data and are currently more often employed in position-aware prosthesis control research than deep learning neural network alternatives [3]. Some researchers have collected EMG data across multiple limb positions to inform statistical classifiers [9], [12], [13], [19], [20], while others have added positional information (quaternions or accelerometer data) to take limb orientation into account [9]- [13]. Statistical regressors have not been as extensively explored as classifiers in device control literature [24].…”

mentioning

confidence: 99%

Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

Williams

Shehata

Dawson

et al. 2022

IEEE Trans. Biomed. Eng.

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mentioning

confidence: 99%

Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

Williams

Shehata

Dawson

et al. 2022

IEEE Trans. Biomed. Eng.

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“…They proposed training the classifier with dynamic activities to reduce both of these effects. Boschmann and Platzner used a 96-channel highdensity electrode array and showed that training the classifier in multiple positions (three positions in their work) in combination with an increased number of EMG channels helped reduce the effect of limb position variation on classification accuracy [21].…”

Section: Introductionmentioning

confidence: 99%

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

108

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Abstract-Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using highdensity force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%-11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

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“…This indicates that appropriate feature representation makes the classification task a linear problem. Hence, recent trend seems to be towards classifiers that are simple to implement, fast to train, and meet real-time constraints, such as the liner discriminant analysis (LDA) [5], [49]- [51], support vector machines (SVM) [52]- [54], Extreme learning machines (ELM) [55], hidden Markov models (HMM) [56]- [58], random forest (RF), and k-nearest neighbors (kNN). Amongst these classifiers, the LDA scheme is the most widely adopted in the field of myoelectric control.…”

Section: E Movement Intent Decodingmentioning

confidence: 99%

Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects

et al. 2019

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Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future.

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Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array

Cited by 17 publications

References 22 publications

Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

High-density force myography: A possible alternative for upper-limb prosthetic control

Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects

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