A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

Conference paper


Kulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G. 2023. A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems. 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171910
AuthorsKulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G.
TypeConference paper
Abstract

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

KeywordsPattern Recognition; Upper Limb Prostheses; Electromyogram (EMG); Electrode Shift
Year2023
Conference2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/MeMeA57477.2023.10171910
Web address (URL)https://ieeexplore.ieee.org/abstract/document/10171910
Publisher's version
License
All rights reserved
File Access Level
Restricted
Journal citationpp. 1-6
ISBN9781665493840
Web address (URL) of conference proceedingshttps://memea2023.ieee-ims.org/
File
File Access Level
Restricted
Output statusPublished
Publication dates
Online10 Jul 2023
Publication process dates
Accepted20 Mar 2023
Deposited03 Oct 2023
Permalink -

https://repository.derby.ac.uk/item/q1vzy/a-novel-duo-stage-driven-deep-neural-network-approach-for-mitigating-electrode-shift-impact-on-myoelectric-pattern-recognition-systems

  • 15
    total views
  • 1
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach
Ahmad, I., Liu, Z., Li, L., Ullah, I., Wang, X., Samuel, O., Li, G., Tao, Y., Chen, Y. and Chen, S. 2024. Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach. IEEE Journal of Biomedical and Health Informatics. pp. 1-13. https://doi.org/10.1109/JBHI.2024.3396130
A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization
Samuel, O., Asogbon, M. and McEwan, A. 2024. A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization. Biomedical Signal Processing and Control. 95 (2024), p. 106446. https://doi.org/https://doi.org/10.1016/j.bspc.2024.106446
Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Kandwal, A., Li, J., Kulwa, F., Samuel, O. and Nie, Z. 2024. Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE Transactions on Instrumentation and Measurements. 73, pp. 1-8. https://doi.org/10.1109/TIM.2024.3400333
A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees
Nsugbe, E., Samuel, O., Asogbon, M. and Jose, J. R. L. 2024. A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees. Biomedical Engineering Advances. 7 (2024), pp. 1-10. https://doi.org/10.1016/j.bea.2024.100117
Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657
Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography
Zhang, H., Peng, B., Tian, L., Samuel, O. and Li, G. 2024. Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography. Cyborg and Bionic Systems. pp. 1-11. https://doi.org/10.34133/cbsystems.0094
An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection
Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., Zhao, G., Samuel, O. and Chen, S. 2023. An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection. Journal of Information Security and Applications. 80, pp. 1-17. https://doi.org/10.1016/j.jisa.2023.103654
Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters
Awomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H. 2023. Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters. City and Environment Interactions. 20, pp. 1-12. https://doi.org/10.1016/j.cacint.2023.100127
A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition
Kulwa, F., Zhang, H., Samuel, O., Asogbon, M., Scheme, E., Kushaba, R., McEwan, A. and Li, G. 2023. A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition. IEEE Transactions on Human-Machine Systems. pp. 1-12. https://doi.org/10.1109/THMS.2023.3329536
Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor
Asogbon, M., Huai, Y., Samuel, O., Jing, Z., Ma, Y., Liu, J., Jiang, Y., Fu, Y., Li, G. and Li, Y. 2023. Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE Xplore. https://doi.org/https://doi.org/10.1109/EMBC40787.2023.10340688
An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals
Zangene, A., Samuel, O., Abbasi, A., Nazarpour, K., McEwan, A. and Li, G. 2023. An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340791
Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors
Samuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G. 2023. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340683
Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Samuel, O.W., Li, J., Kulwa, F., Kandwal, A. and Nie, Z. 2023. Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE International Workshop on Medical Measurement and Applications (MEMEA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171941
An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running
Zangene, A. R., Samuel, O., Abbasi, A., McEwan, A., Asogbon, M. G., Li, G. and Nazarpour, K. 2023. An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running. Biomedical Signal Processing and Control. 86 (B), pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105103
A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution
Li, F., Han, F., Wang, L., Huang, L., Samuel, O.W., Zhao, H., Xie, R., Wang, P., Tian, Q., Li, Q., Zhao, Y., Yu, Mei, Sun, J., Yang, R., Zhou, X., Li, F., Li, G., Lu, Y., Guo, P. and Liu, Z. 2023. A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution. Advanced Functional Materials. 2300859, pp. 1-9. https://doi.org/10.1002/adfm.202300859
On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines
Nsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O. 2023. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthcare Technology Letters. 10 (1-2), pp. 11-22. https://doi.org/10.1049/htl2.12044
Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition
Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G. 2023. Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition. Nature Scientific Data. 10 (358), pp. 1-16. https://doi.org/10.1038/s41597-023-02263-3
A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals
Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S. 2023. A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2023.3265983