Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography

Journal article

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
AuthorsZhang, H., Peng, B., Tian, L., Samuel, O. and Li, G.
Abstract

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOF) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and
intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology,
celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation
method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF
finger kinematics simultaneously. The method's efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000
windows for training Kalman model parameters, the average computation time remains under 0.01 seconds. This pilot study amplifies its potential for further exploration and
application within the realm of continuous finger motion estimation technology.

KeywordsFinger Kinematics Tracking; hand motion intervention ; surface electromyography (sEMG) encounters
Year2024
JournalCyborg and Bionic Systems
Journal citationpp. 1-11
PublisherAmerican Association for the Advancement of Science
ISSN2692-7632
Digital Object Identifier (DOI)https://doi.org/10.34133/cbsystems.0094
Web address (URL)https://spj.science.org/doi/10.34133/cbsystems.0094
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online12 Jan 2024
Publication process dates
Deposited12 Feb 2024
Permalink -

https://repository.derby.ac.uk/item/q4952/continuous-kalman-estimation-method-for-finger-kinematics-tracking-from-surface-electromyography

Log in to edit

Download files

Publisher's version
cbsystems.0094.pdf
License: CC BY 4.0
File access level: Open

  • 29
    total views
  • 14
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

On the Use of Muscle Activation Patterns and Artificial Intelligence Methods for the Assessment of the Surgical Skills of Clinicians †
Samuel, O. 2024. On the Use of Muscle Activation Patterns and Artificial Intelligence Methods for the Assessment of the Surgical Skills of Clinicians †. Engineering Proceedings. 58 (1). https://doi.org/10.3390/ecsa-10-16231
Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies †
Samuel, O. 2024. Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies †. Engineering Proceedings. 58 (1). https://doi.org/10.3390/ecsa-10-16245
Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries
Karmani, P., Chandio, A. A., Korejo, I. A., Samuel, O. and Aborokbah, M. 2024. Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries. Peer J Computer Science . pp. 1-20. https://doi.org/10.7717/peerj-cs.2397
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
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
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems
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
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