Abstract
Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).
Acknowledgments
We would like thank Prof. Teodiano Bastos UFES Brazil and his research team and for assisting in the data collecting from the amputee participants.
References
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©2016 by De Gruyter
Articles in the same Issue
- Frontmatter
- Editorial
- Biosignal processing
- Review
- A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals
- Research articles
- Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms
- Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation
- P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference
- Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients
- A portable device for recording evoked potentials, optimized for pattern ERG
- Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier
- Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal
- Nonlinear analysis of pupillary dynamics
- A multichannel bioimpedance monitor for full-body blood flow monitoring
- Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model
- Short communication
- Quantifying the complexity of human colonic pressure signals using an entropy measure
Articles in the same Issue
- Frontmatter
- Editorial
- Biosignal processing
- Review
- A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals
- Research articles
- Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms
- Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation
- P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference
- Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients
- A portable device for recording evoked potentials, optimized for pattern ERG
- Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier
- Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal
- Nonlinear analysis of pupillary dynamics
- A multichannel bioimpedance monitor for full-body blood flow monitoring
- Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model
- Short communication
- Quantifying the complexity of human colonic pressure signals using an entropy measure