I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., 3, 1157–1182 (2003), https://doi.org/10.1162/153244303322753616.

Article  Google Scholar 

H. Banka and S. Dara, “A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation,” Pattern Recognit. Lett., 52, 94–100 (2015).

Article  Google Scholar 

R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” In: MHS’95. Proc. Sixth Int. Symp. Micro Machine Human Sci., IEEE, 39–43 (1995).

R. Boostani and M. H. Moradi, “Evaluation of the forearm EMG signal features for the control of a prosthetic hand,” Physiol. Meas., 24, No. 2, 309–319 (2003), https://doi.org/10.1088/0967-3334/24/2/307.

Article  PubMed  Google Scholar 

L. Hargrove, K. Englehart, and B. Hudgins, “A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control,” Biomed. Signal Process. Control, 3, No. 2, 175–180 (2008).

Article  Google Scholar 

H.-P. Huang, Y.-H. Liu, L.-W. Liu, and C.-S. Wong, “EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps,” Proc. IEEE Int. Robot. Autom., 1, 1497–1502 (2003), https://doi.org/10.1109/ROBOT.2003.1241803.

Article  Google Scholar 

M. Zardoshti-Kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, “EMG feature evaluation for movement control of upper extremity prostheses,” IEEE Trans. Rehabil. Eng., 3, No. 4, 324–333 (1995).

Article  Google Scholar 

A. Phinyomark, F. Quaine, S. Charbonnier, et al., “EMG feature evaluation for improving myoelectric pattern recognition robustness,” Expert Syst. Appl., 40, No. 12, 4832–4840 (2013), https://doi.org/10.1016/j.eswa.2013.02.023.

Article  Google Scholar 

K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., 21, No. 6–7, 431–438 (1999), https://doi.org/10.1016/s1350-4533(99)00066-1.

CAS  Article  PubMed  Google Scholar 

J.-U. Chu, I. Moon, and M.-S. Mun, “A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand,” IEEE Trans. Biomed. Eng., 53, No. 11, 2232–2239 (2006).

Article  Google Scholar 

K. E. Member, B. H. Member, P. A. Parker, and S. Member, “A wavelet based continuous classification scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng., 48, No. 3, 1–31 (2001).

Google Scholar 

H. Huang, F. Zhang, L. J. Hargrove, et al., “Continuous locomotion-mode identification for prosthetic legs based on neuromuscular--mechanical fusion,” IEEE Trans. Biomed. Eng., 58, No. 10, 2867–2875 (2011), https://doi.org/10.1109/TBME.2011.2161671.

R. Gupta and R. Agarwal, “Electromyographic signal-driven continuous locomotion mode identification module design for lower limb prosthesis control,” Arab. J. Sci. Eng. (2018), https://doi.org/10.1007/s13369-018-3193-3.

R. Gupta, I. S. Dhindsa, and R. Agarwal, “Continuous angular position estimation of human ankle during unconstrained locomotion,” Biomed. Signal Process. Control, 60, 101968 (2020).

R. Gupta and R. Agarwal, “Continuous human locomotion identification for lower limb prosthesis control,” CSI Trans. ICT, 6, 1–15 (2017), https://doi.org/10.1007/s40012-017-0178-4.

Article  Google Scholar 

H. Huang, T. A. Kuiken, and R. D. Lipschutz, “A strategy for identifying locomotion modes using surface electromyography,” IEEE Trans. Biomed. Eng., 56, No. 1, 65–73 (2009), https://doi.org/10.1109/TBME.2008.2003293.

Article  PubMed  PubMed Central  Google Scholar 

A. L. Delis, J. L. A. Carvalho, A. F. Da Rocha, et al., “Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses,” Physiol. Meas., 30, No. 9, 931–946 (2009), https://doi.org/10.1088/0967-3334/30/9/005.

Article  PubMed  Google Scholar 

F. Zhang, P. Li, Z.-G. Hou, et al., “sEMG-based continuous estimation of joint angles of human legs by using BP neural network,” Neurocomputing, 78, No. 1, 139–148 (2012).

Article  Google Scholar 

I. S. Dhindsa, R. Agarwal, and H. S. Ryait, “Performance evaluation of various classifiers for predicting knee angle from electromyography signals,” Expert Syst., 36, No. 3, e12381 (2019), https://doi.org/10.1111/exsy.12381.

C.-L. Huang and J.-F. Dun, “A distributed PSO–SVM hybrid system with feature selection and parameter optimization,” Appl. Soft Comput., 8, No. 4, 1381–1391 (2008).

Article  Google Scholar 

Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” Proc. 2001 Congr. Evol. Comput., 1, 81–86 (2001), https://doi.org/10.1109/CEC.2001.934374.

J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” 1997 IEEE Int. Conf. Sys., Man Cybern. Comput. Cybern. Simul., 5, 4104–4108 (1997), https://doi.org/10.1109/ICSMC.1997.637339.

I. S. Dhindsa, R. Agarwal, and H. S. Ryait, “Principal component analysis-based muscle identification for myoelectric-controlled exoskeleton knee,” J. Appl. Stat., 44, No. 10, 1707–1720 (2017), https://doi.org/10.1080/02664763.2016.1221907.

Article  Google Scholar 

B. Frericks, “The recommendations for sensors and sensor placement procedures for surface electromyography,” SENIAM Deliv. 8, Eur. Recomm. Surf. Electromyogr., 15–53 (1999).

L. J. Hargrove, G. Li, K. B. Englehart, and B. S. Hudgins, “Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control,” IEEE Trans. Biomed. Eng., 56, No. 5, 1407–1414 (2009), https://doi.org/10.1109/TBME.2008.2008171.

Article  PubMed  Google Scholar 

B. Hudgins, P. Parker, R. N. Scott, B. Hudgins, P. Parker, and R. Scott, “A new strategy for multifunction myoelectric control,” IEEE Trans. Biomed. Eng., 40, No. 1, 82–94 (1993), https://doi.org/10.1109/10.204774.

CAS  Article  PubMed  Google Scholar 

J. Virmani, V. Kumar, N. Kalra, and N. Khandelwal, “SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors,” J. Digit. Imaging, 26, No. 3, 530–543 (2013), https://doi.org/10.1007/s10278-012-9537-8.

Article  PubMed  Google Scholar 

A. Ganapathiraju, J. E. Hamaker, and J. Picone, “Application of support vector machines to speech recognition,” IEEE Trans. Signal Process., 52, No. 8, 2348–2355 (2004), https://doi.org/10.1109/TSP.2004.831018.

Article  Google Scholar 

S. Tong and D. Koller, “Support vector machine active learning with applications to text classification,” J. Mach. Learn. Res., 2, No. Nov, 45–66 (2001), https://doi.org/10.1162/153244302760185243.

S.-W. Lin, K.-C. Ying, S.-C. Chen, and Z.-J. Lee, “Particle swarm optimization for parameter determination and feature selection of support vector machines,” Expert Syst. Appl., 35, No. 4, 1817–1824 (2008), https://doi.org/10.1016/j.eswa.2007.08.088.

Article  Google Scholar 

M. Pardo and G. Sberveglieri, “Classification of electronic nose data with support vector machines,” Sensors Actuators B Chem., 107, No. 2, 730–737 (2005).

CAS  Article  Google Scholar 

S.-C. Chen, S.-W. Lin, and S.-Y. Chou, “Enhancing the classification accuracy by scatter-search-based ensemble approach,” Appl. Soft Comput., 11, No. 1, 1021–1028 (2011).

Article  Google Scholar 

C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification” (2003).

R. Gupta and R. Agarwal, “sEMG interface design for locomotion identification,” Int. J. Electr. Comput. Eng., 11, No. 2, 66–75 (2017).

Google Scholar