sEMG Interface Design for Locomotion Identification

Authors: Rohit Gupta, Ravinder Agarwal

Abstract:

Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: Classifiers, feature selection, locomotion, sEMG.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339664

References:

[1] O. R. Jimenez-Fabian, Verlinden, “Medical Engineering & Physics Review of control algorithms for robotic ankle systems in lower-limb orthoses , prostheses , and exoskeletons ଝ,” Med. Eng. Phys., vol. 34, no. 4, pp. 397–408, 2012.
[2] K. Veer, “A flexible approach for segregating physiological signals,” Meas. J. Int. Meas. Confed., vol. 87, pp. 21-26, 2016.
[3] H. S. Ryait, A. S. Arora, and R. Agarwal, “Interpretations of wrist/grip operations from SEMG signals at different locations on arm,” IEEE Trans. Biomed. Circuits Syst., vol. 4, no. 2, pp. 101–111, 2010.
[4] P. Geethanjali and K. K. Ray, “A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 4, pp. 1948–1955, 2014.
[5] E. L. Mercado-Medina, Z. D. Chavarro-Hernandez, J. A. Dominguez-Jimenez, and S. H. Contreras-Ortiz, “Design of an electronic system for monitoring muscle activity in weight-lifting,” in 2014 III International Congress of Engineering Mechatronics and Automation (CIIMA), 2014, pp. 1–4.
[6] M. S. Al-Quraishi, A. J. Ishak, S. A. Ahmad, and M. K. Hasan, “Multichannel EMG data acquisition system: Design and temporal analysis during human ankle joint movements,” in IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: “Miri, Where Engineering in Medicine and Biology and Humanity Meet,” 2015, no. December, pp. 338–342.
[7] F. N. Guerrero, E. M. Spinelli, and M. A. Haberman, “Analysis and Simple Circuit Design of Double Differential EMG Active Electrode,” IEEE Trans. Biomed. Circuits Syst., vol. 10, no. 3, pp. 787–795, 2016.
[8] A. Chatterjee, S. Gupta, S. Kumar, K. Garg, and A. Kumar, “An innovative device for instant measurement of Surface Electro-Myography for clinical use,” Meas. J. Int. Meas. Confed., vol. 45, no. 7, pp. 1893–1901, 2012.
[9] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Expert Systems with Applications Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012.
[10] K. Veer, T. Sharma, and R. Agarwal, “A neural network-based electromyography motion classifier for upper limb activities,” J. Innov. Opt. Health Sci., vol. 9, no. 6, pp. 1–8, 2016.
[11] D. Joshi and M. E. Hahn, “Terrain and Direction Classification of Locomotion Transitions Using Neuromuscular and Mechanical Input,” Ann. Biomed. Eng., vol. 44, no. 4, pp. 1275–1284, 2016.
[12] D. Farina et al., “The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 4, pp. 797–809, 2014.
[13] J. D. Miller, M. S. Beazer, and M. E. Hahn, “Myoelectric walking mode classification for transtibial amputees,” IEEE Trans. Biomed. Eng., vol. 60, no. 10, pp. 2745–2750, 2013.
[14] K. Veer and R. Agarwal, “Wavelet denoising and evaluation of electromyogram signal using statistical algorithm,” Int. J. Biomed. Eng. Technol., vol. 16, no. 4, pp. 293–305, 2014.
[15] A. Alkan and M. Günay, “Expert Systems with Applications Identification of EMG signals using discriminant analysis and SVM classifier,” Expert Syst. Appl., vol. 39, no. 1, pp. 44–47, 2012.
[16] A. J. Young, S. Member, A. M. Simon, N. P. Fey, and L. J. Hargrove, “Classifying the Intent of Novel Users During Human Locomotion Using Powered Lower Limb Prostheses,” in Annual International IEEE EMBS Conference on Neural Engineering, 2013, pp. 6–8.
[17] A. Phinyomark, A. Nuidod, P. Phukpattaranont, and C. Limsakul, “Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification,” vol. 6, no. 6, 2012.
[18] A. Fougner, Ø. Stavdahl, P. J. Kyberd, Y. G. Losier, and P. A. Parker, “Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control — A Review,” vol. 20, no. 5, pp. 663–677, 2012.
[19] A. J. Young, A. M. Simon, N. P. Eey, and L. J. Hargrove, “Intent Recognition in a Powered Lower Limb Prosthesis Using Time History Information,” Ann. Biomed. Eng., vol. 42, no. 3, pp. 631–641, 2014.
[20] B. Chen et al., “Locomotion Mode Classification Using a Wearable Capacitive Sensing System,” vol. 21, no. 5, pp. 744–755, 2013.
[21] A. J. Young, A. M. Simon, and L. J. Hargrove, “A Training Method for Locomotion Mode Prediction Using Powered Lower Limb Prostheses,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 671–677, 2014.
[22] B. Chen, X. Wang, Y. Huang, K. Wei, and Q. Wang, “A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution,” Mechatronics, vol. 32, pp. 12–21, 2015.
[23] K. Yuan, Q. Wang, and L. Wang, “Fuzzy-Logic-Based Terrain Identification with Multisensor Fusion for Transtibial Amputees,” IEEE Trans. Mechatronics, vol. 20, no. 2, pp. 618–630, 2015.
[24] A. J. Young and L. J. Hargrove, “A Classification Method for User - Independent Intent Recognition for Transfemoral Amputees using Powered Lower Limb Prostheses,” vol. 24, no. 2, pp. 217–225, 2016.
[25] S. Pati, D. Joshi, and A. Mishra, “Locomotion classification using EMG signal,” in 2010 International Conference on Information and Emerging Technologies, 2010, pp. 1–6.
[26] H. Huang, F. Zhang, Y. L. Sun, and H. He, “Design of a robust EMG sensing interface for pattern classification,” J. Neural Eng., vol. 7, no. 5, p. 56005, 2010.
[27] T. A. ; K. I. ; G. W. ; A. B. Wright;, “A Method for Locomotion Mode Identification Using Muscle Synergies,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. PP, no. 99, pp. 1–10, 2016.
[28] M. T. Farrell and H. Herr, “A method to determine the optimal features for control of a powered lower-limb prostheses,” in 33rd Annual International Conference of the IEEE EMBS, 2011, pp. 6041–6046.
[29] X. Zhang, D. Wang, Q. Yang, and H. Huang, “An Automatic and User-Driven Training Method for Locomotion Mode Recognition for Artificial Leg Control,” in 34th Annual International Conference of the IEEE EMBS, 2012, pp. 6116–6119.
[30] L. Du, F. Zhang, M. Liu, and H. Huang, “Toward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System,” IEEE Trans. Biomed. Eng., vol. 59, no. 10, pp. 2716–2725, 2012.
[31] F. Zhang and H. Huang, “Source Selection for Real-Time User Intent Recognition Toward Volitional Control of Artificial Legs,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 5, pp. 907–914, 2013.
[32] M. Liu, D. Wang, and H. H. Huang, “Development of an Environment-aware Locomotion Mode Recognition System for Powered Lower Limb Prostheses,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 4, pp. 434–443, 2016.
[33] J. A. Spanias, A. M. Simon, K. A. Ingraham, and L. J. Hargrove, “Effect of Additional Mechanical Sensor Data on an EMG - based Pattern Recognition System for a Powered Leg Prosthesis,” in IEEE EMBS Conference on Neural Engineering, 2015, pp. 22–24.
[34] M. E. Joshi, D., Hahn, “Terrain and Direction Classification of Locomotion Transitions Using Neuromuscular and Mechanical Input,” Ann. Biomed. Eng., vol. 44, no. 4, pp. 1275–1284, 2016.
[35] S. Pancholi, “Development of low cost EMG data acquisition system for Arm Activities Recognition,” in Intl. Conference on Advances in Computing, Communications and Informatics, 2016, pp. 2465–2469.
[36] T. Instruments and I. Snas, “ADS1293 Low-Power, 3-Channel, 24-Bit Analog Front-End for Biopotential Measurements,” 2014.
[37] R. N. Khushaba, A. Al-Timemy, S. Kodagoda, and K. Nazarpour, “Combined influence of forearm orientation and muscular contraction on EMG pattern recognition,” Expert Syst. Appl., vol. 61, pp. 154–161, 2016.
[38] Z. Li, B. Wang, C. Yang, and Q. Xie, “Boosting-Based EMG Patterns Classification Scheme for Robustness Enhancement,” IEEE J. Biomed. Heal. INFORMATICS, vol. 17, no. 3, pp. 545–552, 2013.
[39] J. Margarito, R. Helaoui, A. M. Bianchi, F. Sartor, and A. G. Bonomi, “User-independent recognition of sports activities from a single wrist-worn accelerometer: A template-matching-based approach,” IEEE Trans. Biomed. Eng., vol. 63, no. 4, pp. 788–796, 2016.
[40] G. Rasool, K. Iqbal, N. Bouaynaya, and G. White, “Real-time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 4320, no. c, pp. 1–10, 2015.
[41] D. Yang, J. Zhao, Y. Gu, L. Jiang, and H. Liu, “EMG Pattern Recognition and Grasping Force Estimation: Improvement to the Myocontrol of Multi-DOF Prosthetic Hands,” pp. 516–521, 2009.
[42] H. Huang, F. Zhang, L. J. Hargrove, Z. Dou, D. R. Rogers, and K. B. Englehart, “Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular – Mechanical Fusion,” IEEE Trans. Biomed. Eng., vol. 58, no. 10, pp. 2867–2875, 2011.
[43] E. N. Kamavuako, E. J. Scheme, K. B. Englehart, and A, “Determination of optimum threshold values for EMG time domain features; a multi-dataset investigation,” J. Neural Eng., vol. 13, no. 4, pp. 1–10, 2016.
[44] N. R. Hudgins, Bernard, Parker, Philip, Scott, “HuginsParker(1993) IEEEtransBME40(1)82-94.pdf,” IEEE Trans. Biomed. Eng., vol. 40, no. 1, pp. 82–94, 1993.
[45] Y.-C. Du, C.-H. Lin, L.-Y. Shyu, and T. Chen, “Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis,” Expert Syst. Appl., vol. 37, no. 6, pp. 4283–4291, 2010.