Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
A bionic hand with fine motor ability could be a favorable option for replacing the human hand when performing various operations. Myoelectric control has been widely used to recognize hand movements in recent years. However, most of the previous studies have focused on whole-hand movements, with only a few investigating subtler motions. The aim of this study was to construct a prototype system for recognizing hand postures with the aim of controlling a bionic hand by analyzing sEMG signals measured at the flexor digitorum superficialis and extensor digitorum muscles. We adopted multiple features commonly used in previous studies—mean absolute value, zero crossing, slope sign change, and waveform length—in the algorithm for extracting hand-posture features, and the k-nearest-neighbors (KNN) algorithm as the classifier to perform hand-posture recognition. The bionic hand was controlled by an Arduino microprocessor, which converted the signals received from the classification process that were fed to the servo motors controlling the bionic fingers. We constructed a two-channel sEMG pattern-recognition system that can identify human hand postures and control a homemade bionic hand to perform corresponding hand postures. The KNN approach was able to recognize four different hand postures with a classification accuracy of 94% in the online experiment by using the channel combination. Moreover, the experimental tests show that the bionic hand could faithfully imitate the hand postures of the human hand. This study has bridged the gap between the features of sEMG signals of fingers and the postures of a bionic hand.
Wydawca
Czasopismo
Rocznik
Tom
Strony
126--135
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
autor
- Department of Biomedical Engineering, Ming-Chuan University, Taoyuan, Taiwan
autor
- Department of Biomedical Engineering, Ming-Chuan University, Taoyuan, Taiwan
autor
- Department of Biomedical Engineering, Ming-Chuan University, Taoyuan, Taiwan
autor
- Department of Computer and Communication Engineering, Ming-Chuan University, Taoyuan, Taiwan
autor
- Department of Biomedical Engineering, Ming-Chuan University, 5 De Ming Rd., Gui Shan District, Taoyuan County 333, Taiwan
Bibliografia
- [1] Rahman SA, Leung MKH, Cho SY. Human action recognition employing negative space features. J Vis Commun Image Represent 2013;24:217–31.
- [2] Yang S, Premaratne P, Vial P. Hand gesture recognition: an overview. Proc. 2013 5th IEEE Int Conf. 2013. p. 2013.
- [3] Matrone GC, Cipriani C, Carrozza MC, Magenes G. Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis. J Neuroeng Rehabil 2012;9(1):40.
- [4] Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin- Bernard F, Laurillau Y. A feasibility study on the use of anthropometric variables to make muscle–computer interface more practical. Eng Appl Artif Intell 2013;26 (7):1681–8.
- [5] Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin- Bernard F, Laurillau Y. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 2013;40(12):4832–40.
- [6] Asghari Oskoei M, Hu H. Myoelectric control systems – a survey. Biomed Signal Process Control 2007;2(4):275–94.
- [7] Merletti R, Farina D. Surface EMG processing: introduction to the special issue. Biomed Signal Process Control 2008;3 (2):115–7.
- [8] Xing K, Yang P, Huang J, Wang Y, Zhu Q. A real-time EMG pattern recognition method for virtual myoelectric hand control. Neurocomputing 2014;136:345–55.
- [9] Khushaba RN, Kodagoda S, Takruri M, Dissanayake G. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 2012;39 (12):10731–8.
- [10] Kumar DK, Poosapadi Arjunan S, Singh VP. Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects. J Neuroeng Rehabil 2013;10:50.
- [11] Wang N, Chen Y, Zhang X. The recognition of multi-finger prehensile postures using LDA. Biomed Signal Process Control 2013;8(6):706–12.
- [12] Anam K, Khushaba RN, Al-Jumaily A. Two-channel surface electromyography for individual and combined finger movements. 35th Annual International Conference of the IEEE Proc. Engineering in Medicine and Biology Society (EMBC). 2013. p. 2013.
- [13] Mahdavi FA, Ahmad SA. Surface electromyography feature extraction based on wavelet transform. Int J Integr Eng 2012;4(3):1–7.
- [14] Khushaba RN, Takruri M, Miro JV, Kodagoda S. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features. Neural Netw 2014;55:42–58.
- [15] Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin- Bernard F, Laurillau Y. Feature extraction of the first difference of EMG time series for EMG pattern recognition. Comput Methods Programs Biomed 2014;117(2):247–56.
- [16] Alkan A, Günay M. Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 2012;39:44–7.
- [17] Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl 2012;39(8):7420–31.
- [18] Wołczowski A, Zdunek R. Electromyography and mechanomyography signal recognition: experimental analysis using multi-way array decomposition methods. Biocybern Biomed Eng 2017;37(1):103–13.
- [19] Chen X, Wang ZJ. Pattern recognition of number gestures based on a wireless surface EMG system. Biomed Signal Process Control 2013;8(2):184–92.
- [20] Wang N, Chen Y, Zhang X. Realtime recognition of multi- finger prehensile gestures. Biomed Signal Process Control 2014;13:262–9.
- [21] Tang X, Liu Y, Lv C, Sun D. Hand motion classification using a multi-channel surface electromyography sensor. Sensors 2012;12(2):1130–47.
- [22] Tsai AC, Luh JJ, Lin TT. A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition. Expert Syst Appl 2015;42(7):3327–41.
- [23] Purushothaman G, Ray KK. EMG based man-machine interaction – a pattern recognition research platform. Rob Auton Syst 2014;62(6):864–70.
- [24] Riillo F, Quitadamo LR, Cavrini F, Saggio G, Pinto CA, Pasto NC, et al. Evaluating the influence of subject-related variables on EMG-based hand gesture classification. Proc. 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA); 2014.
- [25] Mesa I, Rubio A, Tubia I, De No J, Diaz J. Channel and feature selection for a surface electromyographic pattern recognition task. Expert Syst Appl 2014;41(11):5190–200.
- [26] Riillo F, Quitadamo LR, Cavrini F, Gruppioni E, Pinto CA, Pastò NC, et al. Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees. Biomed Signal Process Control 2014;14:117–25.
- [27] Pan L, Zhang D, Liu J, Sheng X, Zhu X. Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals. Biomed Signal Process Control 2014;14:265–71.
- [28] Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 1993;40:82–94.
- [29] Motion Lab Systems. Retrieved from http://www.motion-labs.com/prod_emg_systems.html [16.04.15].
- [30] Manimegalai EHSD. Hand Gesture Recognition Based on Emg Signals Using ann. Int J Comput Appl 2013;3(2):31–9.
- [31] Castro MCF, Arjunan SP, Kumar DK. Selection of suitable hand gestures for reliable myoelectric human computer interface. Biomed Eng Online 2015;14:1–11.
- [32] Góra J, Szecówka PM, Wolczowski AR. Control of dexterous hand-algorithm implementation issues. 9th International Conference of the IEEE Proc. In Information Technology and Applications in Biomedicine (ITAB). 2009. p. 2009.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-64eef698-4d3d-409a-96f6-6d7c03d5ee45