Similar articles

Abstract: There are two famous function decomposition methods in math: the taylor series and the Fourier series. The Fourier series developed into the Fourier spectrum, which was applied to signal decomposition and analysis. However, because the taylor series function cannot be solved without a definite functional expression, it has rarely been used in engineering. We developed a taylor series using our proposed dendrite net (DD), constructed a relation spectrum, and applied it to decomposition and analysis of models and systems. Specifically, knowledge of the intuitive link between muscle activity and finger movement is vital for the design of commercial prosthetic hands that do not need user pre-training. However, this link has yet to be understood due to the complexity of the human hand. In this study, the relation spectrum was applied to analyze the muscle–finger system. One single muscle actuates multiple fingers, or multiple muscles actuate one single finger simultaneously. Thus, the research was focused on muscle synergy and muscle coupling for the hand. The main contributions are twofold: (1) The findings concerning the hand contribute to the design of prosthetic hands; (2) The relation spectrum makes the online model human-readable, which unifies online performance and offline results. Code is available at https://github.com/liugang1234567/Gang-neuron.

继承泰勒级数的关系谱分析：手部肌肉协同与耦合

[1]Abramowitz M, Stegun IA, 1972. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. National Bureau of Standards, Washington, DC, USA, p.1076.

[2]Atzori M, Cognolato M, Müller H, 2016. Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorob, 10:9. doi: 10.3389/fnbot.2016.00009

[3]Bracewell RN, 1978. The Fourier Transform and Its Applications. McGraw-Hill, New York, USA.

[4]Brown CY, Asada HH, 2007. Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2877-2882. doi: 10.1109/IROS.2007.4399547

[5]Devore JL, 2011. Probability and Statistics for Engineering and the Sciences (8^th Ed.). Cengage Learning, p.768.

[6]Farina D, Jiang N, Rehbaum H, et al., 2014. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Trans Neur Syst Rehabil Eng, 22(4):797-809. doi: 10.1109/TNSRE.2014.2305111

[7]Hahne JM, Bießmann F, Jiang N, et al., 2014. Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neur Syst Rehabil Eng, 22(2):269-279. doi: 10.1109/TNSRE.2014.2305520

[8]He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770-778. doi: 10.1109/CVPR.2016.90

[9]Hornik K, Stinchcombe M, White H, 1989. Multilayer feedforward networks are universal approximators. Neur Netw, 2(5):359-366. doi: 10.1016/0893-6080(89)90020-8

[10]Jiang N, Englehart KB, Parker PA, 2009. Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal. IEEE Trans Biomed Eng, 56(4):1070-1080. doi: 10.1109/TBME.2008.2007967

[11]Kutner MH, Nachtsheim CJ, Neter J, et al., 2005. Applied Linear Statistical Models (5^th Ed.). McGraw-Hill, New York, USA, p.716.

[12]Kuzborskij I, Gijsberts A, Caputo B, 2012. On the challenge of classifying 52 hand movements from surface electromyography. Annual Int Conf of the IEEE Engineering in Medicine and Biology Society, p.4931-4937. doi: 10.1109/EMBC.2012.6347099

[13]Lang CE, Schieber MH, 2004. Human finger independence: limitations due to passive mechanical coupling versus active neuromuscular control. J Neurophysiol, 92(5):2802-2810. doi: 10.1152/jn.00480.2004

[14]Liu G, 2020. It may be time to improve the neuron of artificial neural network. doi: 10.36227/techrxiv.12477266

[15]Liu G, Wang J, 2021. Dendrite net: a white-box module for classification, regression, and system identification. IEEE Trans Cybern, early access. doi: 10.1109/TCYB.2021.3124328

[16]Malesevic N, Björkman A, Andersson GS, et al., 2020. A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions. Sci Data, 7(1):10. doi: 10.1038/s41597-019-0335-8

[17]Ngeo JG, Tamei T, Shibata T, 2014. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J Neuroeng Rehabil, 11(1):122. doi: 10.1186/1743-0003-11-122

[18]Oskoei MA, Hu HS, 2007. Myoelectric control systems—a survey. Biomed Signal Process Contr, 2(4):275-294. doi: 10.1016/j.bspc.2007.07.009

[19]Parker P, Englehart K, Hudgins B, 2006. Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol, 16(6):541-548. doi: 10.1016/j.jelekin.2006.08.006

[20]Perotto AO, 2011. Anatomical Guide for the Electromyographer: the Limbs and Trunk (5^th Ed.). Charles C Thomas Publisher.

[21]Poggio T, 1975. On optimal nonlinear associative recall. Biol Cybern, 19(4):201-209. doi: 10.1007/BF02281970

[22]Schielzeth H, 2010. Simple means to improve the interpretability of regression coefficients. Methods Ecol Evol, 1(2):103-113. doi: 10.1111/j.2041-210X.2010.00012.x

[23]Tolstov GP, 2012. Fourier Series. Courier Corporation, North Chelmsford, MA, USA.

[24]van Loan C, 1992. Computational Frameworks for the Fast Fourier Transform. SIAM. doi: 10.1137/1.9781611970999

[25]Wu Y, Lin JY, Huang TS, 2001. Capturing natural hand articulation. Proc 8^th IEEE Int Conf on Computer Vision, 426-432. doi: 10.1109/ICCV.2001.937656

[26]Zhuang KZ, Sommer N, Mendez V, et al., 2019. Shared human–robot proportional control of a dexterous myoelectric prosthesis. Nat Mach Intell, 1(9):400-411. doi: 10.1038/s42256-019-0093-5