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On-line Access: 2022-01-24
Received: 2020-10-26
Revision Accepted: 2022-04-22
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Citations: Bibtex RefMan EndNote GB/T7714
Gang LIU, Jing WANG. A relation spectrum inheriting Taylor series: muscle synergy and coupling for hand[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000578 @article{title="A relation spectrum inheriting Taylor series: muscle synergy and coupling for hand", %0 Journal Article TY - JOUR
继承泰勒级数的关系谱分析:手部肌肉协同与耦合西安交通大学机器人与智能系统研究所,中国西安市,710049 摘要:数学中有两种著名的函数分解方法:泰勒级数和傅里叶级数。傅里叶级数发展成为傅里叶频谱,用于信号分解和分析;而泰勒级数的求解需要已知具体函数表达式,所以其在工程领域很少被应用。本文使用树突网络发展了泰勒级数,构造了关系谱,并将其应用于模型或系统分解和分析。了解肌肉激活与手指运动之间的直观联系对于开发无需用户预训练的商业假肢至关重要。然而,由于人手的复杂性,该直观联系尚未被理解。本文使用关系谱分析了肌肉-手指系统。在手指运动中,一块肌肉同时驱动多个手指,多块肌肉同时驱动一个手指。因此,本研究聚焦于手部的肌肉协同与耦合。本文有两个主要贡献:(1)有关手部的发现有助于假肢手的设计;(2)关系谱使在线模型可读,从而统一了在线性能和离线结果。开源代码见https://github.com/liugang1234567/Gang-neuron。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[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 (8th 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 (5th 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 (5th 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 8th 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 Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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