Design and manufacturing of 15 DOF myoelectric controlled prosthetic hand

This study's main purpose is to manufacture a low-cost, highly functional, myoelectric signal-controlled prosthetic hand for amputees in developing countries below a certain economic level. In this study, a prosthetic hand with five fingers was modeled on 15-degree freedom, and an independent joint movement was achieved through the use of a separate motor actuator for each joint in the fingers. The hand of the prosthetic can therefore keep the objects in the best possible way. The prosthetic was produced by hand using PLA material on a 3D printer to reduce cost. Bioelectric signals provide the human-prosthetic hand interaction, i.e. identification of the form of hand gesture. With 97 percent progress, the classification of a human hand with the SVM algorithm has been achieved. The prosthetic hand's total cost is US$ 450. The hand was compared in terms of qualitative and quantitative performance metrics with other high-priced rivals and the findings were interpreted.

Keywords:

prosthetic hand, 3D Manufacturing, PLA, Myo-controlled-hand EMG, Supported vector machine, Gesture recognition,

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[1] World Health Organization.“World Report on Disability”. New York, NY: World Health Organization, 2011. https://www.who.int/disabilities/world_report/2011/report.pdf (24.09.2019).
[2] Hamner SR, Narayan VG, Donaldson, KM. “Designing for scale: Development of the remotion knee for global emerging markets”. Annals of Biomedical Engineering, 41(9), 1851-1859, 2013.
[3] Borne A, Porter A, Recicar J, Maxson, T and Montgomery C. “Pediatric Traumatic Amputations in the United States”. Journal of Pediatric Orthopaedics, 37(2), 104-107, 2017.
[4] Strait E. “Prosthetics in Developing Countries.” http://www.oandp.org/publications/resident/pdf/DevelopingCountries.pdf. (25.05.2010).
[5] Smith M. “Engadget-new bebionic hand almost doubles its grip-strength, steered by users’ electrical skin signals”. http://www.engadget.com/2012/09/07/bebionic-3-bionic-hand/ (24.09.2019).
[6] Webster G. “The bionic hand with the human touch”. https://edition.cnn.com/2013/02/01/tech/bionic-hand-ilimb-prosthetic/index.html. (24.09.2019).
[7] Lightbody S. “Design of an Articulated Thumb for a Low-Cost Prosthetic Hand”. Department of Engineering, Sweet Briar College, Lecturer Notes, Virginia, ABD.
[8] Marrero IC. “Hand Anatomy”. http://emedicene.medscape.com/article/1285060-overview. (01.01.2010).
[9] Cipriani C, Controzzi M, and Carrozza, MC. “Objectives, criteria and methods for the design of the SmartHand transradial prosthesis”. Robotica, 28(6), 919-927, 2010.
[10] Slade P, Akhtar A, Nguyen, M, Bretl, T. “Tact: Design and Performance of an Open-Source, Affordable, Myoelectric Prosthetic Hand”. 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington State Convention Center, Seattle, Washington, May 26-30, 2015.
[11] Stratasys Direct Manufacturing. “3D Printing Materials: Choosing the Right Material for Your Application”. https://docplayer.net/22763790-3d-printing-advanced-manufacturing-3d-printing-materials-choosing-the-right-material-for-your-application-stratasysdirect-com.html, (24.09.2019).
[12] Mcor Technologies, “How Paper-Based 3D Printing Works”. http://www.samhirota.com/uploads/8/3/4/4/8344944/mcor-wpeu-06092013_low.pdf (24.09.2019).
[13] Zhao Z. and Laperrière, L. “Adaptive Direct Slicing of the Solid Model for Rapid Prototyping”. International Journal of Production Research, 38(1), 69-83, 2000.
[14] Parasa V, Gopichand A, Shankar NVS, Rao KR. “Fabrication of low cost prosthetic arm with foamed fingers”. International Journal of Engineering Research & Science, 2(10),47-50, 2016.
[15] Mangezi A, Rosendo A, Howard M and Stopforth R. “Embroidered archimedean spiral electrodes for contactless prosthetic control”. International Conference on Rehabilitation Robotics, London, UK. 17-20 July 2017.
[16] Palli G, Melchiorri C, Vassura G, Scarcia U, Moriello L, Berselli G, Cavallo A, De Maria G, Natale C, Pirozzi S. “The dexmart hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping”. The International Journal of Robotics Research, 33(5), 799-824, 2014.
[17] Jacobsen SC, Inversen EK, Knutti DF, Johnson RT and Biggers KB. “Design of the Utah/MIT Dexterous Hand”. IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, 7-10 April 1986.
[18] Tarmizi, WFBW, Elamvazuthi, I, Begam, M. “Kinematic and dynamic modeling of a multi-fingered robot hand”. International Journal of Basic & Applied Sciences, 9(10), 2009.
[19] Eshak GA, Ahmed HM, Abdel Gawad EA. “Gender determination from hand bones length and volume using multidetector computed tomography: A study in Egyptian people”. Journal of Forensic and Legal Medicine, 18(6), 246-52,2011.
[20] Case,D. and Ross,AH. “Sex determination from hand and foot bone lengths”. Journal of Forensic Sciences, 52(2), 2007.
[21] Buryanov A, Kotıuk, V. “Proportions of hand segments”. International Journal of Morphology, 28(3), 755-758, 2010.
[22] Gustus A, Stillfried G, Visser J, Jörntell H, Van der Smagt, P. “Human hand modeling: Kinematics, dynamics, applications”. Biological Cybernetics, 106, 741-755, 2012.
[23] James N. Ingram, Konrad P. Körding, Ian S. Howard, Daniel MW.. “The statistics of natural hand movements”. Experimental Brain Research, 188(2), 223-236,2009.
[24] Buryanov, A. Kotıuk, V. “Proportions of hand segments”. The International Journal of Morphology, 28(3), 755-758, 2010.
[25] Belter JT, Segil JL, Dollar AM and Weir RF. “Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review”. Journal of Rehabilitation Research & Development, 50(5), 599-618, 2013.
[26] Touch Bionics. “i-limbTM ultra revolution”. ,http://www.touchbionics.com/sites/default/files/files/i-limb%20ultra%20revolution%20user%20manual%20September%202014.pdf (24.09.2019).
[27] RSL Steeper. “Prosthetics”. https://www.steepergroup.com/, (24.09.2019).
[28] Gibbard J, “Open Hand Project: A low cost Robitic Hand”. 2014. https://www.indiegogo.com/projects/the-open-hand-project-a-low-cost-robotic-hand#/, (24.09.2019).
[29] Parasa V, Gopichand A, Shankar NVS, Rao KR. “Fabrication of low cost prosthetic arm with foamed fingers”. International Journal of Engineering Research & Science, 2(10),47-50, 2016.
[30] Hudgins B, Parker P, Scott RN, “A new strategy for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering, 40, 82-94, 1993.
[31] Englehart K, Hugdins B, Parker P, Multifunction Control of Prostheses Using the Myoelectric Signal. Editors: Teodorescu, HNL, Jain LC. In Intelligent Systems and Technologies in Rehabilitation Engineering, New York, NY, USA, CRC Press, 2000.
[32] Englehart K, Hudgins B. “A robust, real-time control scheme for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering, 50, 848-854,2003.
[33] Ibrahimy MI, Khalifa OO, Ahsan MR. “EMG Motion pattern classification through design and optimization of neural network”. In Proceedings of the International Conference on Biomedical Engineering, Kuala Lumpur, Malaysia, 27-28 February 2012.
[34] Rajesh V, Kumar PR, Reddy DV. “SEMG based human machine ınterface for controlling wheel chair by using ANN”. In Proceedings of the International Conference of Control, Automation, Communication and Energy Conservation, Perundurai, Tamilnadu, India, 4-6 June 2009.
[35] Hudgins B, Parker P, Scott RN, “A new strategy for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering, 40, 82-94,1993.
[36] Englehart K, Hudgins B, Parker PA, Stevenson M, “Classification of the myoelectric signal using time-frequency based representations”. Medical Engineering & Physics, 21, 431-438, 1999.
[37] Hargrove LJ, Li G, Englehart KB, Hudgins BS. “Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control”. IEEE Transactions on Biomedical Engineering, 56, 1407-1414, 2009.
[38] Phinyomark A, Phukpattaranont P, Limsakul C. “Feature reduction and selection for EMG signal classification”. Expert Systems With Applications, 39, 7420-7431, 2012.
[39] Reaz MBI, Hussain MS, Mohd-Yasin F. “Techniques of EMG signal analysis: detection, processing, classification and applications”. Biological Procedures, 8(1), 11-35, 2006.
[40] Seniam Project Management Group. “SENIAM EMG protocol”. http://www.seniam.org/ (24.09.2019).
[41] Sang-Ho K, Jae-Hwan R, Byeong-Hyeon L, Deok-Hwan K. “Human identification using EMG signal based artificial neural network”. Journal of the Institute of Electronics and Information Engineers, 53(4), 142-148,2016.
[42] Belgacem N, Fournier R, Nait-Ali A and Bereksi-Reguig F. “A novel biometric authentication approach using ECG and EMG signals”. Journal of Medical Engineering & Technology, 39(4), 226-238, 2015.
[43] Englehart K, Hudgins B, Parker P, Stevenson M. “Time-Frequency Representation for Classification of the Transient Myoelectric Signal”. 20th Annual International Conference on Engineering in Medicine and Biology Society, Hong Kong, China, 06 August 2002.
[44] Englehart K. Signal Representation for Classification of the Transient Myoelectric Signal. Doctoral Thesis. University of New Brunswick, Fredericton, New Brunswick, Canada, 1998.
[45] Daud WMBW, Yahya AB, Horng CS, Sulaima MF, Sudirman R. “Features extraction of electromyography signals in time domain on biceps brachii muscle”. International Journal of Modeling and Optimization, 3(6),515-519, 2013.
[46] Phinyomark A, Phukpattaranont P, Limsakul C. “Feature reduction and selection for EMG signal classification”. Expert Systems With Applications, 39, 7420-7431,2012.
[47] Scheme E, Englehart K. “Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use”. Journal of Rehabilitation Research Development, 48, 643-659,2011.
[48] Jiang N, Dosen S, Muller KR, Farina D. “Myoelectric control of artificial limbs-Is there a need to change focus?”. IEEE Signal Processing Magazine, 29, 152-150,2012.
[49] Al-Mulla MR, Sepulveda F, Colley M. “A review of non-invasive techniques to detect and predict localised muscle fatigue”. Sensors, 11, 3545-3594,2011.
[50] Boca AD, Park DC. “Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time”. IEEE International Conference on Neural Networks and IEEE World Congress on Computational Intelligence, Orlando, FL, USA, 27 June-2 July 1994.
[51] Tsuji T, Ichinobe H, Ito K, Nagasaki, M. “Discrimination of forearm motions from EMG signals by error back propagation typed neural network using entropy”. Transactions of the Society of Instrument and Control Engineers, 29, 1213-1220,1993.
[52] Nan B, Fukuda O, Tsuji T. “EMG-based motion discrimination using a novel recurrent neural network”. The Journal of Intelligent Information Systems, 21, 113-126, 2003.
[53] Naik GR, Kumar DK, Weghorn H. “A comparison of ICA algorithms in surface EMG signal processing”. The Journal of Intelligent Information Systems, 6, 363-374, 2011.
[54] Khezri M, Jahed M. “A neuro-fuzzy inference system for semg-based identification of hand motion commands”. IEEE Transactions on Industrial Electronics, 58, 1952-1960, 2011.
[55] Subasi A, Yilmaz M, Ozcalik HR. “Classification of EMG signals using wavelet neural network”. Journal of Neuroscience Methods, 156, 360-367,2006.
[56] Christodoulou CI, Kaplanis PA, Murray V, Pattichis MS, Pattichis CS, Kyriakides T, “Multi-scale AM-FM analysis for the classification of surface electromyographic signals”. Biomedical Signal Processing and Control, 7, 265-269, 2012.
[57] Güler NF, Koçer, S. “Use of support vector machines and neural network in diagnosis of neuromuscular disorders”. The Journal of Medical Systems, 29, 271-284, 2005.
[58] Pal M, Foody GM. “Feature selection for classification of hyperspectral data by SVM”. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297-2307, 2010.
[59] Peker M. “A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM”. Journal of Medical Systems, 40, 116, 2016.
[60] Schölkopf B, Smola A, Williamson R, Bartlett. PL. “New support vector algorithms”. Neural Computation, 12, 1207-1245, 2000.
[61] Chih-Chung Chang, Chih-Jen Lin, LIBSVM: A library for support vector machines”. Journal ACM Transactions on Intelligent Systems and Technology (TIST), 2(3) 27, 2011.
[62] Anand M., “Multi Class Support Vector Machine, MathWorks File Exchange”. https://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/(01.01.2020).
[63] Kılıç S, “Klinik karar vermede ROC analizi”. Journal of Mood Disorders, 3(3), 2013.
[64] Pylatiuk C, Schulz S, and Doderlein L. “Results of an internet survey of myoelectric prosthetic hand users”. Prosthetics and Orthotics International, 31(4, 362-70, 2007.
[65] Ibrahimy MI, Khalifa OO, Ahsan MR. “EMG motion pattern classification through design and optimization of neural network”. In Proceedings of the International Conference on Biomedical Engineering, Kuala Lumpur, Malaysia, 27-28 February 2012.
[66] Rajesh V, Kumar PR, Reddy DV, “SEMG based human machine ınterface for controlling wheel chair by using ANN”. The International Conference of Control, Automation, Communication and Energy Conservation, Perundurai, Tamilnadu, India, 4-6 June 2009.
[67] Tsuji T, Ichinobe H, Ito K, Nagamachi, M. “Discrimination of forearm motions from EMG signals by error back propagation typed neural network using entropy”. Transactions of the Society of Instrument and Control Engineers, 29, 1213-1220,1993.
[68] Khezri M, Jahed M. “A neuro-fuzzy inference system for semg-based identification of hand motion commands”. IEEE Transactions on Industrial Electronics, 58, 1952-1960, 2011.
[69] Shenoy P, Miller KJ., Crawford B, Rao RN, “Online electromyographic control of a robotic prosthesis”. IEEE Transactions on Biomedical Engineering, 55, 1128-1135, 2008.
[70] Naik GR, Kumar DK, Palaniswami M. “Multi Run ICA and Surface EMG Based Signal Processing System for Recognising Hand Gestures”. IEEE International Conference on Computer and Information Technology, Sydney, Australia, 8-11 July 2008.
[71] Chen X, Wang Z J. “Pattern recognition of number gestures based on a wireless surface EMG system”. Biomedical Signal Processing and Control, 8(2), 184-192,2013.
[72] Eldin HSD, Manimegalai P. “Hand gesture recognition based on EMG signals using ANN”. International Journal of Computer Application, 3(2), 31-39, 2013.