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This article presents an electromyography (EMG) controlled hand exoskeleton for basic movements in assisted bilateral therapy, where bimanual work is required by the user. The target users are individuals with the right hand affected by an accident or cerebrovascular problems which require passive or assisted rehabilitation. Through a Matlab GUI, the system receives, processes and classifies electromyographic signals from the user acquired by a MYO armband obtaining an accuracy of 81.2% using k-Nearest Neighbors (kNN) as the classification algorithm and Random Subset Feature Selection (RSFS) as the feature selection algorithm. Subsequently, the exoskeleton reproduces the movement detected in the user’s opposite hand. The exoskeleton prototype is 8 degrees of freedom (DOF), built using 3D printing and has independent movement of the fingers. The movement controller is based on fuzzy logic. For the system performance analysis, kinematic information from a motion capture system is used to compare the trajectories in different grasping tasks of a user’s hand with and without the exoskeleton with a maximum error of 10.63% and a minimum of 3.46% with the desired final position, which physically represents a difference of 1.89° and 0.07° respectively.
Twórcy
  • Control and Robotics Laboratory, École de Technologie Supérieure, 1100 Notre-Dame StW, Montreal, Quebec H3C 1K3, Canada
  • Postgraduate Division, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, Mexico
  • Institute of Electronics and Mechatronics, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, Mexico
  • Institute of Electronics and Mechatronics, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, Mexico
Bibliografia
  • [1] Li M, Chen J, He G, Cui L, Chen C, Secco EL, Yao W, Xie J, Xu G, Wurdemann H. Attention enhancement for exoskeleton-assisted hand rehabilitation using fingertip haptic stimulation. Front Robot AI 2021;8:1–13. https://doi.org/10.3389/frobt.2021.602091.
  • [2] Dindorf R, Wos P. Using the bioelectric signals to control of wearable orthosis of the elbow joint with bi-muscular pneumatic servo-drive. Robotica 2020;38(5):804–18. https://doi.org/10.1017/S0263574719001097.
  • [3] Tinoco-Varela D, Gudiño-Peñaloza F, Villaseñor-Rodelas CJ. Characterized bioelectric signals by means of neural networks and wavelets to remotely control a human-machine interface. Sensors 2019;19(8):1–21. https://doi.org/10.3390/s19081923.
  • [4] Al-Quraishi MS, Elamvazuthi I, Daud SA, Parasuraman S, Borboni A. Eeg-based control for upper and lower limb exoskeletons and prostheses: A systematic review. Sensors 2018;18(10):3342. https://doi.org/10.3390/s18103342.
  • [5] Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, San-Tan R. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ecg signals. Appl Intell 2019;49(1):16–27. https://doi.org/10.1007/s10489-018-1179-1.
  • [6] Santos-Pessoa de Melo M, Gomes da Silva-Neto J, Natario-Teixeira JMX, Elza-Fontes Da Gama A, Teichrieb V. An emg-based virtual reality application for motor rehabilitation. In: Proceedings of 2019 21st Symposium on Virtual and Augmented Reality (SVR). p. 170–7. https://doi.org/10.1109/SVR.2019.00041.
  • [7] Armijo PR, Huang CK, High R, Leon M, Siu KC, Oleynikov D. Ergonomics of minimally invasive surgery: an analysis of muscle effort and fatigue in the operating room between laparoscopic and robotic surgery. Surg Endosc 2019;33 (7):2323–31. https://doi.org/10.1007/s00464-018-6515-3.
  • [8] Li M, He B, Liang Z, Zha CG, Chen J, Zhuo Y, Xu G, Xie J, Althoefer K. An attention-controlled hand exoskeleton for the rehabilitation of finger extension and flexion using a rigid-soft combined mechanism. Front Neurorobot 2019;13:1–13. https://doi.org/10.3389/fnbot.2019.00034.
  • [9] Karabulut D, Ortes F, Arslan YZ, Adli MA. Comparative evaluation of emg signal features for myoelectric controlled human arm prosthetics. Biocybern Biomed Eng 2017;37 (2):326–35. https://doi.org/10.1016/j.bbe.2017.03.001.
  • [10] Sánchez-Velasco LE, Arias-Montiel M, Guzmán-Ramírez E, Lugo-González E. A low-cost emg-controlled anthropomorphic robotic hand for power and precision grasp. Biocybern Biomed Eng 2020;40(1):221–37. https://doi.org/10.1016/j.bbe.2019.10.002.
  • [11] Wang N, Lao K, Zhang X. Design and myoelectric control of an anthropomorphic prosthetic hand. J Bionic Eng 2017;14(1):47–59. https://doi.org/10.1016/S1672-6529(16)60377-3.
  • [12] Altin C, Orhan E. Designing wearable joystick and performance comparison of emg classification methods for thumb finger gestures of joystick control. Biomed Res 2017;28(11):4730–6.
  • [13] Ali S, Samad M, Mehmood F, Ayaz Y, Qazi WM, Khan MJ, Asgher U. Hand gesture based control of nao robot using myo armband. In: Proceedings of Advances in Neuroergonomics and Cognitive Engineering, Springer. p. 449–57. https://doi.org/10.1007/978-3-030-20473-0_44.
  • [14] Shih-Chieh L, Fong-Gong W, Shu-Hsuanb F. Playing games with your mouth: Improving gaming experience with emg supportive input device. In: Proceedings of International Association of Societies of Design Research Conference.
  • [15] Carey SL, Lura DJ, Highsmith MJ. Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review. J Rehabil Res Dev 2015;52(3). https://doi.org/10.1682/JRRD.2014.08.0192.
  • [16] Jaramillo-Yanez A, Benalcazar ME, Mena-Maldonado E. Real-time hand gesture recognition using surface electromyography and machine learning: A systematic literature review. Sensors 2020;20(9):1–38. https://doi.org/10.3390/s20092467.
  • [17] Plessis T, Djouani K, Oosthuizen C. A review of active hand exoskeletons for rehabilitation and assistance. Robotics 2021;10(1):1–42. https://doi.org/10.3390/robotics10010040.
  • [18] Huang Y, Lai WP, Qian Q, Hu X, Tam EWC, Zheng Y. 14 - translation of robot-assisted rehabilitation to clinical service in upper limb rehabilitation. In: Hu X, editor. Intelligent Biomechatronics in Neurorehabilitation. Academic Press; 2020. p. 225–38. https://doi.org/10.1016/B978-0-12-814942-3.00014-3.
  • [19] Castiblanco JC, Mondragon IF, Alvarado-Rojas C, Colorado JD. Assist-as-needed exoskeleton for hand joint rehabilitation based on muscle effort detection. Sensors 2021;21(13):1–16. https://doi.org/10.3390/s21134372.
  • [20] Butzer T, Lambercy O, Arata J, Gassert R. Fully wearable actuated soft exoskeleton for grasping assistance in everyday activities. Soft Rob 2021;8(2):128–43. https://doi.org/10.1089/soro.2019.0135.
  • [21] Akgun G, Cetin AE, Kaplanoglu E. Exoskeleton design and adaptive compliance control for hand rehabilitation. Trans Inst Meas Control 2020;42(3):493–502. https://doi.org/10.1177/0142331219874976.
  • [22] Kaur A. Machine learning-based novel approach to classify the shoulder motion of upper limb amputees. Biocybern Biomed Eng 2019;39(3):857–67. https://doi.org/10.1016/j.bbe.2019.07.007.
  • [23] Liu H, Tao J, Lyu P, Tian F. Human-robot cooperative control based on semg for the upper limb exoskeleton robot. Robot Auton Syst 2020;125 . https://doi.org/10.1016/j.robot.2019.103350 103350.
  • [24] Reyes DA, Daniel Arteaga J, Barrera F. Classifying hand movement intentions using surface emg signals and svm. In: Proceedings of 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). p. 1–5. https://doi.org/10.1109/STSIVA.2019.8730264.
  • [25] Subasi A. Classification of emg signals using pso optimized svm for diagnosis of neuromuscular disorders. Comput Biol Med 2013;43(5):576–86. https://doi.org/10.1016/j.compbiomed.2013.01.020.
  • [26] Zhang Z, Yang K, Quian J, Zhang L. Real-time surface emg pattern recognition for hand gestures based on an artificial neural network. Sensors 2019;19(14):1–15. https://doi.org/10.3390/s19143170.
  • [27] Carlson D, Hsieh Y, Collins E, Carin L, Cevher V. Stochastic spectral descent for discrete graphical models. IEEE J Select Top Signal Process 2016;10(2):296–311. https://doi.org/10.1109/JSTSP.2015.2505684.
  • [28] Shao J, Niu Y, Xue C, Wu Q, Zhou X, Xie Y, Zhao X. Single-channel semg using wavelet deep belief networks for upper limb motion recognition. Int J Ind Ergon 2020;76 . https://doi.org/10.1016/j.ergon.2019.102905 102905.
  • [29] Onay F, Mert A. Phasor represented emg feature extraction against varying contraction level of prosthetic control. Biomed Signal Process Control 2020;59. https://doi.org/10.1016/j.bspc.2020.101881 101881.
  • [30] Parajuli N, Sreenivasan N, Bifulco P, Cesarelli M, Savino S, Niola V, Esposito D, Hamilton TJ, Naik GR, Gunawardana U, Gargiulo GD. Real-time emg based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation. Sensors 2019;19(20):1–30. https://doi.org/10.3390/s19204596.
  • [31] Benazzouz A, Guilal R, Amirouche F, Hadj Slimane ZE. Emg feature selection for diagnosis of neuromuscular disorders. In: Proceedings of 2019 International Conference on Networking and Advanced Systems (ICNAS). p. 1–5. https://doi.org/10.1109/ICNAS.2019.8807862.
  • [32] Shi WT, Lyu ZJ, Tang ST, Chia TL, Yang CY. A bionic hand controlled by hand gesture recognition based on surface emg signals: A preliminary study. Biocybern Biomed Eng 2018;38(1):126–35. https://doi.org/10.1016/j.bbe.2017.11.001.
  • [33] Jiang D, Li G, Sun Y, Kong J, Tao B, Chen D. Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using semg. Pers Ubiquit Comput 2019:1–10. https://doi.org/10.1007/s00779-019-01268-3.
  • [34] Li Z, Guan X, Zou K, Xu C. Estimation of knee movement from surface emg using random forest with principal component analysis. Electronics 2020;9(1):43. https://doi.org/10.3390/electronics9010043.
  • [35] Leonardis D, Barsotti M, Loconsole C, Solazzi M, Troncossi M, Mazzotti C, Castelli VP, Procopio C, Lamola G, Chisari C, Bergamasco M, Frisoli A. An emg-controlled robotic hand exoskeleton for bilateral rehabilitation. IEEE Trans Haptics 2015;8(2):140–51. https://doi.org/10.1109/TOH.2015.2417570.
  • [36] Bouteraa Y, Abdallah IB, Elmogy AM. Training of hand rehabilitation using low cost exoskeleton and vision-based game interface. J Intell Robot Syst 2019;96(1):31–47. https://doi.org/10.1007/s10846-018-0966-6.
  • [37] Arteaga MV, Castiblanco JC, Mondragon IF, Colorado JD, Alvarado-Rojas C. Emg-driven hand model based on the classification of individual finger movements. Biomed Signal Process Control 2020;58 . https://doi.org/10.1016/j.bspc.2019.101834 101834.
  • [38] Pardoel S, Doumit M. Development and testing of a passive ankle exoskeleton. Biocybern Biomed Eng 2019;39(3):902–13. https://doi.org/10.1016/j.bbe.2019.08.007.
  • [39] Hazam Majid MS, Khairunizam W, Shahriman A, Zunaidi I, Sahyudi B, Zuradzman M. Emg feature extractions for upper-limb functional movement during rehabilitation. In 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. 3; 2018. pp. 314–320. doi:10.1109/ICIIBMS.2018.8549932.
  • [40] Chen Y, Yag Z, Wen Y. A soft exoskeleton glove for hand bilateral training via surface emg. Sensors 2021;21(2):1–18. https://doi.org/10.3390/s21020578.
  • [41] Côté-Allard U, Campbell E, Phinyomar A, Laviolette F, Gosselin B, Scheme E. Interpreting deep learning features for myoelectric control: a comparison with handcrafted features. Front Bioeng Biotechnol 2020;8:1–22. https://doi.org/10.3389/fbioe.2020.00158.
  • [42] Burns MK, Pei D, Vinjamuri R. Myoelectric control of a soft hand exoskeleton using kinematic synergies. IEEE Trans Biomed Circuits Syst 2019;13(6):1351–61. https://doi.org/10.1109/TBCAS.2019.2950145.
  • [43] Zhang J, Dai J, Chen S, Xu G, Gao X. Design of finger exoskeleton rehabilitation robot using the flexible joint and the myo armband. In Yu H, Liu J, Liu L, Ju Z, Liu Y, Zhou D. (Eds.) Proceedings of Intelligent Robotics and Applications. Springer International Publishing; 2019. pp. 213–225.
  • [44] Li YC, Lin KC, Chen CL, Yao G, Chang YJ, Lee YY, Liu CT. Comparative efficacy study of robotic priming of bilateral approach in stroke rehabilitation. Front Neurol 2021;12:1–9. https://doi.org/10.3389/fneur.2021.658567.
  • [45] Hsu HY, Chiu HY, Kuan TS, Tsai CL, Su LC, Kuo FC. Robotic-assisted therapy with bilateral practice improves task and motor performance in the upper extremities of chronic stroke patients: A randomised controlled trial. Aust Occup Ther J 2019;66(5):637–47. https://doi.org/10.1111/1440-1630.12602.
  • [46] Chen PM, Kwong PWH, Lai CKY, Ng SSM. Comparison of bilateral and unilateral upper limb training in people with stroke: A systematic review and meta-analysis. PLoS ONE 2019;14(5):1–21. https://doi.org/10.1371/journal.pone.0216357.
  • [47] Sheng VN, Yanxin Z, Wei M, Chao D, Shengquan X. Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects. Med Eng Phys 2016;38(7):587–606. https://doi.org/10.1016/j.medengphy.2016.04.004.
  • [48] Gandolla M, Niero L, Molteni F, Guanziroli E, Ward NS, Predrocchi A. Brain plasticity mechanisms underlying motor control reorganization: Pilot longitudinal study on post-stroke subjects. Brain Sci 2021;11(3):1–16. https://doi.org/10.3390/brainsci11030329.
  • [49] Kim H, Miller LM, Fedulow I, Simkins M, Abrams GM, Byl N, Rosen J. Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system. IEEE Trans Neural Syst Rehabil Eng 2013;21(2):153–64. https://doi.org/10.1109/TNSRE.2012.2207462.
  • [50] De la Cruz-Sánchez BA, Arias-Montiel M, Lugo-González E. Development of hand exoskeleton prototype for assisted rehabilitation. In Gasparetto A, Ceccarelli M. (Eds.), Proceedings of Mechanism Design for Robotics. Springer International Publishing: Cham; 2019. pp. 378–385. doi:10.1007/978-3-030-00365-4_45.
  • [51] Cotugno G, Althoefer K, Nanayakkara T. The role of the thumb: Study of finger motion in grasping and reachability space in human and robotic hands. IEEE Trans Syst Man Cybern Syst 2017;47(7):1061–70. https://doi.org/10.1109/TSMC.2016.2531679.
  • [52] Nanayakkara VK, Cotugno G, Vitzilaios N, Venetsanos D, Nanayakkara T, Sahinkaya MN. The role of morphology of the thumb in anthropomorphic grasping: A review. Front Mech Eng 2017;3:5. https://doi.org/10.3389/fmech.2017.00005.
  • [53] Joseph Vedhagiri GJ, Prituja AV, Li C, Zhu G, Thakor NV, Ren H. Pinch grasp and suction for delicate object manipulations using modular anthropomorphic robotic gripper with soft layer enhancements. Robotics 2019;8(3).
  • [54] Takata SC, Wade ET, Roll SC. Hand therapy interventions, outcomes, and diagnoses evaluated over the last 10-years: A mapping review linking research to practice. J Hand Ther 2019;32(1):1–9. https://doi.org/10.1016/j.jht.2017.05.018.
  • [55] Ribeiro TH, Vieira M. Motion capture technology benefits and challenges. Int J Innov Res Technol Sci 2016;48(1). pp. 2321–1156.
  • [56] De la Cruz-Sanchez BA, Arias-Montiel M, Lugo-González E. Mocap database for three hand movements. Mendeley Data 2020;V2:1. https://doi.org/10.17632/y6r62t56km.1.
  • [57] Gülke J, Wachter NJ, Geyer T, Schöll H, Apic G, Mentzel M. Motion coordination patterns during cylinder grip analyzed with a sensor glove. J Hand Surg Am 2010;35(5):797–806. https://doi.org/10.1016/j.jhsa.2009.12.031.
  • [58] Sandoval Gonzalez O, Jacinto Villegas J, Herrera Aguilar I, Portillo Rodiguez O, Tripicchio P, Hernandez Ramos M, et al. Design and development of a hand exoskeleton robot for active and passive rehabilitation. Int J Adv Robot Syst 2016;13(66):1–12. https://doi.org/10.5772/62404.
  • [59] Basteris A, Nijenhuis SM, Stienen AHA, Buurke JH, Prange GB, Amirabdollahian F. Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review. J Neuroeng Rehabil 2014;11(1):111. https://doi.org/10.1186/1743-0003-11-111.
  • [60] Yue Z, Zhang X, Wang J. Hand rehabilitation robotics on poststroke motor recovery. Behav Neurol. doi:10.1155/2017/3908135.
  • [61] Ding J, Lin R-Z, Lin Z-Y. Service robot system with integration of wearable myo armband for specialized hand gesture human-computer interfaces for people with disabilities with mobility problems. Comput Electr Eng 2018;69:815–27. https://doi.org/10.1016/j.compeleceng.2018.02.041.
  • [62] De la Cruz-Sanchez BA, Arias-Montiel M, Lugo-González E. semg database of the myo bracelet for hand gestures. Mendeley Data 2019;V1:1. https://doi.org/10.17632/sg9kwnwcp6.1.
  • [63] Giannakopoulos T, Pikrakis A. Chapter 4 – audio features. In: Giannakopoulos T, Pikrakis A, editors. Introduction to Audio Analysis. Oxford: Academic Press; 2014. p. 59–103. https://doi.org/10.1016/B978-0-08-099388-1.00004-2.
  • [64] Phukpattaranont P, Thongpanja S, Anam K, Al-Jumaily A, Limsakul C. Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal. Med Biol Eng Comput 2018;56(12):2259–71. https://doi.org/10.1007/s11517-018-1857-5.
  • [65] Alpaydin E. Introduction to Machine Learning, Adaptive Computation and Machine Learning. MIT Press; 2010.
  • [66] Padmaja DL, Vishnuvardhan B. Comparative study of feature subset selection methods for dimensionality reduction on scientific data, in. In: Proceedings of 2016 IEEE 6th International Conference on Advanced Computing (IACC). p. 31–4. https://doi.org/10.1109/IACC.2016.16.
  • [67] Pohjalainen J, Räsänen O, Kadioglu S. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang 2015;29(1):145–71. https://doi.org/10.1016/j.csl.2013.11.004.
  • [68] Räsänen O, Pohjalainen J. Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech. In: Proceedings of 14th Annual Conference of the International Speech Communication Association (Interspeech). p. 210–4.
  • [69] De la Cruz-Sanchez BA, Schoen MP, Perez-Gracia A, Arias-Montiel M, Lugo-González E. Semg pattern recognition for assisted rehabilitation. Proceedings of XXI Congreso Mexicano de Robótica 2019;vol. 1000:186–91.
  • [70] Farah N, Talib MHN, Mohd Shah NS, Abdullah Q, Ibrahim Z, Lazi JBM, Jidin A. A novel self-tuning fuzzy logic controller based induction motor drive system: An experimental approach. IEEE Access 2019;7:68172–84. https://doi.org/10.1109/ACCESS.2019.2916087.
  • [71] Acikgoz H. Speed control of dc motor using interval type-2 fuzzy logic controller. Int J Intell Syst Appl Eng 2018;6(3):197–202. https://doi.org/10.18201/ijisae.2018644777.
  • [72] De la Cruz-Sánchez BA, Arias-Montiel M, Lugo-González E. Trajectory planning and fuzzy control of a hand exoskeleton for assisted rehabilitation. In: Proceedings of Seventh International Workshop on Medical and Service Robots (MESROB2020). Springer International Publishing; 2020. p. 3–11. https://doi.org/10.1007/978-3-030-58104-6_1.
  • [73] Iancu I. A mamdani type fuzzy logic controller. In: Dadios EP. (Ed.), Fuzzy Logic. IntechOpen: Rijeka; 2012, Ch. 16. pp. 325–350. doi:10.5772/36321.
  • [74] Gull MA, Bai S, Blicher JU, Stærmose TG. Design and performance evaluation of a hybrid hand exoskeleton for hand opening/closing. J Med Devices 2021;15(4):1–10. https://doi.org/10.1115/1.4052448.
  • [75] Moreno-San Juan V, Cisnal A, Fraile JC, Pérez-Turiel J, De-la Fuente E. Design and characterization of a lightweight underactuated raca hand exoskeleton for neurorehabilitation. Rob Auton Syst 2021;143 . https://doi.org/10.1016/j.robot.2021.103828 103828.
  • [76] Hsu TH, Chiang YC, Chan WT, Chen SJ. A finger exoskeleton robot for finger movement rehabilitation. Inventions 2017;2(3):1–8. https://doi.org/10.3390/inventions2030012.
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