PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Measurements comparison of finger joint angles in hand postures between an sEMG armband and a sensory glove

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study compares the simultaneous measurements of finger joint angles obtained with a myoelectric armband (Myo), composed of eight surface electromyography (sEMG) sensors mounted on an elastic support, and a data glove, equipped with ten flex sensor on metacarpal and proximal finger joints. The flexion angles of all finger joints in four hand postures, that is open hand, closed hand and grasping two 3D printed molds of different size, were measured with a manual goniometer, and used to create, for each finger joint, a linear model from the measurement of the corresponding flex sensor in an electronic glove, as well as a regression model from the simultaneous measurements of 8 sEMG sensors of the Myo armband. The regression models were extracted testing different algorithms from the Matlab Regression Learner Toolbox. The performance of the models of the two wearable devices were evaluated and compared, applying a standard test, taken from literature on sensory gloves to evaluate the repeatability, reproducibility and reliability of finger joint measurements. These results were also compared with those reported by published works that followed the same standard test, using data gloves based on different sensing technologies. This work aims to demonstrate that the sEMG armbands can be applied to register the static postures of each finger joint with almost the same accuracy of sensory gloves.
Twórcy
  • Department of Management and Law, University of Rome Tor Vergata, Roma, Italy
  • Department of Electronics Engineering, University of Rome Tor Vergata, via Politecnico 1, 00133 Roma, Italy
  • Department of Electronics Engineering, University of Rome Tor Vergata, Roma, Italy
Bibliografia
  • [1] Sbernini L, Quitadamo L, Riillo F, Di Lorenzo N, Gaspari AL, Saggio G. Sensory-Glove-Based Open Surgery Skill Evaluation. IEEE Trans Hum-Mach Syst 2017;48(2):213–8.
  • [2] Ricci M, Terribili M, Giannini F, Errico V, Pallotti A, Galasso C, et al. Wearable-based electronics to objectively support diagnosis of motor impairments in school-aged children. J Biomech 2019;83:243–52.
  • [3] Ahmed MA, Zaidan BB, Zaidan AA, Salih MM, Lakulu MMB, A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017, Sensors, 18(7), 2018, 2208.
  • [4] Cavallo P, Saggio G. Conversion of sign language to spoken sentences by means of a sensory glove. J Softw 2014;9 (8):2002–9.
  • [5] McCaw JCS, Yuen MC, Kramer-Bottiglio R. Sensory glove for dynamic hand proprioception and tactile sensing. Proceedings of the ASME Design Engineering Technical Conference 2018;2B. https://doi.org/10.1115/DETC2018-85703.
  • [6] Chuang W-C, Hwang W-J, Tai T-M, Huang D-R, Jhang Y-J. Continuous finger gesture recognition based on flex sensors. Sensors 2019;19(18):3986.
  • [7] Saggio G, Orengo G, Pallotti A, Errico V, Ricci M, Evaluation of an integrated sensory glove at decreasing joint flexion degree. In IEEE International Symposium on Medical Measurements and Applications (MeMeA), 11-13 June 2018, Rome, Italy.
  • [8] Kim MK et al. Soft-packaged sensory glove system for human-like natural interaction and control of prosthetic hands. NPG Asia Mater 2019;11(1):43.
  • [9] Junior JCVS, Torquato MF, Noronha DH, Silva SN, Fernandes MAC. Proposal of the tactile glove device. Sensors 2019;19 (22):5029.
  • [10] Sorgini F, et al., Neuromorphic vibrotactile stimulation of fingertips for encoding object stiffness in telepresence sensory substitution and augmentation applications, Sensors, 18(1), 2018, 261.
  • [11] D’Abbraccio J, et al., Haptic glove and platform with gestural control for neuromorphic tactile sensory feedback in medical telepresence, Sensors (Switzerland), 19(3), 2019, 641.
  • [12] Kortier HG, Sluiter VI, Roetenberg D, Veltink PH. Assessment of hand kinematics with inertial and magnetic sensors. J NeuroEng Rehabil 2014;11:70.
  • [13] Al-Timemy AH, Bugmann G, Escudero J, Outram N. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inf 2013;17(3):608–18.
  • [14] 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;38:126–35.
  • [15] Song W, Han Q, Lin Z, Yan N, Luo D, Liao Y, et al. Design of a flexible wearable smart sEMG recorder integrated gradient boosting decision tree based hand gesture recognition. IEEE Trans Biomed Circuits Syst 2019;13(6):1563–74.
  • [16] Leone F, Gentile C, Ciancio AL, Gruppioni E, Davalli A, Sacchetti R, et al. Simultaneous sEMG classification of hand/ wrist gestures and forces. Front Neurorobot 2019;13:42.
  • [17] Velasco LES, Montiel MA, Ramírez EG, González EL. A lowcost EMG-controlled anthropomorphic robotic hand for power and precision grasp. Biohcybern Biomed Eng 2020;40:221–37.
  • [18] Wahid MF, Tafreshi R, Al-Sowaidi M, Langari R. Subjectindependent hand gesture recognition using normalization and machine learning algorithms. J Comput Sci 2018;27:69–76.
  • [19] Jr Ding I, Lin RZ, Lin ZY. 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.
  • [20] Ngeo JG, Tamei T, Shibata T. Continuous and simultaneous estimation of finger kinematics using inputs from an EMGto-muscle activation model. J Neuroeng Rehabil 2014;11(122).
  • [21] Chen Chen, Guohong Chai, WeiChao Guo, Xinjun Sheng, Dario Farina and Xiangyang Zhu, Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses, J Neural Eng, 16(2), 2019.
  • [22] Hioki M, Kawasaki H, Estimation of finger joint angles from sEMG using a neural network including time delay factor and recurrent structure, Int Scholar Res Netw (ISRN) Rehabil, 2012;13, 604314.
  • [23] 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.
  • [24] Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AGM, Elsig S, et al. Electromyography data for non-invasive naturallycontrolled robotic hand prostheses. Nature 2014;1:605–10.
  • [25] Matran-Fernandez A, Rodríguez Martínez IJ, Poli R, et al. SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements. Sci Data 2019;6:186.
  • [26] Wang C, Guo W, Zhang H, Guo L, Huang C, Lin C. sEMG-based continuous estimation of grasp movements by long-short term memory network. Biomed Signal Process Control 2020;59 101774.
  • [27] Gijsberts A, Atzori M, Castellini C, Müller H, Caputo B. Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE Trans Neural Syst Rehabil Eng 2014;22(4):735–44.
  • [28] Wise S, Gardner W, Sabelman E, Valainis E, Wong Y, Glass K, et al. Evaluation of a fiber optic glove for semi-automated goniometric measurements. J Rehabil Res Dev 1990;27 (4):411–24.
  • [29] Saggio G, Orengo G. Flex sensor characterization against shape and curvature changes. Sensors Actuators A Phys 2018;273:221–31.
  • [30] Gentner R, Classen J. Development and evaluation of a lowcost sensor glove for assessment of human finger movements in neurophysiological settings. J Neurosci Methods 2009;178:138–47.
  • [31] Simone LK, Sundarrajan N, Luoc X, Jia Y, Kamper DG. A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 2007;160:335–48.
  • [32] Dipietro L, Sabatini AM, Dario P. Evaluation of an instrumented glove for hand-movement acquisition. J Rehabil Res Dev (JRRD) 2003;40(2):179–90.
  • [33] Chowdhury RH, Mamun BIR, Mohd Alauddin AB, Ashrif AAB, Kalaivani C, Chang TG. Surface electromyography signal processing and classification techniques. Sensors 2013;13:12431–66.
  • [34] Karabulut D, Ortes F, Arslan YZ, Adli MA. Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics. Biocybern Biomed Eng 2017;37:326–35.
  • [35] Naik GR, Selvan SE, Gobbo M, Acharyya A, Nguyen HT. Principal component analysis applied to surface electromyography: A comprehensive review. IEEE Access 2016;4:4025–37.
  • [36] Naik GR, Nguyen HT. Nonnegative matrix factorization for the identification of EMG finger movement: evaluation using matrix analysis. IEEE J Biomed Health Inf 2015;19(2):478–85.
  • [37] McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods, l 1996;1:30–46.
  • [38] Li K, Chen I-M, Yeo SH, Lim CK. Development of fingermotion capturing device based on optical linear encoder. J Rehabil Res Dev 2011;48(11):68–72.
  • [39] O’Flinn B, Sanchez JT, Tedesco S, Downes B, Connolly J, Condell J, et al. Novel smart glove technology as a biomechanical monitoring tool. Sens Transd 2015;193 (10):23–32.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c43cd9dd-29c4-4819-88be-00a65ffd5b58
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.