OPTICAL FEEDBACK BASED ON THE PHOTOMETRYBY ELLIPSOIDAL REFLECTOR IN BIONIC FINGERS APPLICATION
DOI:
https://doi.org/10.20535/kpi-sn.2019.3.175785Keywords:
Bionic finger, Optical feedback, Ellipsoidal reflector, Artificial neural network, Finger prosthesisAbstract
Background. The investigation focus is the specificity of effective recognition of surface types during the interaction between finger prosthesis and manipulation objects, and organization of optical feedback system in the control module of bionic limb.
Objective. The purpose of the paper is development and testing of method of optical feedback organization in the systems of bionic prostheses of human hand fingers.
Methods. The developed system of optical feedback is based on the microcontroller measuring device with infrared (IR) optical emitter and sensor, ellipsoidal reflector, and artificial neural network.
Results. During the research the comparison of application effectivity of optical feedback system with ellipsoidal reflector and without one was performed. The analysis was performed by classification of set with twelve, eleven, and ten types of surfaces of investigated specimens with the means of artificial neural network. The obtained accuracy of surface recognition for the system without an ellipsoidal reflector was 77%, 82% and 87%. In turn, the accuracy of surface recognition in the application of the ellipsoidal reflector was 94%, 98%, and 100%, correspondingly. Such results prove the possibility of further use of the photometry system by ellipsoidal reflectors for organization of constructive parts or complete feedback modules of the bionic fingers.
Conclusions. The organization system of optical feedback based on optical emitter and sensor, ellipsoidal reflectors and artificial neural network for recognition of kinds of separate surfaces, with which can interact bionic prostheses fingers is proposed. The proposed system proved the certainty in recognition of limited set of investigated specimens. The efficiency of such system can be improved by using sensors array and wider data set for training.References
H. Liu et al., “Artificial eye for scotopic vision with bioinspired all-optical photosensitivity enhancer”, Proceedings of the National Academy of Sciences, vol. 113, no. 1, pp. 3982–3985, 2016. doi: 10.1073/pnas.1517953113
J.E. Fitzgerald et al., “Artificial nose technology: Status and prospects in diagnostics”, Trends Biotechnol., vol. 35, no. 1, pp. 33–42, 2017. doi: 10.1016/J.TIBTECH.2016.08.005
С. Dietrich et al., “Leg prosthesis with somatosensory feedback reduces phantom limb pain and increases functionality”, Frontiers Neurol., vol. 9, 2018. doi: 10.3389/fneur.2018.00270
K. Koganezawa and A. Ito, “Artificial hand with stiffness adjuster”, Intelligent Autonomous Systems, vol. 13, pp. 1079–1093, 2016. doi: 10.1007/978-3-319-08338-4_78
K. Vonsevych et al., “Information-measuring system of myograph of bionic limb prosthesis”, Perspektyvni Tekhnolohii ta Prilady, vol. 10, no. 1, pp. 32–37, 2017 (in Ukrainian).
P.S. Ramaiah et al., “A microcontroller based four fingered robotic hand”, Int. J. Artificial Intelligence & Applications, vol. 2, no. 2, pp. 90–102, 2011. doi: 10.5121/ijaia.2011.2207
N.M. Kakoty and S.M. Hazarika, “Bio-signals controlled prosthetic hand”, in Proc. National Conference on Design and Manufacturing Issues in Automotive and Allied Industries, IPRoMM, 2009, pp. 1–12.
С. Cipriani et al., “On the shared control of an EMG-controlled prosthetic hand: analysis of user–prosthesis interaction”, IEEE Trans. Robotics, vol. 24, no. 1, pp. 170–184, 2008. doi: 10.1109/TRO.2007.910708
Z. Kappassov et al., “Tactile sensing in dexterous robot hands”, Robotics and Autonomous Systems, vol. 74, pp. 195–220, 2015. doi: 10.1016/j.robot.2015.07.015
J.D. Brown et al., “Understanding the role of haptic feedback in a teleoperated/prosthetic grasp and lift task”, in Proc. World Haptics Conference, 2013, pp. 271–276. doi: 10.1109/WHC.2013.6548420
A. Gailey et al., “Proof of concept of an online EMG-based decoding of hand postures and individual digit forces for prosthetic hand control”, Frontiers Neurol., vol. 8, no. 1, pp. 1–15, 2017. doi: 10.3389/fneur.2017.00007
B. Peerdeman et al., “Myoelectric forearm prostheses: state of the art from a user-centered perspective”, J. Rehabilitation Research & Development, vol. 48, no. 6, pp. 719–737, 2011. doi: 10.1682/JRRD.2010.08.0161
С. Antfolk et al., “Using EMG for real-time prediction of joint angles to control a prosthetic hand equipped with a sensory feedback system”, J. Med. Bio. Eng., vol. 30, no. 6, pp. 399–406, 2010. doi: 10.5405/jmbe.767
A. Persichetti et al., “Optoelectronic-based flexible contact sensor for prosthetic hand application”, IEEE 10th Int. Conf. Rehabilitation Robotics, vol. 30, no. 6, pp. 415–420, 2007. doi: 10.1109/ICORR.2007.4428458
B. Cheung et al., “Anatomical, neurophysiological and perceptual issues of tactile perception”, in Tactile displays for orientation, navigation and communication in air, sea and land environments. Neuilly-sur-Sein Cedex, France: NATO Research and Technology Organisation, 2008, pp. 1–18.
R.E. Fan et al., “A haptic feedback system for lower-limb prostheses”, IEEE Trans. Neural Syst. Rehabilit. Eng., vol. 16, no. 3, pp. 270–277, 2008. doi: 10.1109/TNSRE.2008.920075
D.G. Buma et al., “Intermittent stimulation delays adaptation to electrocutaneous sensory feedback”, IEEE Trans. Neural Syst. Rehabilit. Eng., vol. 15, no. 3, pp. 435–441, 2007. doi: 10.1109/TNSRE.2007.903942
L.A. Jones and N.B. Sarter, “Tactile displays: Guidance for their design and application”, Human Factors, vol. 50, no. 1, pp. 90–111, 2008. doi: 10.1518/001872008X250638
S.B. Godfrey et al., “A synergy-driven approach to a myoelectric hand”, in Proc. IEEE Int. Conf. Rehabilitation Robotics, 2013, pp. 1–6. doi: 10.1109/ICORR.2013.6650377
H. Zhao et al., “Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides”, Sci. Robotics, vol. 1 , no. 1, pp. 1–10, 2016. doi: 10.1126/scirobotics.aai7529
M.A. Bezuglyi and N.V. Pavlovets, “Optical biometry of biological tissues by ellipsodal reflectors”, in Proc. SPIE 8798, Clinical and Biomedical Spectroscopy and Imaging III, 87980Q, 2013. doi: 10.1117/12.2031142
M.A. Bezuglyi et al., “Ray tracing in ellipsoidal reflectors for optical biometry of media”, Appl. Opt., vol. 56, no. 30, pp. 8520–8526, 2017. doi: 10.1364/AO.56.008520
M. Bezuglyi et al., “The non-invasive optical glucometer prototype with ellipsoidal reflectors”, 2018 IEEE 59th Int. Sci. Conf. Power and Electrical Engineering of Riga Technical University, vol. 56, no. 30, pp. 8520–8526, 2018. doi: 10.1109/rtucon.2018.8659864
M.A. Bezuglyi et al., “Image processing at ellipsoidal photometry”, Devices and Methods of Measurements, vol. 7, no. 1, pp. 67–76, 2016. doi: 10.21122/2220-9506-2016-7-1-67-76
M.A. Bezuglyi et al., “Influence of laser beam profile on light scattering by human skin during photometry by ellipsoidal reflectors”, Devices and Methods of Measurements, vol. 9, no. 1, pp. 56–65, 2018. doi: 10.21122/2220-9506-2018-9-1-56-65
K. Vonsevych et al., “Fingers movements control system based on artificial neural network model”, Radioelectronics and Communications Systems, vol. 62, no. 1, pp. 23–33, 2019. doi: 10.3103/s0735272719010047
R. Chowdhury et al., “Surface electromyography signal processing and classification techniques”, Sensors, vol. 13, no. 9, pp. 12431–12466, 2013. doi: 10.3390/s130912431
N. Nazmi et al., “A review of classification techniques of EMG signals during isotonic and isometric contractions”, Sensors, vol. 16, no. 8, pp. 1304, 2016. doi: 10.3390/s16081304
W. He et al., “Neural network control of a rehabilitation robot by state and output feedback”, J. Intelligent & Robotic Systems, vol. 80, no. 1, pp. 15–31, 2015. doi: 10.1007/s10846-014-0150-6
M. Atzori et al., “Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands”, Frontiers in Neurorobotics, vol. 10, no. 9, 2016. doi: 10.3389/fnbot.2016.00009
M. Kerzel et al., “Haptic material classification with a multi-channel neural network”, Int. Joint Conf. Neural Networks, vol. 10, no. 9, pp. 439–466, 2017. doi: 10.1109/IJCNN.2017.7965887
S. Bell et al., “Material recognition in the wild with the materials in context database”, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2015, pp. 3479–3487. doi: 10.1109/CVPR.2015.7298970
P.L. Gentili et al., “Photochromic and luminescent compounds as artificial neuron models”, Dyes and Pigments, vol. 156, pp. 149–159, 2018. doi: 10.1016/j.dyepig.2018.04.006
M.A. Bezuglyi and N.V. Bezuglaya, “Ellipsoidal reflectors in biomedical diagnostic”, Proc. SPIE 9032-15, vol. 2, pp. Q1–Q5, 2013. doi: 10.1117/12.2044606
K.P. Vonsevych et al., “Evaluation of electromyogram time characteristics of the wrist functional movements for intuitive control of bionic prosthesis”, Naukovi Visti NTUU KPI, no. 1, pp. 45–53, 2018. doi: 10.20535/1810-0546.2018.1.115941
C. Goutte and E. Gaussier, “A probabilistic interpretation of precision, recall and F-score, with implication for evaluation”, in Proc. European Conference on Information Retrieval, 2005, pp. 345–359. doi: 10.1007/978-3-540-31865-1_25
M. Sokolova et al., “Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation”, in Proc. Australasian Joint Conference on Artificial Intelligence, 2006, pp. 1015–1021. doi: 10.1007/11941439_114
Q.J. Song et al., “Feature selection based on FDA and F-score for multi-class classification”, Expert Systems with Applications, vol. 81, pp. 22–27, 2017. doi: 10.1016/J.ESWA.2017.02.049
K. Levenberg, “A method for the solution of certain non-linear problems in least squares”, Q. Appl. Math., vol. 2, no. 2, pp. 164–168, 1944. doi: 10.109/qam/10666
D. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters”, J. Soc. Ind. Appl. Math., vol. 11, pp. 431–441, 1963. doi: 10.1137/0111030
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