Abstract
The article considers the robotic arm rehabilitation device for severely paralyzed and motor disabled people with a control system using a portable eye tracker and functional near-infrared spectroscopy (fNIRS). An eye tracker is used to control the trajectory of the robotic arm's grip, while fNIRS is used to switch between control planes, or to open and close a prosthetic hand. Artificial neural networks (ANN) are employed for pattern recognition in the processing of fNIRS signals. User safety is ensured by a vision system and mechanical safety elements. The pilot experiment showed the feasibility of the implemented device. A prototype of the robotic arm rehabilitation device with a spherical coordinate system was implemented to help severely paralyzed people to take care of themselves.
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References
Chen, T.L., Ciocarlie, M., Cousins, S., et al.: Robots for humanity: a case study in assistive mobile manipulation. IEEE Rob. Autom. Mag. 20(1), 30–39 (2013)
Hammel, J., Hall, K., Lees, D., et al.: Clinical evaluation of a desktop robotic assistant. J. Rehabil. Res. 26(3), 1–16 (1989)
Park, D., Hoshia Y., Mahajanb, H.P., Rogersc, W.A., Kempa, C.C.: Active robot-assisted feeding with a general-purpose mobile manipulator: design, evaluation, and lessons learned. Robot. Auton. Syst. 124 (2020)
Lazarou, I., Nikolopoulos, S., Petrantonakis, P.C., Kompatsiaris, I., Tsolaki, M.: EEG-based brain-computer interfaces for communication and rehabilitation of people with motor impairment: a novel approach of the 21st Century. Front. Hum. Neurosci. 12, art. 14 (2018)
Afonin, A.N., Asadullaev, R.G., Sitnikova, M.A., Gladishev, A.R., Davletchurin, KKh.: Brain-computer interfaces in robotics. COMPUSOFT Int. J. Adv. Comput. Technol. 8(8), 3356–3361 (2019)
Kaplan, AYa., Zhigulskaya, D.D., Kirjanov, D.A.: The control human phantom fingers by means of P300 brain-computer interface for neurorehabilitation. Opera Medica et Physiologica. 2(S2), 73–74 (2016)
Lee, J.H., Ryub, J., Jolesza, F.A., et al.: Brain–machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm. Neurosci. Lett. 450(1), 1–6 (2009)
Bianchi, T., Croitoru, N.I., Frenz, M., et al.: NIRS monitoring of muscle contraction to control a prosthetic device. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 3570, pp. 157–163 (1998)
Batula, A.M., Kim, Y.E., Ayaz, H.: Virtual and actual humanoid robot control with four-class motor-imagery-based optical brain-computer interface. BioMed. Res. Int. 2017, article ID 1463512 (2017)
Chaudhary, U., Xia, B., Silvoni, S., Cohen, L.G., Birbaumer, N.: Brain computer interface based communication in the completely locked-in state. PLoS Biology 15(1), e1002593 (2017)
Duchowski A.T.: Eye Tracking Methodology. Theory and Practice, 3rd Edition, p. 387. Springer, Heidelberg (2017)
Bhattacharjee, K., Soni, M.: Eye controlled robotic motion using video tracking in real time. Int. J. Innov. Res. Sci. Eng. Technol. 6(7), 14828–14838 (2017)
Shishkin, S.L., Zhao, D.G., Isachenko, A.V., Velichkovsky, B.M.: Gaze-and-brain-controlled interfaces for human-computer and human-robot interaction. Psychol. Russ. State Art 10(3), 120–137 (2017)
Asadullaev, R.G., Afonin, A.N., Lomakin, V.V., Sitnikova, M.A.: Neural network classifier of hemodynamic brain activation patterns within a functional near-infrared spectroscopy-based brain-computer interface for a bionic prosthetic hand. Drug Invent. Today 12(9), 2130–2136 (2019)
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Research is supported by the RFBR grant 20–08-01178.
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Afonin, A.N., Asadullayev, R.G., Sitnikova, M.A., Shamrayev, A.A. (2021). A Rehabilitation Device for Paralyzed Disabled People Based on an Eye Tracker and fNIRS. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IV. NEUROINFORMATICS 2020. Studies in Computational Intelligence, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-60577-3_7
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DOI: https://doi.org/10.1007/978-3-030-60577-3_7
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