Abstract
This paper presents a linear optimization procedure able to adapt a simplified EMG-driven NeuroMusculoSkeletal (NMS) model to the specific subject. The optimization procedure could be used to adjust a NMS model of a generic human articulation in order to predict the joint torque by using ElectroMyoGraphic (EMG) signals. The proposed approach was tested by modeling the human elbow joint with only two muscles. Using the cross-validation method, the adjusted elbow model has been validated in terms of both torque estimation performance and predictive ability. The experiments, conducted with healthy people, have shown both good performance and high robustness. Finally, the model was used to control directly and continuously a exoskeleton rehabilitation device through EMG signals. Data acquired during free movements prove the model ability to detect the human’s intention of movement.
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References
Basteris, A., Nijenhuis, S.M., Stienen, A., Buurke, J.H., Prange, G.B., Amirabdollahian, F.: Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review. J. Neuroeng. Rehabil. 11(1), 111 (2014)
Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F.: Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J. Appl. Biomech. 20(4), 367 (2004)
Buongiorno, D., Barsotti, M., Sotgiu, E., Loconsole, C., Solazzi, M., Bevilacqua, V., Frisoli, A.: A neuromusculoskeletal model of the human upper limb for a myoelectric exoskeleton control using a reduced number of muscles. In: 2015 IEEE World Haptics Conference (WHC), pp. 273–279, June 2015
Burdet, E., Franklin, D.W., Milner, T.E.: Human Robotics: Neuromechanics and Motor Control. MIT Press, Cambridge (2013)
Cavallaro, E.E., Rosen, J., Perry, J.C., Burns, S.: Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Trans. Biomed. Eng. 53(11), 2387–2396 (2006)
Desrosiers, J., Bourbonnais, D., Corriveau, H., Gosselin, S., Bravo, G.: Effectiveness of unilateral and symmetrical bilateral task training for arm during the subacute phase after stroke: a randomized controlled trial. Clin. Rehabil. 19(6), 581–593 (2005)
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)
Fleischer, C., Hommel, G.: A human-exoskeleton interface utilizing electromyography. IEEE Trans. Robot. 24(4), 872–882 (2008)
Frisoli, A., Rocchi, F., Marcheschi, S., Dettori, A., Salsedo, F., Bergamasco, M.: A new force-feedback arm exoskeleton for haptic interaction in virtual environments. In: Eurohaptics Conference, 2005 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2005. World Haptics 2005. First Joint, pp. 195–201. IEEE (2005)
Hassani, W., Mohammed, S., Rifai, H., Amirat, Y.: Emg based approach for wearer-centered control of a knee joint actuated orthosis. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 990–995. IEEE (2013)
Hermens, H.J., Freriks, B., Merletti, R., Stegeman, D., Blok, J., Rau, G., Disselhorst-Klug, C., Hägg, G.: European recommendations for surface electromyography. Roessingh Res. Dev. 8(2), 13–54 (1999)
Hill, A.: The heat of shortening and the dynamic constants of muscle. Proc. R. Soc. Lond. B: Biol. Sci. 126(843), 136–195 (1938)
Holzbaur, K.R., Murray, W.M., Delp, S.L.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33(6), 829–840 (2005)
Huang, V.S., Krakauer, J.W.: Robotic neurorehabilitation: a computational motor learning perspective. J. Neuroeng. Rehabil. 6(1), 5 (2009)
Jarrassé, N., Proietti, T., Crocher, V., Robertson, J., Sahbani, A., Morel, G., Roby-Brami, A.: Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients. Front. Hum. Neurosci. 8, 947 (2014)
Kiguchi, K., Hayashi, Y.: An emg-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(4), 1064–1071 (2012)
Leys, D., Hénon, H., Mackowiak-Cordoliani, M.A., Pasquier, F.: Poststroke dementia. Lancet Neurol. 4(11), 752–759 (2005)
Lloyd, D.G., Besier, T.F.: An emg-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J. Biomech. 36(6), 765–776 (2003)
Lo, A.C., Guarino, P.D., Richards, L.G., Haselkorn, J.K., Wittenberg, G.F., Federman, D.G., Ringer, R.J., Wagner, T.H., Krebs, H.I., Volpe, B.T., et al.: Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362(19), 1772–1783 (2010)
Loconsole, C., Leonardis, D., Barsotti, M., Solazzi, M., Frisoli, A., Bergamasco, M., Troncossi, M., Foumashi, M.M., Mazzotti, C., Castelli, V.P.: An emg-based robotic hand exoskeleton for bilateral training of grasp. In: World Haptics Conference (WHC), pp. 537–542. IEEE (2013)
Marchal-Crespo, L., Reinkensmeyer, D.J.: Review of control strategies for robotic movement training after neurologic injury. J. Neuroeng. Rehabil. 6(1), 20 (2009)
Moreland, J.D., Thomson, M.A., Fuoco, A.R.: Electromyographic biofeedback to improve lower extremity function after stroke: a meta-analysis. Arch. Phys. Med. Rehabil. 79(2), 134–140 (1998)
Oskoei, M.A., Hu, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. 55(8), 1956–1965 (2008)
Sartori, M., Reggiani, M., Pagello, E., Lloyd, D.G.: Modeling the human knee for assistive technologies. IEEE Trans. Biomed. Eng. 59(9), 2642–2649 (2012)
Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, Hoboken (2009)
Zajac, F.E.: Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit. Rev. Biomed. Eng. 17(4), 359–411 (1988)
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This work has been partially funded from the EU Horizon2020 project n. 644839 CENTAURO and the EU FP7 project n. 601165 WEARHAP.
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Buongiorno, D., Barone, F., Solazzi, M., Bevilacqua, V., Frisoli, A. (2016). A Linear Optimization Procedure for an EMG-driven NeuroMusculoSkeletal Model Parameters Adjusting: Validation Through a Myoelectric Exoskeleton Control. In: Bello, F., Kajimoto, H., Visell, Y. (eds) Haptics: Perception, Devices, Control, and Applications. EuroHaptics 2016. Lecture Notes in Computer Science(), vol 9775. Springer, Cham. https://doi.org/10.1007/978-3-319-42324-1_22
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