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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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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