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Session: NeuroMuscular
Session starts: Thursday 26 January, 14:30
Presentation starts: 14:30
Room: Room 530

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Antonio Gogeascoechea (University of Twente)
Rafael Ornelas-Kobayashi (University of Twente)
Utku S. Yavuz (University of Twente)
Massimo Sartori (University of Twente)

Abstract:
BACKGROUND: Human motor control is a bundle of complex neuromusculoskeletal processes. Although current electromyography (EMG)-driven modeling frameworks (e.g. [1]) aim at representing such complexity, they often fail to capture the interaction between neural and mechanical mechanisms of movement. The ability to decode motor units (MUs) from high-density EMGs enables extending current neuromusculoskeletal models into MU-specific formulations where the neural information is preserved. Herein, we propose a high-resolution framework to generate MU-specific neuromusculoskeletal models based on the identification of MU-twitch properties. METHODS: We recorded torque and high-density EMGs from the lower leg during isometric dorsi-plantarflexion contractions across multiple activation levels and ankle positions [2]. We decomposed the EMGs into MU spike trains and calculated their recruitment thresholds and discharge rates. We computed a linear combination of these neural features and mapped them into contractile properties (contraction time and peak amplitude) found in humans [3]. We employed the resulting properties to design twitch models as impulse responses of a second-order system [4]. The MU-specific activation dynamics were defined as the convolution between the MU-twitch responses and their corresponding spike trains. The resulting activation profiles were used to drive a subject-specific musculoskeletal model which allowed computing joint moments. Moreover, we compared our methodology with the conventional EMG-driven framework. RESULTS: For the MU-driven models, the normalized RMSE values between the reference and predicted torques were below 0.5 across all conditions. The EMG-driven models, contrastingly, were unable to adapt to all conditions, providing high errors (nRMSE > 0.5) in the plantar-flexed and low activation conditions. CONCLUSION: Our proposed methodology showed robustness in predicting torque across multiple conditions and provides a deeper insight into force-generation processes of human movement. REFERENCES: [1] C. Pizzolato et al., (2015) J. Biomech., vol. 48, no. 14, pp. 3929–3936. [2] M. Sartori, U. Yavuz, and D. Farina, (2017). Sci. Rep., vol. 7, no. 1, p. 13465. [3] R. A. Garnett, M. J. O’Donovan, J. A. Stephens, and A. Taylor, (1979) J. Physiol., vol. 287, no. 1, pp. 33–43. [4] A. J. Fuglevand, D. A. Winter, and A. E. Patla, (1993) J. Neurophysiol., vol. 70, no. 6, pp. 2470–2488.