An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

Abstract

In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

“…Results from the noise analysis are as expected, as the RMSE on the variables of interest generally increases with the level of the noise. They also confirm the superiority of an EMG tracking formulation over minimizing excitations when processing motions involving co-contraction ( Figure 6 ), reinforcing the findings of Bélaise et al ( 2018a ); Moissenet et al ( 2019 ), and Lloyd and Besier ( 2003 ). We showed that, because our tracking variables are of a completely different nature (marker positions and EMG signals), they complement each other.…”

“…Forward approaches, also known as dynamic optimization, address limitations (i) and (ii) by integrating both the equation of motion and the activation dynamics forward in time (Ackermann and Schiehlen, 2009 ; Bélaise et al, 2018a ) while tracking experimental data such as body kinematics or contact forces. Issue (iii) can be tackled by tracking both markers and EMG data as in the so-called “EMG-marker tracking” proposed in Bélaise et al ( 2018a ) and Moissenet et al ( 2019 ). High computational costs and challenging convergence are the main shortcomings of dynamic optimization approaches which, at first glance, disqualify them for real-time applications.…”

A piezoelectric flexible insole system for gait monitoring for the Internet of Health Things

“…2 (b). It can be observed that two important gait phases (stance and swing) are clearly demonstrated with corresponding pressure amplitude, proving the ability to diagnose diseases like stroke [1][2] and Parkinson's disease [4][5]. The 2-dimensional plantar pressure distribution shown in Fig.…”

“…Gait is closely associated with many chronic diseases, as well as their progressions (e.g., stroke [1][2], tarsal coalition [3], Parkinson's disease [4][5], and diabetes [6][7]). It is of great significance to monitor gait parameters, such as velocity [8], stride length [9], and plantar pressure distribution accurately and continuously for disease evaluation [4] and efficient rehabilitation [10].…”

A Pilot Study of Musculoskeletal Abnormalities in Patients in Recovery from a Unilateral Rupture-Repaired Achilles Tendon

“…The maximal voluntary isometric contraction (MVIC) trials were performed followed by the trials as reference data. EMG intensities for each muscle were normalized to the MVIC EMG activity from zero to one [26].…”