CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks

Pizzolato, Claudio; Lloyd, David G.; Sartori, Massimo; Ceseracciu, Elena; Besier, Thor F.; Fregly, Benjamin J.; Reggiani, Monica

doi:10.1016/j.jbiomech.2015.09.021

Cited by 271 publications

(370 citation statements)

References 48 publications

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“…We developed software (C++) based on CEINMS [50] and OpenSim [43, 51] that uses a marker-based Vicon motion capture system, force plates, and EMG to estimate MTFF in real-time (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

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Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Pizzolato

Reggiani

Saxby

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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102

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Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this study we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed 5 healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only 3 subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this study focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

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“…We developed software (C++) based on CEINMS [50] and OpenSim [43, 51] that uses a marker-based Vicon motion capture system, force plates, and EMG to estimate MTFF in real-time (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

“…CEINMS [50], was used in combination with OpenSim to estimate MTFF in real-time. Subjects were asked to modify their gait pattern in order to modify the model estimate of MTFF which was provided as visual biofeedback.…”

Section: Introductionmentioning

confidence: 99%

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Pizzolato

Reggiani

Saxby

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

102

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“…1) [15], [23], [29]. This enables simulating how EMG-controlled muscle contractions transfer mechanical force to skeletal structures instantly during an individual's movement.…”

Section: Real-time Emg-driven Modelingmentioning

confidence: 99%

“…Moreover, it comprises a modeling component for the computation of musculotendon kinematics based on our previously developed Multidimensional Cubic B-Spline (MCBS) method (Fig. 1F) [29] as well as a component for the simulation of musculotendon dynamics based on the previously developed Calibrated EMG-informed Neuromusculoskeletal Modeling (CEINMS) method [15], [23] (Fig. 1G).…”

Section: Real-time Emg-driven Modelingmentioning

confidence: 99%

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Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

Durandau

Farina

Sartori

2018

IEEE Trans. Biomed. Eng.

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132

113

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Abstract-Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses. Current biomechanical models cannot be used for real-time control in man-machine interfaces. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e. predicting accurate joint moments during six unseen motor tasks and about one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient interfacing with wearable assistive devices.

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Surgical parameters influence paediatric knee kinematics and cartilage stresses in anterior cruciate ligament reconstruction: Navigating subject‐specific variability using neuromusculoskeletal‐finite element modelling analysis

Dastgerdi,

Esrafilian,

Carty

et al. 2024

Knee surg. sports traumatol. arthrosc.

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PurposeAnterior cruciate ligament (ACL) rupture is increasingly common in paediatric and adolescent populations, typically requiring surgical ACL reconstruction (ACLR) to restore knee stability. However, ACLR substantially alters knee biomechanics (e.g., motion and tissue mechanics) placing the patient at elevated risk of early‐onset knee osteoarthritis.MethodsThis study employed a linked neuromusculoskeletal (NMSK)‐finite element (FE) model to determine effects of four critical ACLR surgical parameters (graft type, size, location and pre‐tension) on tibial articular cartilage stresses in three paediatric knees of different sizes during walking. Optimal surgical combinations were defined by minimal kinematic and tibial cartilage stress deviations in comparison to a corresponding intact healthy knee, with substantial deviations defined by normalized root mean square error (nRMSE) > 10%.ResultsResults showed unique trends of principal stress deviations across knee sizes with small knee showing least deviation from intact knee, followed by large‐ and medium‐sized knees. The nRMSE values for cartilage stresses displayed notable variability across different knees. Surgical combination yielding the highest nRMSE in comparison to the one with lowest nRMSE resulted in an increase of maximum principal stress on the medial tibial cartilage by 18.0%, 6.0% and 1.2% for small, medium and large knees, respectively. Similarly, there was an increase of maximum principal stress on lateral tibial cartilage by 11.2%, 4.1% and 12.7% for small, medium and large knees, respectively. Knee phenotype and NMSK factors contributed to deviations in knee kinematics and tibial cartilage stresses. Although optimal surgical configurations were found for each knee size, no generalizable trends emerged emphasizing the subject‐specific nature of the knee and neuromuscular system.ConclusionStudy findings underscore subject‐specific complexities in ACLR biomechanics, necessitating personalized surgical planning for effective restoration of native motion and tissue mechanics. Future research should expand investigations to include a broader spectrum of subject‐specific factors to advance personalized surgical planning.Level of EvidenceLevel III.

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CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks

Cited by 271 publications

References 48 publications

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

Surgical parameters influence paediatric knee kinematics and cartilage stresses in anterior cruciate ligament reconstruction: Navigating subject‐specific variability using neuromusculoskeletal‐finite element modelling analysis

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