Real-Time Estimation of Lower Limb Musculoskeletal Tissue Loading Using an Electromyogram-Informed Neuromusculoskeletal Model

Numerous musculoskeletal pathologies have been linked to altered tissue loading conditions. However, it is extremely difficult to measure in-vivo tissue loads, and internal loads are often inferred from external variables. A typical example is knee osteoarthritis (OA), with one of the main causes believed to be inappropriate loading in the tibiofemoral joint during walking. Large external adduction moments have been associated with the progression of knee OA, increased pain, and worse outcome after osteotomy surgery. However, estimates of the knee joint articular loading should also include contributions from muscle forces, which account for up to 50% of the total load. Also, muscle activation patterns differ between individuals, even when kinematics and kinetics are the same.

The modification of gait based on real-time biofeedback is a non-surgical treatment that has the potential to reduce the symptoms associated with knee OA. However, current gait retraining practices focus on the reduction of the external knee adduction moment, which is not necessarily a good indicator of knee load, and it may be crucial to find better approaches to retrain gait.

Neuromusculoskeletal (NMS) models are an anatomical and physiological representation of an individual, and can be used to estimate muscle forces and tissue loading inside the human body. However, the musculoskeletal system is inherently redundant and the same external loads can be distributed differently across muscles. Several neural control solutions have been suggested in the literature to solve this problem: the use of electromyograms (EMG), optimisation, or combinations of both. Nonetheless, there is no public available software implementing all these methods using a consistent NMS model, thus impeding the comparison of different neural solutions in a consistent manner.

This thesis relates the development of a comprehensive and computationally efficient NMS toolbox (CEINMS – Calibrated EMG-Informed NMS model) that incorporates multiple neural control solutions to estimate muscle forces and tissue loads, and its application to the real-time estimation of knee joint loads.

CEINMS was developed and released as open-source software and toolbox for OpenSim (https://simtk.org/home/ceinms). In this thesis the main components of CEINMS are described, including the calibration procedure to estimate individual musculotendon parameters, and the implemented neural control algorithms: EMG-driven, EMG-assisted, and static optimisation. CEINMS was then used to compare the different neural control algorithms and estimate muscle co-contractions in healthy subjects during walking. Results showed the EMG-driven mode to be adequate to investigate knee and ankle joints, while EMG-assisted mode has to be preferred to analyse the hip joint. The paper describing CEINMS and this research was published as Pizzolato C., Lloyd D.G., Sartori M., Fregly B.J., Ceseracciu E., Besier T.F., Reggiani, M., CEINMS: a toolbox to investigate the influence of different neural solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks, Journal of Biomechanics, 48: 3929–3936, 2015.

To enable the use of CEINMS in real-time during walking, the estimation of joint angles and moments from motion capture data was also required. Inverse kinematics and inverse dynamics were solved in real-time at a frequency up to 2000Hz. Real-time and offline solutions were compared and a sensitivity analysis explored the best cut-off frequency to be used in real-time to minimise both errors and time delays. The manuscript describing this research was submitted as Pizzolato C., Reggiani M., Modenese L., Lloyd D.G., Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim, Computer Methods in Biomechanics and Biomedical Engineering.

Real-time EMG-driven mode of CEINMS, inverse kinematics, and inverse dynamics were combined to estimate musculotendon forces and tissue load. The real-time system was used for provide the visual biofeedback of the medial knee contact force to subjects walking on a instrumented treadmill. The subjects modified their gait in order to manipulate the medial contact force, and it was shown that the use of NMS models that account for individuals’ muscle activation patterns and co-contraction are essential to the estimation of knee loads. The manuscript describing this research is close to submission as Pizzolato C., Reggiani M., Saxby D.J., Modenese L., Lloyd D.G., Real-time tibiofemoral joint contact force biofeedback for gait retraining, Journal of Biomechanics

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Year

Entry

2001

Anderson FC, Pandy MG. Static and dynamic optimization solutions for gait are practically equivalent. J Biomech. February 2001;34(2):153-161.

2001

Anderson FC, Pandy MG. Dynamic optimization of human walking. J Biomech Eng. October 2001;123(5):381-390.

2008

Chaudhari AMW, Briant PL, Bevill SL, Koo S, Andriacchi TP. Knee kinematics, cartilage morphology, and osteoarthritis after ACL injury. Med Sci Sports Exer. February 2008;40(2):215-222.

2005

Andriacchi TP, Dyrby CO. Interactions between kinematics and loading during walking for the normal and ACL deficient knee. J Biomech. February 2005;38(2):293-298.

2007

Erdemir A, McLean S, Herzog W, van den Bogert AJ. Model-based estimation of muscle forces exerted during movements. Clin Biomech (Bristol, Avon). February 2007;22(2):131-154.