Gil Serrancolí

Analysis of muscle synergies and activation-deactivation patterns in subjects with anterior cruciate ligament deficiency during walking.

BACKGROUND The knowledge of muscle activation patterns when doing a certain task in subjects with anterior cruciate ligament deficiency could help to improve their rehabilitation treatment. The goal of this study is to identify differences in such patterns between anterior cruciate ligament-deficient and healthy subjects during walking. METHODS Electromyographic data for eight muscles were measured in a sample of eighteen subjects with anterior cruciate ligament deficiency, in both injured (ipsilateral group) and non-injured (contralateral group) legs, and a sample of ten healthy subjects (control group). The analysis was carried out at two levels: activation-deactivation patterns and muscle synergies. Muscle synergy components were calculated using a non-negative matrix factorization algorithm. FINDINGS The results showed that there was a higher co-contraction in injured than in healthy subjects. Although all muscles were activated similarly since all subjects developed the same task (walking), some differences could be observed among the analyzed groups. INTERPRETATION The observed differences in the synergy components of injured subjects suggested that those individuals alter muscle activation patterns to stabilize the knee joint. This analysis could provide valuable information for the physiotherapist to identify alterations in muscle activation patterns during the follow-up of the subjects rehabilitation.

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and -0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

A weighted cost function to deal with the muscle force sharing problem in injured subjects: A single case study

The human body is an over-actuated multi-body system, as each joint degree of freedom can be controlled by more than one muscle. Solving the force-sharing problem (i.e. finding out how the resultant joint torque is shared among the muscles actuating that joint) calls for an optimization process where a cost function, representing the strategy followed by the central nervous system to activate muscles, is minimized. The main contribution of the present study has been the particular formulation of that cost function for the case of the pathological gait of a single subject suffering from anterior cruciate ligament rupture. Our hypothesis was that the central nervous system does not weight equally the muscles when trying to compensate for a lower limb injury during gait (in contrast to what is the usual practice for healthy gait where all muscles are weighted equally). This hypothesis is supported by the fact that muscle activity in injured individuals differs from that of healthy subjects. Different functions were tested until we finally came out with a cost function that was consistent with experimental electromyography measurements and inverse dynamics results for a subject suffering this particular pathology.

A powered lower limb orthosis for gait assistance in incomplete spinal cord injured subjects

The paper deals with the mechanical design of a new active stance-control knee-ankle-foot orthosis (SCKAFO). The orthosis is intended to provide gait assistance for incomplete spinal cord injured patients that present functional hip muscles, but partially denervated knee and ankle muscles. It consists of a passive compliant joint that constrains ankle plantar flexion, along with a powered knee unit that prevents knee flexion during stance and controls flexion-extension during swing. For this purpose, the knee joint incorporates a controllable mechanical locking system and an electrical DC motor that actuate independently. The prototype is equipped with different sensors (plantar sensors and angular encoders) for control purposes. They are used to identify the main events defining the gait phases and to provide feedback measurements for the motor control system.

On the force sharing problem in patients suffering joint pain

Gait pattern in patients suffering joint pain usually shows alterations which may differ according to the pain origin. Whether caused by an accident, a sport injury or arthritis, the reason for joint pain may be associated to damage on muscles near the joint, damage on cartilage between skeletal segments as well as ligament injury [Calmbach and Hutchens, 2003]. It is well known that the human body is redundantly actuated (i.e., it has more muscles than degrees of freedom), and that leads to the force sharing problem in biomechanics [Anderson and Pandy, 2001]. In this study, we assume that muscles are activated by the Central Nervous System (CNS) according to a minimum-pain strategy. Translating minimum pain into a cost function related to welldefined dynamical variables (muscle forces, muscle stresses, joint contact forces, etc.) is not straightforward. The aim of this work is precisely to investigate the most suitable cost function leading to a distribution of muscle activations consistent with electromyographic data (EMG).