Individual muscle contributions to tibiofemoral compressive articular loading during walking, running and sidestepping

Abstract

The tibiofemoral joint (TFJ) experiences large compressive articular contact loads during activities of daily living, caused by inertial, ligamentous, capsular, and most significantly musculotendon loads. Comparisons of relative contributions of individual muscles to TFJ contact loading between walking and sporting movements have not been previously examined. The purpose of this study was to determine relative contributions of individual lower-limb muscles to compressive articular loading of the medial and lateral TFJ during walking, running, and sidestepping. The medial and lateral compartments of the TFJ were loaded by a combination of medial and lateral muscles. During all gait tasks, the primary muscles loading the medial and lateral TFJ were the vastus medialis (VM) and vastus lateralis (VL) respectively during weight acceptance, while typically the medial gastrocnemii (MG) and lateral gastrocnemii (LG) dominated medial and lateral TFJ loading respectively during midstance and push off. Generally, the contribution of the quadriceps muscles were higher in running compared to walking, whereas gastrocnemii contributions were higher in walking compared to running. When comparing running and sidestepping, contributions to medial TFJ contact loading were generally higher during sidestepping while contributions to lateral TFJ contact loading were generally lower. These results suggests that after orthopaedic procedures, the VM, VL, MG and LG should be of particular rehabilitation focus to restore TFJ stability during dynamic gait tasks.

An EMG-assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities: Towards a Rapid Assessment Toolbox

“…The enveloped EMGs showed a wide variation between subjects (Figs. S1 to S7) that may account for individual variations in muscle recruitment strategies [24][25][26][27]77]. These variations were also seen in the muscle activations estimated by the EMG-P r e p r i n t assisted MS model but to a lesser degree in those estimated by the SO-based MS model (Figs.…”

“…However, none of the developed multiscale MS-FE models have been used for analyzing joint mechanics in functional activities other than gait. Furthermore, to the best P r e p r i n t of our knowledge, there are no studies incorporating subject-specific muscle recruitment (activation) strategies in the estimation of tissue-level mechanical responses, although muscle recruitment has a significant effect on the joint loading, especially in the presence of MS disorders [24][25][26][27][28]. Moreover, those studies reporting detailed joint mechanical responses (either joint-level or tissue-level) have investigated healthy subjects [17,[28][29][30], utilized simplified joint models in terms of limited degrees of freedom (DoFs) [30][31][32], excluded subject-specific joint geometries [28,[30][31][32], omitted crucial joint tissues (e.g., menisci) [28,30,31], and/or utilized simple soft tissue material models [28].…”

Machine learning methods to support personalized neuromusculoskeletal modelling

“…Computational neuromusculoskeletal biomechanics encompasses physics-based modelling of the complex, multi-scale, non-linear, and dynamic interaction between neural drive to muscles, muscle dynamics, joint kinematics and kinetics, and their effects on the loading experienced by musculoskeletal tissues. Computational neuromusculoskeletal biomechanics aims to understand and manage many neuromusculoskeletal conditions, and to rehabilitate patients and have been used to study many phenomena ranging from muscle function during locomotion in healthy individuals (Hamner et al 2010;Killen et al 2018;Pandy and Andriacchi 2010;Sasaki 2010;Saxby et al 2016b;Schache et al 2012;Shelburne et al 2006;Thelen and Anderson 2006) and those with pathologies (Gerus et al 2013;Hoang et al 2019;Konrath et al 2017;Montefiori et al 2019a;Saxby et al 2016a;Shao et al 2009), to model-driven control of prostheses or rehabilitation robotics (Sartori et al 2018;Sartori et al 2016). Other applications include estimation of tissue loading (Kim et al 2009;Saxby et al 2016b;Wellsandt et al 2016) and how this is effected by ergonomic aids (Hall et al 2019) or occupational demands (Lenton et al 2018).…”

The effects of knee extensor moment biofeedback on gait biomechanics and quadriceps contractile behavior

“…Quadriceps muscle forces are the largest contributor to knee loading during the early stance phase of walking (Killen et al, 2018). What we know about quadriceps muscle contractile behavior comes primarily from electromyographic measures and computational simulations.…”