Aims. This study aimed to analyze kinematics and kinetics of the tibiofemoral joint in healthy subjects with valgus, neutral, and varus limb alignment throughout multiple gait activities using dynamic videofluoroscopy. Methods. Five subjects with valgus, 12 with neutral, and ten with varus limb alignment were assessed during multiple complete cycles of level walking, downhill walking, and stair descent using a combination of dynamic videofluoroscopy, ground reaction
The aim of this study was to investigate the biomechanical effect of the anterolateral ligament (ALL), anterior cruciate ligament (ACL), or both ALL and ACL on kinematics under dynamic loading conditions using dynamic simulation subject-specific knee models. Five subject-specific musculoskeletal models were validated with computationally predicted muscle activation, electromyography data, and previous experimental data to analyze effects of the ALL and ACL on knee kinematics under gait and squat loading conditions.Objectives
Methods
The aim of the current study was to analyse the effects of posterior cruciate ligament (PCL) deficiency on forces of the posterolateral corner structure and on tibiofemoral (TF) and patellofemoral (PF) contact force under dynamic-loading conditions. A subject-specific knee model was validated using a passive flexion experiment, electromyography data, muscle activation, and previous experimental studies. The simulation was performed on the musculoskeletal models with and without PCL deficiency using a novel force-dependent kinematics method under gait- and squat-loading conditions, followed by probabilistic analysis for material uncertain to be considered.Objectives
Methods
To compare the gait of unicompartmental knee arthroplasty (UKA)
and total knee arthroplasty (TKA) patients with healthy controls,
using a machine-learning approach. 145 participants (121 healthy controls, 12 patients with cruciate-retaining
TKA, and 12 with mobile-bearing medial UKA) were recruited. The
TKA and UKA patients were a minimum of 12 months post-operative,
and matched for pattern and severity of arthrosis, age, and body
mass index. Participants walked on an instrumented treadmill until their
maximum walking speed was reached. Temporospatial gait parameters,
and vertical ground reaction force data, were captured at each speed.
Oxford knee scores (OKS) were also collected. An ensemble of trees
algorithm was used to analyse the data: 27 gait variables were used
to train classification trees for each speed, with a binary output
prediction of whether these variables were derived from a UKA or
TKA patient. Healthy control gait data was then tested by the decision
trees at each speed and a final classification (UKA or TKA) reached
for each subject in a majority voting manner over all gait cycles
and speeds. Top walking speed was also recorded.Aims
Patients and Methods
