Aims. In Asia and the Middle-East, people often flex their knees deeply
in order to perform activities of daily living. The purpose of this
study was to investigate the 3D kinematics of normal knees during
high-flexion activities. Our hypothesis was that the femorotibial
rotation, varus-valgus angle, translations, and kinematic pathway
of normal knees during high-flexion activities, varied according
to activity. Materials and Methods. We investigated the in vivo kinematics of eight
normal knees in four male volunteers (mean age 41.8 years; 37 to
53) using 2D and 3D registration technique, and modelled the knees
with a computer aided design program. Each subject squatted, kneeled,
and sat cross-legged. We evaluated the femoral rotation and varus-valgus
angle relative to the tibia and anteroposterior translation of the
medial and lateral side, using the transepicodylar axis as our femoral
reference relative to the perpendicular projection on to the tibial
plateau. This method evaluates the femur medially from what has
elsewhere been described as the extension facet centre, and differs
from the method classically applied. . Results. During squatting and kneeling, the knees displayed femoral external
rotation. When sitting cross-legged, femurs displayed internal rotation
from 10° to 100°. From 100°, femoral external rotation was observed.
No significant difference in varus-valgus angle was seen between
squatting and kneeling, whereas a varus position was observed from
140° when sitting cross-legged. The measure kinematic pathway using
our methodology found during squatting a
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
