  Sit-to-stand transitions are an important part of activities of daily living
and play a key role in functional mobility in humans. The sit-to-stand movement
is often affected in older adults due to frailty and in patients with motor
impairments such as Parkinson's disease leading to falls. Studying kinematics
of sit-to-stand transitions can provide insight in assessment, monitoring and
developing rehabilitation strategies for the affected populations. We propose a
three-segment body model for estimating sit-to-stand kinematics using only two
wearable inertial sensors, placed on the shank and back. Reducing the number of
sensors to two instead of one per body segment facilitates monitoring and
classifying movements over extended periods, making it more comfortable to wear
while reducing the power requirements of sensors. We applied this model on 10
younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with
Parkinson's disease (PwP). We have achieved this by incorporating unique
sit-to-stand classification technique using unsupervised learning in the model
based reconstruction of angular kinematics using extended Kalman filter. Our
proposed model showed that it was possible to successfully estimate thigh
kinematics despite not measuring the thigh motion with inertial sensor. We
classified sit-to-stand transitions, sitting and standing states with the
accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We
have proposed a novel integrated approach of modelling and classification for
estimating the body kinematics during sit-to-stand motion and successfully
applied it on YH, OH and PwP groups.
