  Get-Up-and-Go Test is commonly used for assessing the physical mobility of
the elderly by physicians. This paper presents a method for automatic analysis
and classification of human gait in the Get-Up-and-Go Test using a Microsoft
Kinect sensor. Two types of features are automatically extracted from the human
skeleton data provided by the Kinect sensor. The first type of feature is
related to the human gait (e.g., number of steps, step duration, and turning
duration); whereas the other one describes the anatomical configuration (e.g.,
knee angles, leg angle, and distance between elbows). These features
characterize the degree of human physical mobility. State-of-the-art machine
learning algorithms (i.e. Bag of Words and Support Vector Machines) are used to
classify the severity of gaits in 12 subjects with ages ranging between 65 and
90 enrolled in a pilot study. Our experimental results show that these features
can discriminate between patients who have a high risk for falling and patients
with a lower fall risk.
