Automatic Measurement of Physical Mobility in Get-Up-and-Go Test Using Kinect Sensor

TL;DR

This paper introduces an automatic method using Kinect sensor data and machine learning to assess elderly individuals' mobility during the Get-Up-and-Go Test, effectively identifying fall risk levels.

Contribution

It presents a novel approach combining gait and anatomical features with machine learning for automatic mobility assessment using Kinect data.

Findings

Features can distinguish high fall risk from low risk

Support Vector Machines achieved high classification accuracy

Method provides an objective mobility assessment tool

Abstract

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.