Abstract
Assessing the fall risk of a patient in a busy clinical setting is challenging. Tests such as the timed-up-and-go test and narrow beam walking are difficult to perform due to space restrictions. Moreover, it is not easy to directly connect the results of these tests to fundamental biomechanical principles of gait stability, which emphasize the interplay between the movements of the body's center of mass (CoM) and its base of support (BoS). Herein, we show how a 1.2 m-long treadmill and a single "time-of-flight" Azure Kinect camera can capture the CoM-BoS interplay within 5 min. The CoM was calculated by dividing the body into 14 segments determined from 20 joint positions measured by the Kinect camera's body tracking SDK. By tracking the CoM and joint positions from stride to stride, we can evaluate different gait stability metrics using a markerless, contactless, space-efficient approach. A large digital database of CoM movements relative to foot placement will be useful for the future development of statistical and machine learning techniques for identifying subjects at higher risk of falling.