Gait analysis and visualization in a fall risk assessment system

Abstract

Falls are a major health concern among elderly populations. There is a critical need to develop automated systems for assessing a patient's fall risk although the methodologies for determining this risk vary in efficacy, accessibility, and comfort. With advancements in smart home technology, aging in place and accurate fall risk assessment are no longer mutually exclusive. This paper presents a user friendly fall risk assessment system designed for care providers to non-invasively but continuously monitor their patient's risk of falling. The proposed system employs a pressure sensor-embedded floor - a SmartFloor - installed in the patient's home to monitor trends in gait parameters like gait speed, stride length, and step width. The system allows care providers to visualize dangerous changes to their patient's gait 24/7 and without disturbing the patient. To facilitate diagnoses and fall risk assessment, the system also reconstructs a skeletal visualization of each recorded walking segment. This is done using a motion similarity algorithm and a database of SmartFloor and Microsoft Kinect data. We tested the accuracy of several variations of the motion similarity algorithm using a small pool of seven participants and the results are presented in this paper.