Computer Vision System Based on the Analysis of Gait Features for Fall Risk Assessment in Elderly People

Abstract

Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of establishing a preventive system for the care of the elderly, both in the hospital environment and at home. Therefore, this work proposes the development of an intelligent vision system that uses a novel methodology to infer fall risk from the analysis of kinetic and spatiotemporal gait parameters. In general, each patient is assessed using the Tinetti scale. Then, the computer vision system estimates the biomechanics of walking and obtains gait features, such as stride length, cadence, period, and range of motion. Subsequently, this information serves as input to an artificial neural network that diagnoses the risk of falling. Ninety-six participants took part in the study. The system’s performance was 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score. Thus, the proposed system can evaluate the fall risk assessment, which could benefit clinics, hospitals, and even homes by allowing them to assess in real time whether a person is at high risk of falling to provide timely assistance.

Keywords

• fall risk
• machine learning
• gait assessment
• computer vision
• physiotherapy
• biomechanics