Using virtual reality to assess the risk of falling in elderly people

Falls are the leading cause of accidents among the elderly. Every year, it affects one in four people over the age of sixty-five. There are many factors involved in the risk of falling, including motor, attentional, and cognitive factors. In order to better characterize, understand, and predict the risk of falling, we aim to provide practitioners with a technological solution based on virtual reality, enabling them to collect and identify various indicators of the risk of falls. This user-friendly solution automates the experimental protocol and the collection of indicators, ensures the reproducibility of experimental conditions, and immerses patients in a realistic environment and real-life situations. Our tool, which is compatible with consumer virtual reality devices, uses a total of six sensors worn by the patient to capture the kinematics of the whole body, displayed in real time in the form of a virtual avatar. These kinematic data, which can be replayed by the practitioner, can be used to feed a digital learning process. The experiment places patients in six test situations, progressively introducing different tasks and obstacles in order to test and collect indicators of their motor, attentional, and cognitive capacities, allowing for inter-patient and inter-condition comparisons.