The Problem - Accidental falls are the most frequent injury of old age and have dramatic implications. To date, fall prediction estimation is clinical and subjective, relying on the expertise of the physiotherapist for performing the diagnosis based on standard scales

The Solution- In this study, we developed an objective, computational tool, which automates the fall assessment process and allows easy, efficient and accessible assessment of fall risk. The system enables large scale screening of the general public at very little cost

Intellectual Property - The tool is based on a PCT novel multi depth-camera human motion tracking system integrated with Machine Learning algorithms.

Pilot and Prototype - The system was pilot tested in the physiotherapy unit at a major hospital and showed high rates of fall risk predictions as well as correlation with physiotherapists BBS scores on individual BBS motion tasks.