A hardware-software framework for high-reliability people fall detection
M. Grassi, Andrea Lombardi, Gabriele Rescio, P. Malcovati, Mattia Malfatti, L. Gonzo, Alessandro Leone, Giovanni Diraco, Cosimo Distante, Pietro Aleardo Siciliano, Vit Libal, Jing Huang, Gerasimos Potamianos · 2008
This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated prototype includes three different sensors: a 3D time-of-flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each of the three sensors, an optimized algorithm for fall-detection has been developed and benchmarked on a collected mulitimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.