Sensor Fusion-Oriented Fall Detection for Assistive Technologies Applications

Abstract

A new trend in modern Assistive Technologies implies making extensive use of ICT to develop efficient and reliable "Ambient Intelligence" applications dedicated to disabled, elderly or frail people. In this paper we describe two fall detectors, based on bio-inspired algorithms. Such devices can either operate independently or be part of a modular and easily extensible architecture, able to manage different areas of an intelligent environment. In this case, effective data fusion can be achieved, thanks to the complementary nature of the sensors on which the detectors are based. One device is based on vision and can be implemented on a standard FPGA programmable logic. It relies on a simplified version of the Particle Swarm Optimization algorithm. The other device under consideration is a wearable accelerometer-based fall detector, which relies on a recent soft-computing paradigm called Hierarchical Temporal Memories (HTMs).