Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion
Samuele Gasparrini, Enea Cippitelli, Ennio Gambi, Susanna Spinsante, Jonas Wåhslén, Ibrahim B. Orhan, Thomas Lindh · Advances in intelligent systems and computing · 2015
Fall injury issues represent a serious problem for elderly in our society. These people want to live in their home as long as possible and technology can improve their security and independence. In this work we study the joint use of a camera based system and wearable devices, in the so called data fusion approach, to design a fall detection solution. The synchronization issues between the heterogeneous data provided by the devices are properly treated, and three different fall detection algorithms are implemented. Experimental results are also provided, to compare the proposed solutions.