A combination of visual and temporal trajectory features for cognitive assessment in smart home

The rapid increase of the elderly population and new advances in pervasive computing technologies allow innovative tools and applications to support independent living for frail people and identify early symptoms of health problems, including neurodegenerative disorders. Among several studies reported in the literature, monitoring locomotion traces to detect symptoms of cognitive impairment has gained increasing attention. Therefore, in this work, we propose a novel technique for the recognition of locomotion patterns related to cognitive decline based on sensor data acquired in smart homes. In particular, we introduce a vision-based method to graphically represent indoor trajectories with random rotation, using different handcrafted features designed for image analysis tasks and combined with features extracted directly from spatio-temporal sequences of movements. Experiments on a real-world dataset acquired in a smart-home test-bed show that the proposed approach achieves promising results.