2020/08/28 by Anna Boschi, Francesco Salvetti, Vittorio Mazzia +1 · 14 citations
Computer Science · Psychology · #Context-Aware Activity Recognition Systems #Deep learning #Enhanced Data Rates for GSM Evolution #Gaze Tracking and Assistive Technology #Modular design #Population #Robotics #Service (business) #Social Robot Interaction and HRI #Task (project management) #cs.RO
paper · pdf · doi:10.3390/machines8030049
published in Machines 8(3), 49 (Multidisciplinary Digital Publishing Institute)
openalex publication_date 2020/08/28 · arxiv created 2020/08/31 · openalex created_date 2020/09/08 · arxiv updated 2020/11/02 · openalex updated_date 2026/08/05
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.