Research Area:  Machine Learning

Abstract:

The simultaneous surges in the research on socially assistive robotics and that on computer vision can be seen as a result of the shifting and increasing necessities of our global population, especially towards social care with the expanding population in need of socially assistive robotics. The merging of these fields creates demand for more complex and autonomous solutions, often struggling with the lack of contextual understanding of tasks that semantic analysis can provide and hardware limitations. Solving those issues can provide more comfortable and safer environments for the individuals in most need. This work aimed to understand the current scope of science in the merging fields of computer vision and semantic analysis in lightweight models for robotic assistance. Therefore, we present a systematic review of visual semantics works concerned with assistive robotics. Furthermore, we discuss the trends and possible research gaps in those fields. We detail our research protocol, present the state of the art and future trends, and answer five pertinent research questions. Out of 459 articles, 22 works matching the defined scope were selected, rated in 8 quality criteria relevant to our search, and discussed in depth. Our results point to an emerging field of research with challenging gaps to be explored by the academic community. Data on database study collection, year of publishing, and the discussion of methods and datasets are displayed. We observe that the current methods regarding visual semantic analysis show two main trends. At first, there is an abstraction of contextual data to enable an automated understanding of tasks. We also observed a clearer formalization of model compaction metrics.

Keywords:  
Computer Vision
Semantic Analysis
Socially Assistive Robotics
Deep Learning
Machine Learning

Author(s) Name:  Antonio Victor Alencar Lundgren, Matheus Albert Oliveira dos SantosORCID,Byron Leite Dantas Bezerra and Carmelo José Albanez Bastos-Filho

Publisher name:  MDPI

DOI:  10.3390/ai3010014

Volume Information:  Volume 3 Issue 1

Paper Link:   https://www.mdpi.com/2673-2688/3/1/14