Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature.

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

Remote HRI: a Methodology for Maintaining COVID-19 Physical Distancing and Human Interaction Requirements in HRI Studies.

Observing how humans and robots interact is an integral part of understanding how they can effectively coexist. This ability to undertake these observations was taken for granted before the COVID-19 pandemic restricted the possibilities of performing HRI study-based interactions. We explore the problem of how HRI research can occur in a setting where physical separation is the most reliable way of preventing disease transmission. We present the results of an exploratory experiment that suggests Remote-HRI (R-HRI) studies may be a viable alternative to traditional face-to-face HRI studies. An R-HRI study minimizes or eliminates in-person interaction between the experimenter and the participant and implements a new protocol for interacting with the robot to minimize physical contact. Our results showed that participants interacting with the robot remotely experienced a higher cognitive workload, which may be due to minor cultural and technical factors. Importantly, however, we also found that whether participants interacted with the robot in-person (but socially distanced) or remotely over a network, their experience, perception of, and attitude towards the robot were unaffected.