Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting.

ABSTRACT:

Background

Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administ

SUBMITTER: Konishi S 

PROVIDER: S-EPMC10877007 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature