A Robot-Administered ICU Confusion Assessment with Brain-Computer Interface Control

Evaluation of patient delirium in hospital Intensive Care Units (ICU) is a crucial and challenging task, with conventional assessments like CAM-ICU relying largely on verbal and physical communication, making it difficult for patients with limited physical abilities. To address this, we propose a system that integrates Brain-Computer Interface (BCI) technology and a Socially Assistive Robot (SAR) through brain-controlled mental commands. In a pilot user study, we demonstrate how our system could successfully administer a version of the CAM-ICU to 13 medical professionals and students roleplaying various level of delirium severity. Our work reveals early usability and workload insights, and next steps to improve upon assessment classification accuracy and interaction design.</p

Communicating Complex Decisions in Robot-Assisted Therapy

Socially Assistive Robots (SARs) have shown promising potential in therapeutic scenarios as decision-making instructors or motivational companions. In human-human therapy, experts often communicate the thought process behind the decisions they make to promote transparency and build trust. As research aims to incorporate more complex decision-making models into these robots to drive better interaction, the ability for the SAR to explain its decisions becomes an increasing challenge. We present the latest examples of complex SAR decision-makers. We argue that, based on the importance of transparent communication in human-human therapy, SARs should incorporate such components into their design. To stimulate discussion around this topic, we present a set of design considerations for researchers

A Systematic Approach to Modeling Structured Behavior in Social Robots

Social Robots (SRs) often require structured models of behavior to facilitate sophisticated interaction episodes in their capacity as coaches, teachers, assistants, and beyond. Techniques in human-centered design can support the translation of human-human to human-robot behavior, but can be challenging and often lead to weak interpretations. We introduce a four-step approach of data gathering, behavior model development, behavior model annotation, and robot implementation to promote a more systematic approach to the development of SR behaviors. The efficacy of this approach was demonstrated in a set of case studies involving 24 participants. We demonstrate how a structured behavior model for a SR was developed systematically by clustering observed human interaction episodes, and that the researchers’ original translations of human to robot behavior models captured task knowledge, but in each case, our model annotation step was necessary to validate the designs and further refine missing aspects