Researchers have explored the effects of expressing emotions using various modalities in the field of social robots. Prior studies have demonstrated that the use of color in emotional expressions can enhance user acceptance, relationship building, and communication effectiveness. This study aims to validate the effectiveness of different modalities in expressing Ekman’s six basic emotions. Specifically, four modalities were compared: face expression (F), face and LED color expression (FL), face and LED color expression with blinking (FLB), and face and eye color expression (FE). To accomplish this, we developed a small social robot prototype and used a computational emotion model to improve robot dynamics and interactivity. The findings revealed that, although the F modality effectively expressed emotions, ambiguous emotions were better perceived when color or blinking was incorporated. Emotions such as anger, sadness, disgust, surprise, and fear were better conveyed to the participants when the FL, FLB, and FE modalities were utilized. For happiness, F alone was sufficient for recognition. This study provides empirical evidence on the effectiveness of different modalities for expressing emotions in social robots and offers valuable insights gathered from participants’ feedback and reflections.

In this paper, we present ALH-E (ALternative Healthcare for Expressing ache), an assistive robot with a deformable and flexible interface for pain communication. It consists of two components: a squeezable device for inputting the patient’s pain intensity and a flexible output device that expresses the pain by twisting-bending movements in response to the input signals. The interconnectivity between the devices allows for communication of pain intensity between patients and caregivers, anywhere and anytime. A field study in the hospital (dental clinic and orthopedic) was conducted to verify the usability and effectiveness of ALH-E in pain communication. Our field study results demonstrated the unique advantages of ALH-E over conventional methods, providing significant assistance to patients and caregivers.

Describing pain intensity constitutes an essential part of pain communication. A medical practitioner cannot depend on pain scales because of the criteria differences between the patient and caregiver. However, online pain communication is dependent on a patient’s description and an assessment on a pain scale. This paper proposes a robot-assisted pain communication system with a tangible user interface that enables non-numerical pain communication. The SQTT interface is proposed using design processes consisting of a novel squeezable input device and a twisting robot. The twisting expression of the robot represents the pain intensity, which is gauged from the squeezing power on the input device. Integrating input and output requires defining how the twisting motion of the robot is rendered from the squeezing input. An experiment was conducted to evaluate the four methods of rendering: raw, smoothing (moving average), dynamic smoothing, and updating peak. The result of a non-parametric one-way analysis of variance indicates a significant difference between the rendering methods. As a result, an appropriate rendering method is proposed based on the ranges: smoothing for mild pain, vibrating for moderate pain, and exaggerating for severe pain. In conclusion, the robot-assisted pain communication system can be implemented with intuitive interaction primitives for online context. This paper contributes a lesson in designing a robot-assisted online pain communication, which uses a new method of measuring deformation.

This paper details the development of a Shared Indoor Smart Mobility device called AngGo. As a precursor to the development process, we conducted user research on three kinds of outdoor personal mobility. Our goal was to determine the major differences between outdoor and indoor personal mobility and to ensure that AngGo would meet the requirements of indoor personal mobility in a practical way, as informed by the results of surveys and interviews. Tests were conducted on the time-of-flight sensors to be used for indoor autonomous driving. Manual mode as well as the experiment-based equations governing the sensors were optimized through user testing. Our observational experiments, which were carried out in the lobby of a building, showed that both autonomous and manual modes functioned as designed. This study makes a contribution to the literature by describing how our AngGo device features an autonomous driving platform that can transport riders around an indoor environment.