This study presents an adaptive human-robot interaction (HRI) system that evaluates older adult participants' satisfaction with personalized health care programs. By integrating the CLOi robot with a large language model (LLM), the system conducts satisfaction surveys that adapt in real-time to participant responses. The system was applied to evaluate healthcare programs that include physical health measurements, exercise assessments, and virtual reality (VR) experiences. The system utilizes the CLOi robot and Claude API to analyze response clarity in real-time, automatically generating contextually appropriate follow-up questions when responses are deemed ambiguous. This adaptive questioning strategy ensures comprehensive response quality before proceeding to subsequent survey items. We conducted a preliminary feasibility study with five older adult participants to evaluate our approach. The system leverages LLM prompts to analyze gaps between question intent and participant responses, generating targeted follow-up questions as needed. Results demonstrate that our LLM-enhanced robotic interview system effectively reduced response ambiguity through dynamic follow-up questioning, achieving an 85% response resolution rate. This adaptive approach improved the clarity and specificity of healthcare satisfaction assessments for older adults.

Deep learning, based on which many modern algorithms operate, is well known to be data-hungry. In particular, the datasets appropriate for the intended application are difficult to obtain. To cope with this situation, we introduce a new dataset called ETRI-Activity3D, focusing on the daily activities of the elderly in robot-view. The major characteristics of the new dataset are as follows: 1) practical action categories that are selected from the close observation of the daily lives of the elderly; 2) realistic data collection, which reflects the robot's working environment and service situations; and 3) a large-scale dataset that overcomes the limitations of the current 3D activity analysis benchmark datasets. The proposed dataset contains 112,620 samples including RGB videos, depth maps, and skeleton sequences. During the data acquisition, 100 subjects were asked to perform 55 daily activities. Additionally, we propose a novel network called four-stream adaptive CNN (FSA-CNN). The proposed FSA-CNN has three main properties: robustness to spatio-temporal variations, input-adaptive activation function, and extension of the conventional two-stream approach. In the experiment section, we confirmed the superiority of the proposed FSA-CNN using NTU RGB+D and ETRI-Activity3D. Further, the domain difference between both groups of age was verified experimentally. Finally, the extension of FSA-CNN to deal with the multimodal data was investigated.

In this paper, we identify the effects of robot social skills for enhancing social interaction in a physical training. To that end, we designed a physical training scenario and conducted an experiment with 28 participants using the humanoid robot NAO. As a result, there were significant differences between the control group where social skills were not used, and the experimental group where social skills were used by the robot.

This paper presents a process to build a classifier in a data-driven way for recognizing engagement of children in a robot-based math quiz game. The process consists of collecting video recordings from HRI experiments; annotating the social signals and engagement states via video analysis; extracting feature vectors from the annotations and training classifiers. We conducted an experiment with 7 participants of 10 -- 11 years of age using an android robot EveR-4. With three coders annotating the video recordings and extracting features by snapshot model with 1-second time window, we achieved 84.83% recall performance.

This paper describes a location and emergency monitoring system, which aims to monitor an emergency situation, activities and location for elder care. The watch-type device detect emergency such as emergency call or fall. The collected data through the ZigBee network is send to a DB server and stored in a DB. A care staff recognizes an emergency message via SMS or monitoring interface. At the same time, robot moves to an emergency location and sends video stream to a caregiver using tele-presence. A caregiver copes with an emergency. This system was tested in a retirement village.