Many social robots have emerged in public places to serve people. For these services, the robots are assumed to be able to present internal aspects (i.e., mind, sociability) to engage and interact with people over the long term. In this paper, we propose a novel dialogue structure called experience-based dialogue to help a robot present and maintain a good interaction over the long term. This dialogue structure contains a piece of knowledge and a story about how the robot gained this knowledge, which are used to compose the robot’s experience-related utterances for sharing experiences of interacting with previous users other than just the current user and help it present its internal aspects. We conducted an experiment to test the effects of our proposed dialogue structure and measure them with some published subjective scales. The results showed that experience-based dialogue can help a robot obtain better evaluations in terms of perceived intelligence, sociability, mind, anthropomorphism, animacy, likability, level of acceptance, and positive user reaction.

Mental health issues are receiving more and more attention in society. In this paper, we introduce a preliminary study on human–robot mental comforting conversation, to make an android robot (ERICA) present an understanding of the user’s situation by sharing similar emotional experiences to enhance the perception of empathy. Specifically, we create the emotional speech for ERICA by using CycleGAN-based emotional voice conversion model, in which the pitch and spectrogram of the speech are converted according to the user’s mental state. Then, we design dialogue scenarios for the user to talk about his/her predicament with ERICA. In the dialogue, ERICA shares other people’s similar predicaments and adopts a low-spirit voice to express empathy to the interlocutor’s situation. At the end of the dialogue, ERICA tries to encourage with a positive voice. Subsequently, questionnaire-based evaluation experiments were conducted with the recorded conversation. In the questionnaire, we use the Big Five scale to evaluate ERICA’s personality. In addition, the perception of emotion, empathy, and encouragement in the dialogue are evaluated. The results show that the proposed emotional expression strategy helps the android robot better present low-spirit emotion, empathy, the personality of extroversion, while making the user better feel the encouragement.

Social connectedness is vital for developing group cohesion and strengthening belongingness. However, with the accelerating pace of modern life, people have fewer opportunities to participate in group-building activities. Furthermore, owing to the teleworking and quarantine requirements necessitated by the Covid-19 pandemic, the social connectedness of group members may become weak. To address this issue, in this study, we used an android robot to conduct daily conversations, and as an intermediary to increase intra-group connectedness. Specifically, we constructed an android robot system for collecting and sharing recent member-related experiences. The system has a chatbot function based on BERT and a memory function with a neural-network-based dialog action analysis model. We conducted a 3-day human-robot conversation experiment to verify the effectiveness of the proposed system. The results of a questionnaire-based evaluation and empirical analysis demonstrate that the proposed system can increase the familiarity and closeness of group members. This suggests that the proposed method is useful for enhancing social connectedness. Moreover, it can improve the closeness of the user-robot relation, as well as the performance of robots in conducting conversations with people.

The diagnosis of depression in individuals with Parkinson's Disease (PD) through the utilization of multimodal fusion techniques represents a significant domain. The primary challenge involves the creation of a robust fusion framework to address the heterogeneity among different modalities effectively. However, previous studies primarily focused on interactions between heterogeneous data, neglecting the structural similarities among isomorphic data, resulting in a substantial loss of feature information when merging heterogeneous data. In this study, we introduced a multi-modal data image encoding and fusion approach for diagnosing depression in PD patients. Additionally, we proposed a multi-modal dataset encompassing motion, facial expression, and audio data. Firstly, we designed an RGB and sparse coding method to encode the multi-modal data, achieving the isomorphic transformation of multi-modal information and extracting feature information from lower-dimensional spaces. Furthermore, we introduced a Spatial-Temporal Network (STN) to fuse the three types of encoded images. We incorporated the Relation Global Attention (RGA) to enhance feature extraction and leverage all encoded image location feature nodes for balanced decision attention. Finally, recognizing the limitations of traditional machine learning algorithms in handling multi-tasks in medical diagnosis, we established a multi-task weighted loss function to achieve depression identification and severity prediction through Multi-Task learning (MTL).