Video-mediated support group interaction

Support groups help people to learn from others who share similar experiences; they are known to be effective in reducing stress caused by negative life events. With broader access to the Internet, support groups have also expanded into video conferencing format. In-person and remote support groups are led by facilitators with wide-ranging backgrounds and qualifications. Unfortunately, such facilitators often suffer from burnout, leading to support group closure. Hence, automated facilitators offer a way for maintaining support groups when human facilitators are unavailable. The main aims of this project are (i) to identify and evaluate the characteristics of an effective autonomous group facilitator; (ii) to study and develop computational methods for measuring individual engagement and group cohesion in video-mediated multiparty interaction; and (iii) to develop and evaluate an autonomous group facilitator that can maximize group cohesion through computational means. To achieve these aims, this project builds and studies an autonomous agent facilitator in the form of a socially assistive robot for remote support groups via Zoom or a similar platform. The interpersonal connectedness and alliances in a group make a support group more effective.

The research incorporates the study of expressive robot and agent embodiment, algorithm for autonomous conversation facilitation, and user engagement for novel facilitation strategies. To this end, the project will first use a human-driven agent, through a Wizard-of-Oz (WoZ) strategy, to design the agent’s action space (both verbal and nonverbal behaviors) necessary for moderating a support group. The WoZ study will also test the hypothesis that an embodied agent facilitator is as effective as a human facilitator in engaging users and projecting competence to group participants. After coding the data recorded during the WoZ study, multimodal machine learning models will be trained for the automatic recognition of engagement and conversational stages and acts. Group cohesion will be assessed based on dyadic engagement and individual responses, through network analysis. The research team will finally build an autonomous facilitator leveraging a reinforcement learning model that optimizes for increasing group cohesion. The autonomous facilitator will be evaluated against a second agent that optimizes for equal access to the conversational floor, in terms of individual engagement and group cohesion assessed by post-session questionnaires. This work will build technologies for automated group facilitation that can assist to bridge the gaps in delivering support groups when human facilitators are absent or in short supply.

Team

Support

This project is funded by the NSF.

Title: “HCC: Medium: Agent-Facilitated, Video-Mediated Multiparty Interactions in Support Groups”

Award Number: 2211550

Publications

Publications will be posted here.

  1. M. Tran, Y. Yin, Y., M. Soleymani, “Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition,” Proc. INTERSPEECH 2023, Dublin, Ireland, 2023, pp. 636-640, doi: 10.21437/Interspeech.2023-2170
  2. E. Zhou, M. Soleymani and M. J. Matarić, “Investigating the Generalizability of Physiological Characteristics of Anxiety,” 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 4848-4855, doi: 10.1109/BIBM58861.2023.10385292