Reflective Dialogues with a Humanoid Robot Integrated with an LLM and a Curated NLU System for Positive Behavioral Change in Older Adults

We developed an innovative system that combines Natural Language Understanding (NLU), a curated knowledge base, and the efficient management of a Large Language Model (LLM) to support motivational health coaching. Using Rasa as the core framework, we enhanced it by integrating the GPT-3.5-turbo model. Users opt into reflective dialogues during conversations. When they respond to open-ended questions, their input goes directly to the GPT-3.5-turbo model, allowing for more flexible responses. To provide curated trustworthy content, we integrated a knowledge provision component that searches a PDF-based knowledge base and generates user-friendly responses using Retrieval-Augmented Generation. We tested the system in a real-world scenario by deploying it on a Nao robot in seven older adults’ homes for 1–2 weeks, encouraging positive behavioral changes in some users. Our system serves as a valuable foundation for building an even more integrated, personalized system that can connect with other Application Programing Interfaces (APIs) and integrate with home sensors and edge devices.

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Publication Details

Journal
Electronics
Published
2024-11-07
DOI
https://doi.org/10.3390/electronics13224364
Citations
5
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
3.22

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article

Reflective Dialogues with a Humanoid Robot Integrated with an LLM and a Curated NLU System for Positive Behavioral Change in Older Adults

Qasid Saleem, Ryan Browne, Kristiina Jokinen, Keiko Homma et al.
5 citations
Electronics
Social Robot Interaction and HRI
3.22
article

Reflective Dialogues with a Humanoid Robot Integrated with an LLM and a Curated NLU System for Positive Behavioral Change in Older Adults

Qasid Saleem, Ryan Browne, Kristiina Jokinen, Keiko Homma, Yasuyuki Taki, Michael McTear, Giulio Napolitano, Toshimi Ogawa, Andrew Hyder, Mirza Mohtashim Alam, Kyoung‐Sook Kim, Martino Maggio, Rainer Wieching, Francesca D’Agresti, Jun’ichi Tsujii, T Kudo, Naoko Kounosu, Eerik-Juhanna Siitonen
article en
5 citations

Abstract

We developed an innovative system that combines Natural Language Understanding (NLU), a curated knowledge base, and the efficient management of a Large Language Model (LLM) to support motivational health coaching. Using Rasa as the core framework, we enhanced it by integrating the GPT-3.5-turbo model. Users opt into reflective dialogues during conversations. When they respond to open-ended questions, their input goes directly to the GPT-3.5-turbo model, allowing for more flexible responses. To provide curated trustworthy content, we integrated a knowledge provision component that searches a PDF-based knowledge base and generates user-friendly responses using Retrieval-Augmented Generation. We tested the system in a real-world scenario by deploying it on a Nao robot in seven older adults’ homes for 1–2 weeks, encouraging positive behavioral changes in some users. Our system serves as a valuable foundation for building an even more integrated, personalized system that can connect with other Application Programing Interfaces (APIs) and integrate with home sensors and edge devices.

ElectronicsVol. 13(22)
University of Ulster (GB), Tohoku University (JP), University of Siegen (DE), Informa (Italy) (IT), Institut für Angewandte Trainingswissenschaft (DE), Engineering (Italy) (IT), National Institute of Advanced Industrial Science and Technology (JP)
European Commission
Quality Education
Openalex Percentile: Top 8%
Social Robot Interaction and HRI
3.22
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