A Human Trial Study on Overcoming Linguistic and Stress-Related Barriers in Chronic Disease Management

Witharamulage Nikhil Yeshmantha; Walisarage Anne Thushini Amasha Fernando; Kuruppuarachchige Kumashi Sakunika; Sanvitha Kasthuriarachchi; Mihiri Samaraweera

doi:10.46604/emsi.2024.15059

Authors

Witharamulage Nikhil Yeshmantha Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Walisarage Anne Thushini Amasha Fernando Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Kuruppuarachchige Kumashi Sakunika Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Sanvitha Kasthuriarachchi Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Mihiri Samaraweera Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

DOI:

https://doi.org/10.46604/emsi.2024.15059

Keywords:

AI chatbot, Augmented Reality, chronic disease management, Sinhala NLP, stress reduction

Abstract

The study addresses language and stress barriers in chronic disease management among Sinhala-speaking patients, using an Augmented Reality (AR) embedded platform combined with an Artificial Intelligence (AI) chatbot. Rasa is integrated into the AI chatbot using Bidirectional Encoder Representations from Transformer (BERT)-Sinhala embeddings to deliver culturally tailored, real-time health recommendations. An AR module on AR.js and Three.js implements personalized stress-reduction interventions. The chatbot achieves an F1 score of 91% and a word error rate (WER) of 8.5%, while the AR system reduces systolic blood pressure by 8.96% (p = 0.002). The combined platform attains 88% scenario accuracy with a mean response time of 1.2 seconds. These findings support the system’s potential to improve healthcare access and reduce stress markers among Sinhala-speaking chronic patients, offering a low-resource, scalable, and culturally appropriate model for healthcare environments.

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