TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment - Johnvictor AC; Poonkodi M; Prem Sankar N; VS T (Journal of Multidisciplinary Healthcare)
Anita Christaline Johnvictor,1 M Poonkodi,1 N Prem Sankar,1 Thinesh VS2 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India; 2Arista Networks Pvt Ltd, Bangalore, IndiaCorrespondence: Anita Christaline Johnvictor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai campus, Kelambakkam-Vandalur Road, Rajan Nagar, Chennai, 600127, India, Email anitachristaline.j@vit.ac.inIntroduction: In healthcare applications, AI-driven innovations are set to revolutionise patient interactions and care, with the aim of improving patient satisfaction. Recent advancements in Artificial Intelligence have significantly affected nursing, assistive management, medical diagnoses, and other critical medical procedures.Purpose: Many artificial intelligence (AI) solutions operate online, posing potential risks to patient data security. To address these security concerns and ensure swift operation, this study has developed a chatbot tailored for hospital environments, running on a local server, and utilising TinyML for processing patient data.Patients and Methods: Edge computing technology enables secure on-site data processing. The implementation includes patient identification using a Histogram of Gradient (HOG)-based classification, followed by basic patient care tasks, such as temperature measurement and demographic recording.Results: The classification accuracy of patient detection was 95.8%. An autonomous temperature-sensing unit equipped with a medical-grade infrared temperature scanner detected and recorded patient temperature. Following the temperature assessment, the tinyML-powered chatbot engaged patients in a series of questions customised by doctors to train the model for diagnostic scenarios. Patients’ responses, recorded as “yes” or “no”, are stored and printed in their case sheet. The accuracy of the TinyML model is 95.3% and the on-device processing time is 217 ms. The implemented TinyML model uses only 8.8Kb RAM and 50.3Kb Flash memory, with a latency of only 4 ms.Conclusion: Each patient was assigned a unique ID, and their data were securely stored for further consultation and diagnosis via hospital management. This research demonstrates faster patient data recording and increased security compared to existing AI-based healthcare solutions, as all processes occur within the local host.Keywords: edge computing, healthcare, HOG descriptors, assistive management, autonomous robot