Towards Automatic & Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective

TL;DR

This paper explores an interactive machine learning system designed for personalized mobile health interventions, aiming to promote healthy lifestyles and prevent chronic diseases through automated, user-specific support.

Contribution

It introduces a novel architecture and process for integrating interactive machine learning into telemedicine for personalized health promotion.

Findings

Preliminary implementation results demonstrate system feasibility.

Profiles and feedback effectively inform personalized interventions.

Future work includes system refinement and broader testing.

Abstract

Machine learning (ML) is the fastest growing field in computer science and healthcare, providing future benefits in improved medical diagnoses, disease analyses and prevention. In this paper, we introduce an application of interactive machine learning (iML) in a telemedicine system, to enable automatic and personalised interventions for lifestyle promotion. We first present the high level architecture of the system and the components forming the overall architecture. We then illustrate the interactive machine learning process design. Prediction models are expected to be trained through the participants' profiles, activity performance, and feedback from the caregiver. Finally, we show some preliminary results during the system implementation and discuss future directions. We envisage the proposed system to be digitally implemented, and behaviourally designed to promote healthy lifestyle and activities, and hence prevent users from the risk of chronic diseases.