Personalized exercise training Chatbot based on wearable fitness devices

Abstract

This report presents a personalized exercise training chatbot for individual users based on data collected from the Internet of Things (IoT), particularly wearable fitness devices. The chatbot is designed with our goal of motivating users to exercise more by discussing exercise statistics with the user, such as whether their daily steps have increased, decreased, or remained steady. In this work I first survey a few examples of how increased interest in fitness and the promotion of healthy lifestyles is driving demand for personalized artificial intelligence, wear- able computing, and ubiquitous computing applications. Next, I describe the design of a data-driven ”personal trainer” chatbot. I then develop a prototype persuasion system based on interactive dialogs delivered via a front-end application, that collects data from wearable equipment using back-end data loggers that I instrumented as a mobile application. Finally, I describe the process of deploying and demonstrating this prototype along with technical challenges and early findings. The overall system consists of (1) the back-end Coach agent, an Android application that collects data from all wearable instruments, and (2) the front-end Me agent, which initiates and continues conversations with the user using notifications that are in turn based on data from the Coach agent. This data-driven ensemble reminds the user to exercise and also gives the user a chance to provide feedback via human/agent interactive dialogs. In this project, I used only one wearable device, the MI Band 2, and get real-time steps and weekly step aggregates from it. The human/agent dialogues are deployed via the Slack groupware platform. Google Sheets is used as a web service for updating and exchanging data.