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It is important to protect health and improve quality of life for people, without causing them inconvenience in today's world. Since most people are living a busy life dealing with various activities at work, school, or home, there is a need for systematic analysis of their life patterns. However, since person's life patterns could change depending on ambient environmental factors, an effective management scheme to specify one's state is required. We propose a method, in this paper, to support and enhance the personal healthy life patterns by analyzing the daily life data that has been continuously recorded by wearable sensors, such as activity trackers. We implement a mobile wellness management system by learning RNN-based user's lifestyle model, and developing behavior recommendation using greedy policy. We also consider user context and feedback to personalize each user's lifestyle.

Developing a Mobile Wellness Management System for Healthy Lifestyle by Analyzing Daily Living Activities