Adaptive Learning Models for Getting Insights into Multimodal Lifelog Data

Abstract

Regular exercise and scientific eating can support weight control and benefit everyone’s health, especially athletes. In recent years, although much research has been conducted in this field, only small groups of people were studied, and a few models revealed links between weight and speed attributes (e.g., activities, wellbeing, habits) to extract tips to assist people in controlling their weight and running speed. In this research, we propose an approach that uses pattern mining and correlation discovery techniques to discover the most optimal attributes over time to forecast the weight and speed of an athlete for a sports event. Furthermore, we propose Adaptive Learning Models, which can learn from personal and public data to forecast a person’s weight or speed in various age groups, such as young adults, middle-aged adults, and female or male members. Based on the above analysis, different approaches to building prediction models of athletes’ weight or running speed are being examined based on the primary data. Our suggested approach yields encouraging results when tested on public and private data sets.