Machine Learning-Based Adaptive Wireless Interval Training Guidance System

Authors:
Myung-Kyung Suh;Ani Nahapetian;Jonathan Woodbridge;Mahsan Rofouei;Majid Sarrafzadeh
Affiliations:
Computer Science Department, University of California, Los Angeles, USA;Computer Science Department, California State University, Northridge (CSUN), Los Angeles, USA and Computer Science Department, UCLA, Los Angeles, USA;Computer Science Department, University of California, Los Angeles, USA;Computer Science Department, University of California, Los Angeles, USA;Computer Science Department, University of California, Los Angeles, USA and Wireless Health Institute, University of California, Los Angeles, USA
Venue:
Mobile Networks and Applications
Year:
2012

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Abstract

Interval training has been shown to improve the physical and psychological performance of users, in terms of fatigue level, cardiovascular build-up, hemoglobin concentration, and self-esteem. Despite the benefits, there is no known automated method for formulating and tailoring an optimized interval training protocol for a specific individual that maximizes the amount of calories burned while limiting fatigue. Additionally, an application that provides the aforementioned optimal training protocol must also provide motivation for repetitious and tedious exercises necessary to improve a patient's adherence. This paper presents a system that efficiently formulates an optimized interval training method for each individual by using data mining schemes on attributes, conditions, and data gathered from individuals exercise sessions. This system uses accelerometers embedded within iPhones, a Bluetooth pulse oximeter, and the Weka data mining tool to formulate optimized interval training protocols and has been shown to increase the amount of calories burned by 29.54% as compared to the modified Tabata interval training protocol. We also developed a behavioral cueing system that uses music and performance feedback to provide motivation during interval training exercise sessions. By measuring a user's performance through sensor readings, we are able to play songs that match the user's workout plan. A hybrid collaborative, content, and context-aware filtering algorithm incorporates the user's music preferences and the exercise speed to enhance performance.