Mobile Networks and Applications

, Volume 17, Issue 2, pp 163–177 | Cite as

Machine Learning-Based Adaptive Wireless Interval Training Guidance System

  • Myung-kyung Suh
  • Ani Nahapetian
  • Jonathan Woodbridge
  • Mahsan Rofouei
  • Majid Sarrafzadeh
Article

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.

Keywords

wireless health embedded system exercise guidance system interval training wearable wireless sensors music recommendation social networks rehabilitation data mining Bayesian-network J48 tree 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Myung-kyung Suh
    • 1
  • Ani Nahapetian
    • 2
    • 3
  • Jonathan Woodbridge
    • 1
  • Mahsan Rofouei
    • 1
  • Majid Sarrafzadeh
    • 1
    • 4
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA
  2. 2.Computer Science DepartmentCalifornia State University, Northridge (CSUN)Los AngelesUSA
  3. 3.Computer Science Department, UCLALos AngelesUSA
  4. 4.Wireless Health InstituteUniversity of CaliforniaLos AngelesUSA

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