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Machine Learning-Based Adaptive Wireless Interval Training Guidance System

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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.

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Acknowledgement

This research was supported by NLM (National Library of Medicine).

I would like to thank Alfred Heu and Kyujoong Lee for their help in performing the provided experiments, and to Professor William Kaiser for his helpful advice.

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Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, CA, USA

    Myung-kyung Suh, Jonathan Woodbridge, Mahsan Rofouei & Majid Sarrafzadeh

  2. Computer Science Department, California State University, Northridge (CSUN), Los Angeles, CA, USA

    Ani Nahapetian

  3. Computer Science Department, UCLA, Los Angeles, CA, USA

    Ani Nahapetian

  4. Wireless Health Institute, University of California, Los Angeles, CA, USA

    Majid Sarrafzadeh

Authors
  1. Myung-kyung Suh
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  2. Ani Nahapetian
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  3. Jonathan Woodbridge
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  4. Mahsan Rofouei
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  5. Majid Sarrafzadeh
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Corresponding author

Correspondence to Myung-kyung Suh.

Additional information

This publication was partially supported by Grant Number T15 LM07356 from the NIH/National Library of Medicine Medical Informatics Training Program

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Suh, Mk., Nahapetian, A., Woodbridge, J. et al. Machine Learning-Based Adaptive Wireless Interval Training Guidance System. Mobile Netw Appl 17, 163–177 (2012). https://doi.org/10.1007/s11036-011-0331-5

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  • DOI: https://doi.org/10.1007/s11036-011-0331-5

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