Optimizing Interval Training Protocols Using Data Mining Decision Trees

2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks Pub Date : 2009-06-03 DOI:10.1109/BSN.2009.17

Myung-kyung Suh, Mahsan Rofouei, A. Nahapetian, W. Kaiser, M. Sarrafzadeh

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引用次数: 5

Abstract

Interval training consists of interleaving high intensity exercises with rest periods. This training method is a well known exercise protocol which helps strengthen and improve one’s cardiovascular fitness. However, there is no known method for formulating and tailoring an optimized interval training protocol for a specific individual which maximizes the amount of work done while limiting fatigue. But by using data mining schemes with various attributes, conditions, and data gathered from an individual’s exercise session, we are able to efficiently formulate an optimized interval training method for an individual. Recent advances in wireless wearable sensors and smart phones have made available a new generation of fitness monitoring systems. With accelerometers embedded in an iPhone, a Bluetooth pulse oximeter, and the Weka data mining tool, we are able to formulate the optimized interval training protocols, which can increase the amount of calorie burned up to 29.54%, compared with the modified Tabata interval training protocol.

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利用数据挖掘决策树优化间隔训练协议

间歇训练包括高强度运动与休息时间的交替。这种训练方法是一种众所周知的运动方案，有助于加强和改善一个人的心血管健康。然而，目前还没有一种已知的方法可以为特定的个人制定和定制优化的间歇训练方案，从而在限制疲劳的同时最大限度地完成工作量。但是，通过使用从个人锻炼过程中收集的各种属性、条件和数据挖掘方案，我们能够有效地为个人制定优化的间歇训练方法。无线可穿戴传感器和智能手机的最新进展使新一代健身监测系统成为可能。通过在iPhone中嵌入加速度计，蓝牙脉搏血氧仪和Weka数据挖掘工具，我们能够制定优化的间歇训练方案，与改进的Tabata间歇训练方案相比，可以增加29.54%的卡路里燃烧量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks

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