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A Decision Tree-Based Smart Fitness Framework in IoT

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Abstract

The Internet of Things (IoT) provides opportunities for improving various aspects of human life and enhancing quality of life. It enables monitoring and controlling mechanisms to be more easier than it was in the past. One of the significant objects in people’s life is fitness and body activity. Using IoT and its related technologies like RFID (Radio Frequency Identification), AI (Artificial Intelligence), and WSN (Wireless Sensor Networks), can reduce cost and increase accuracy for fitness monitoring. The problem in current smart fitness and fitness monitoring systems is that there is not a complete monitoring and workout plan provider in these scenarios, especially in non-wearable sensors which are most placed on fitness devices. In this paper, an architecture is proposed that can monitor actions which is executed by sensor-embedded on fitness machines. The paper explores the implementation and examination for biceps and triceps actions. For processing data, Decision Tree Classifier (DTC) was used to reduce computational tasks and make decisions about future workout plans for each user.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Azad University, North Tehran Branch, Tehran, Iran

    Alireza Farrokhi

  2. University of Technology Sydney (UTS), Sydney, Australia

    Javad Rezazadeh

  3. Institut Mines-Telecom, Telecom Sud-Paris, Evry, France

    Reza Farahbakhsh

  4. Crown Institute of Higher Education, Sydney, Australia

    John Ayoade

  5. Azad University, North Tehran Branch, Tehran, Iran

    Javad Rezazadeh

Authors
  1. Alireza Farrokhi
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  2. Javad Rezazadeh
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  3. Reza Farahbakhsh
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  4. John Ayoade
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Correspondence to Javad Rezazadeh.

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Farrokhi, A., Rezazadeh, J., Farahbakhsh, R. et al. A Decision Tree-Based Smart Fitness Framework in IoT. SN COMPUT. SCI. 3, 2 (2022). https://doi.org/10.1007/s42979-021-00940-x

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