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The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

In: Advances in Statistical Methodologies and Their Application to Real Problems

Author

Listed:
  • Serkan Balli
  • Ensar Arif Sagbas

Abstract

The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.

Suggested Citation

  • Serkan Balli & Ensar Arif Sagbas, 2017. "The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches," Chapters, in: Tsukasa Hokimoto (ed.), Advances in Statistical Methodologies and Their Application to Real Problems, IntechOpen.
    • Handle: RePEc:ito:pchaps:107726
    DOI: 10.5772/66213
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    More about this item

    Keywords

    statistical learning; activity recognition; wearable devices; smartwatch; Bayesian networks;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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