Volume 12, Number 1

IoT-based Activity Recognition with Machine Learning from Smartwatch

  Authors

Nassim Mozaffari^c, Javad Rezazadeh^a,c, Reza Farahbakhsh^b, John Ayoade^a, ^aKent Institute Australia, Australia, ^bInstitut Mines-Te´le´com, Te´le´com SudParis and ^cIslamic Azad University, Iran

  Abstract

Human activity recognition(HAR) with wearable Internet of Things (IoT) sensors can be beneficial for the elderly and patients monitoring. Smartwatches are the most accessible IoT devices that play an important role in human activity monitoring. The structure of an activity recognition system involves a platform that holds wearable sensors. Under the background, many platforms such as distributed sensors and smartphones and the combination of them have been investigated but platforms are still one of the main research challenges. Smartwatches can be more comfortable for the elderly and patients, therefore our research is focused on a smartwatch as an emerging IoT platform and machine learning method. The smartwatch attached to arm as the main position then was compared to other positions. We considered machine learning methods to present the smartwatch as a reliable platform in order to recognize activities, also we considered k-nearest neighbor and decision tree as two popular machine learning methods for activity recognition. We evaluated the performance with the confusion matrix and then we used accuracy and f1-score metrics for the result of our experiment. The metrics show accuracy and f1-score almost 99% as the performance of smartwatch on arm position.

  Keywords

Activity Recognition, SmartWatch, Machine Learning