Evaluation of Deep Learning Models for Smoking Recognition with Smartwatch and Smartphone Sensors

Year 2021, Volume: 9 Issue: 4, 354 - 364, 30.10.2021

Yasemin Akan Sümeyye Ağaç Özlem Durmaz İncel

https://doi.org/10.17694/bajece.827342

Abstract

Smartwatches and smartphones are extensively used in human activity recognition, particularly for step counting and daily sports applications, thanks to the motion sensors integrated into these devices. Machine learning algorithms are often utilized to process sensor data and classify the activities. There are many studies that explore the use of traditional classification algorithms in activity recognition, however, recently, deep learning approaches are also receiving attention. In this paper, we use a dataset that particularly consists of smoking-related activities and explores the recognition performance of three deep learning architectures, namely Long-Short Term Memory (LSTM)}, Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). We evaluate their performances according to different hyperparameters, different sensor types and device types. The results show that the performance of LSTM is much higher than that of CNN and RNN. Moreover, the use of magnetometer and gyroscope together with accelerometer data improves the performance. Use of data from smartphone sensors also enhances the performance results and the final accuracy with the best parameter combinations is observed to be 98%.

Keywords

Deep learning, Sensors, Activity recognition

Supporting Institution

Tübitak

Project Number

117E761

Thanks

This work is supported by Galatasaray University Research Fund under Grant Number 17.401.004 and by Tubitak under Grant Number 117E761.

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