Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data

Bolu Oluwalade, Sunil Neela, Judy Wawira, Tobiloba Adejumo, Saptarshi Purkayastha

2021

Abstract

In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.

Paper Citation

in Harvard Style

Oluwalade B., Neela S., Wawira J., Adejumo T. and Purkayastha S. (2021). Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF; ISBN 978-989-758-490-9, SciTePress, pages 645-650. DOI: 10.5220/0010325906450650

in Bibtex Style

@conference{healthinf21,
author={Bolu Oluwalade and Sunil Neela and Judy Wawira and Tobiloba Adejumo and Saptarshi Purkayastha},
title={Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF},
year={2021},
pages={645-650},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010325906450650},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF
TI - Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data
SN - 978-989-758-490-9
AU - Oluwalade B.
AU - Neela S.
AU - Wawira J.
AU - Adejumo T.
AU - Purkayastha S.
PY - 2021
SP - 645
EP - 650
DO - 10.5220/0010325906450650
PB - SciTePress