Research Article
A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones
@ARTICLE{10.4108/eai.2-11-2021.171752, author={Umair Saeed and Kamlesh Kumar and Asif Ali Laghari and Mansoor Ahmed Khuhro and Noman Islam and Ghulam Muhammad Shaikh and Fahad Hussain and Aftab Ahmed Shaikh}, title={A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={8}, number={30}, publisher={EAI}, journal_a={PHAT}, year={2021}, month={11}, keywords={Human activity recognition, multi-class classification, Smart-phone, Wearable sensors, Classifiers, Sensor devices, Business intelligence}, doi={10.4108/eai.2-11-2021.171752} }- Umair Saeed
Kamlesh Kumar
Asif Ali Laghari
Mansoor Ahmed Khuhro
Noman Islam
Ghulam Muhammad Shaikh
Fahad Hussain
Aftab Ahmed Shaikh
Year: 2021
A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones
PHAT
EAI
DOI: 10.4108/eai.2-11-2021.171752
Abstract
INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate. These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS. By using these sensors to analyze the activity of the end-user, behavioural characteristics of the user can be captured.
OBJECTIVES: Although smart-phone and wearable devices provide a platform for conducting social, psychological and physical studies, they still have several limitations and challenges.
METHODS: This paper provides a comparative analysis of different classical Machine Learning and Deep Learning algorithms and discusses their accuracy and efficiency for human activity recognition (HAR).
RESULTS and CONCLUSION: The paper has primarily used the data captured using wireless sensor devices placed on different parts of a human body, and then compared the results for different classifiers. The conclusion shows that Deep learning schemes are extremely accurate and efficient in comparison with classical machine learning techniques.
Copyright © 2021 Umair Saeed et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
