Human activity recognition using wearable sensors

Title

Human activity recognition using wearable sensors

Creator

Joy Rakesh Y.; Kavitha R.; Julian J.

Description

The advancement of the internet coined a new era for inventions. Internet of Things (IoT) is one such example. IoT is being applied in all sectors such as healthcare, automobile, retail industry etc. Out of these, Human Activity Recognition (HAR) has taken much attention in IoT applications. The prediction of human activity efficiently adds multiple advantages in many fields. This research paper proposes a HAR system using the wearable sensor. The performance of this system is analyzed using four publicly available datasets that are collected in a real-time environment. Five machine learning algorithms namely Decision tree (DT), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (kNN), and Support Vector Machine (SVM) are compared in terms of recognition of human activities. Out of this SVM responded well on all four datasets with the accuracy of 77%, 99%, 98%, and 99% respectively. With the support of four datasets, the obtained results proved that the performance of the proposed method is better for human activity recognition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

Source

Advances in Intelligent Systems and Computing, Vol-1177, pp. 527-538.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Activity recognition; Classification; Machine leaning; Sensors; Wearable computing

Coverage

Joy Rakesh Y., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, India; Kavitha R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, India; Julian J., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, India

Rights

Restricted Access

Relation

ISSN: 21945357; ISBN: 978-981155678-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-15-5679-1_51

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091061514&doi=10.1007%2f978-981-15-5679-1_51&partnerID=40&md5=86b5ac4728a2787d3f83db8f32c1e6e6