Abidine, M.B., Oussalah, M., Fergani, B. and Lounis, H. (2022), "Activity recognition on smartphones using an AKNN based support vectors", Sensor Review, Vol. 42 No. 4, pp. 384-401. https://doi.org/10.1108/SR-05-2021-0157
Activity recognition on smartphones using an AKNN based support vectors
|Author:||Abidine, M’hamed Bilal1; Oussalah, Mourad2; Fergani, Belkacem1;|
1Department of Electronics and Electrical Engineering, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
2Faculty of Information Technology and Electrical Engineering, Oulun Yliopisto, Oulu, Finland
3Department of, Université du Québec à Montréal, Montréal, Canada
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022092259873
|Publish Date:|| 2022-09-22
Purpose: Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.
Design/methodology/approach: The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.
Findings: Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.
Practical implications: This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.
Originality/value: The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.
|Pages:||384 - 401|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The research presented in this paper is partly funded by Algeria National and Development Program PRFU (Projets de Recherche FormationUniversitaire) with Key Research project (A25N01UN160420180003), Communication and Intelligent System Engineering Laboratory. The authors also acknowledge the Academy of Finland Project Profi5 (#326291) on DigiHealth.
© 2022, M'hamed Bilal Abidine, Mourad Oussalah, Belkacem Fergani and Hakim Lounis. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.