A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data

Author:

M’hamed Abidine Bilal,Fergani Belkacem,Oussalah Mourad,Fergani Lamya

Abstract

Purpose – The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues. Design/methodology/approach – In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem. Findings – The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors. Originality/value – Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

Publisher

Emerald

Subject

Computer Science (miscellaneous),Social Sciences (miscellaneous),Theoretical Computer Science,Control and Systems Engineering,Engineering (miscellaneous)

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DWOSC: Dynamic Weight Optimization and Smoothness Constraint for Sensor-Based Human Activity Recognition;IEEE Transactions on Instrumentation and Measurement;2024

2. An Efficient Kernel KNN classifier for Activity Recognition on Smartphone;2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA);2022-12-19

3. A Parkinson’s disease diagnosis approach for nonequilibrium gait data;International Journal of Modeling, Simulation, and Scientific Computing;2022-07-09

4. A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses;IEEE Sensors Journal;2021-11-01

5. Violent activity recognition by E-textile sensors based on machine learning methods;Journal of Intelligent & Fuzzy Systems;2020-12-04

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