Impacts of feature selection on classification of individual activity recognitions for prediction of crowd disasters. (9th October 2019)

• Record Type: Journal Article
• Authors: Sadiq, Fatai Idowu; Selamat, Ali; Krejcar, Ondrej; Ibrahim, Roliana
• Is Part Of: International journal of intelligent information and database systems. Volume 12:Number 3(2019)
• Page Start: 179
• Page End: 198
• Publication Date: 2019-10-09
• Subjects: statistical-based time frequency domain -- SBTFD -- human activity recognition -- HAR -- minimum redundancy maximum relevance -- MRMR -- chi-square -- dimensionality reduction; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

We examined the possibility of feature selection using statistical based time frequency domain (SBTFD) extracted features for human activity recognition. This is to reduce the dimensionality of features on monitoring devices to improve accuracy and minimize false negative alarm for crowd disasters. We analysed 54 SBTFD features obtained from 22, 350 instances comprising of a climb down, climb up, peak shake while standing, standing, still, and walking; as classes V1, V2, to V8. Also, the benchmark dataset of 274, 214 instances from nine users for accelerometer signals. Both datasets were subjected to minimum redundancy maximum relevance with information gain (MRMR-IG), correlation and chi-square techniques to select the relevant SBTFD features. We applied ten-fold cross validation using WEKA with four classifiers to classify individual behaviour classes V1 to V8. We achieved 97.8% accuracy and false negative rate of 9.5% to save human lives from crowd disasters with seven features of MRMR-IG using RF.