{"title":"An Efficient Ensemble Framework for Human Gait Recognition Using CNN-LSTM With Extra Tree Classifier and Smartphone Sensors in Real-World Environment","authors":"Nurul Amin Choudhury;Sakshi Singh;Badal Soni","doi":"10.1109/LSENS.2024.3435719","DOIUrl":null,"url":null,"abstract":"Gait recognition is a biometric technology that identifies individuals based on their unique way of walking. Most of the work on human gait recognition (HGR) systems has minimal user records and is performed in a closed simulated environment, which hampers the performance in a real-world scenario. This letter presents an efficient ensemble framework using a hybrid deep learning network (convolutional neural network-long short-term memory) with an extra tree classifier (ETC) for HGR in a real-world environment. The proposed model effectively extracts low-level spatial and temporal features from the sensor data for meaningful pattern generation and classifies them using multiple decision trees present in the ensemble ETC. A State-of-the-Art HGR dataset has also been developed for a diverse set of users in uncontrolled environments in real-world environments using built-in smartphone sensors. The proposed model achieved an average performance accuracy of 99.10% and optimal precision, recall, and F1-score, outperforming all the benchmark models with optimal performance margins in lower computational times.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10614825/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Gait recognition is a biometric technology that identifies individuals based on their unique way of walking. Most of the work on human gait recognition (HGR) systems has minimal user records and is performed in a closed simulated environment, which hampers the performance in a real-world scenario. This letter presents an efficient ensemble framework using a hybrid deep learning network (convolutional neural network-long short-term memory) with an extra tree classifier (ETC) for HGR in a real-world environment. The proposed model effectively extracts low-level spatial and temporal features from the sensor data for meaningful pattern generation and classifies them using multiple decision trees present in the ensemble ETC. A State-of-the-Art HGR dataset has also been developed for a diverse set of users in uncontrolled environments in real-world environments using built-in smartphone sensors. The proposed model achieved an average performance accuracy of 99.10% and optimal precision, recall, and F1-score, outperforming all the benchmark models with optimal performance margins in lower computational times.