Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

作     者：Liaqat, Sidrah Dashtipour, Kia Shah, Syed Aziz Rizwan, Ali Alotaibi, Abdullah Alhumaidi Althobaiti, Turke Arshad, Kamran Assaleh, Khaled Ramzan, Naeem 

作者机构：Univ West Scotland Sch Engn & Comp Paisley PA1 2BE Renfrew Scotland Edinburgh Napier Univ Sch Comp Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Coventry Univ Res Ctr Intelligent Healthcare Coventry CV1 5FB W Midlands England Qatar Mobil Innovat Ctr Qatar Sci & Technol Pk Doha Qatar Taif Univ Coll Ranyah Dept Sci & Technol At Taif 21944 Saudi Arabia Northern Border Univ Fac Sci Ar Ar 91431 Saudi Arabia Ajman Univ Coll Engn & IT Ajman U Arab Emirates 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE传感器杂志)

年 卷 期：2021年第21卷第16期

页      面：18214-18221页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

基　　金：Engineering and Physical Sciences Research Council (EPSRC) [EP/R511705/1] Ajman University Internal Research Grant Taif University, Taif, Saudi Arabia [TURSP-2020/277] 

主　　题：Sensors Legged locomotion Feature extraction Machine learning algorithms Machine learning Support vector machines Senior citizens Posture detection ensemble algorithm deep learning machine learning ubiquitous devices 

摘      要：Ambient assisted living is good way to look after ageing population that enables us to detect human s activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms.