Self-Care Assessment for Daily Living Using Machine Learning Mechanism

作     者：Mouazma Batool Yazeed Yasin Ghadi Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park 

作者机构：Department of Computer ScienceAir UniversityIslamabad44000Pakistan Department of Computer Science and Software EngineeringAl Ain UniversityAl Ain15551UAE Department of Computer ScienceCollege of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social ScienceAl Ain UniversityAl Ain15551UAE Department of Computer EngineeringKorea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：1747-1764页

核心收录：

学科分类：0711[理学-系统科学] 08[工学] 0804[工学-仪器科学与技术] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education Republic of Korea 

主　　题：Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection 

摘      要：Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.