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Self-Care Assessment for Daily Living Using Machine learning Mechanism

Computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool Yazeed Yasin Ghadi Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

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.

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

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