Improved Transient Search Optimization with Machine Learning Based Behavior Recognition on Body Sensor Data

作     者：Baraa Wasfi Salim Bzar Khidir Hussan Zainab Salih Ageed Subhi R.M.Zeebaree 

作者机构：ITM DepartmentTechnical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Information System Engineering DepartmentErbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Computer Science DepartmentCollege of ScienceNawroz UniversityDuhokIraq Energy Eng.DepartmentTechnical College of EngineeringDuhok Polytechnic UniversityDuhokIraq 

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

年 卷 期：2023年第75卷第5期

页      面：4593-4609页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Behavior recognition transient search optimization machine learning healthcare sensors wearables 

摘      要：Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare *** though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an *** article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor *** presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and *** addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard ***,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and ***,the parameters related to the MNN model are optimally selected via the ITSO *** experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH *** comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.