IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems
作者机构：Faculty of Information Science and TechnologyUniversity Kebangsaan MalaysiaUKM43600SelangorMalaysia Skyline University CollegeSharjahUnited Arab Emirates
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第71卷第5期
页 面：2579-2597页
核心收录：
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Data fusion(DF) posture recognition healthcare systems(HCS) wearable sensor(WS) medical data errorless data fusion(EDF)
摘 要：Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring *** systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic *** collection of WS data and integration of that data for diagnostic purposes is a difficult *** paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition *** research is based on a case study in a health *** the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized *** a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic *** breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect *** paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance *** of WS data are examined extensively using active and iterative learning to identify errors in specific *** technology improves position detection accuracy,analysis duration,and error rate,regardless of user *** devices play a critical role in the management and treatment of *** can ensure that patients are provided with a unique treatment for their medical *** paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature *** first,the patients’walking patterns are tracked at various time *** characteristics are then evaluated in relation to the stored data using a random forest classifier.
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