Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning System Based on an IoT Wearable MEMS Accelerometer

Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal act...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 23; číslo 3; s. 1516
Hlavní autoři:	Hoang, Minh Long, Nkembi, Armel Asongu, Pham, Phuong Ly
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 30.01.2023 MDPI
Témata:	accelerometer Accelerometers Accelerometry - methods Accuracy activity recognition Algorithms Classification Coronaviruses Datasets Decision trees Deep learning Discriminant analysis Humans Internet of Things IoT Machine learning Micro-Electrical-Mechanical Systems Microelectromechanical systems Neural networks random forest classification Risk assessment Sensors Sleep Supervised Machine Learning Support vector machines wearable device Wearable Electronic Devices COVID-19 wearable device random forest classification accelerometer machine learning IoT activity recognition
ISSN:	1424-8220, 1424-8220
On-line přístup:	Získat plný text
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