FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4318464273> ?p ?o ?g. }

• W4318464273 endingPage "1516" @default.
• W4318464273 startingPage "1516" @default.
• W4318464273 abstract "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 activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML’s consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user’s chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication." @default.
• W4318464273 created "2023-01-30" @default.
• W4318464273 creator A5022043107 @default.
• W4318464273 creator A5041117762 @default.
• W4318464273 creator A5042241183 @default.
• W4318464273 date "2023-01-30" @default.
• W4318464273 modified "2023-09-26" @default.
• W4318464273 title "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" @default.
• W4318464273 cites W1671008017 @default.
• W4318464273 cites W2029943497 @default.
• W4318464273 cites W2051029594 @default.
• W4318464273 cites W2148217011 @default.
• W4318464273 cites W2170724010 @default.
• W4318464273 cites W2513359455 @default.
• W4318464273 cites W2807792648 @default.
• W4318464273 cites W2808686737 @default.
• W4318464273 cites W2884729415 @default.
• W4318464273 cites W2898646434 @default.
• W4318464273 cites W2911964244 @default.
• W4318464273 cites W3022370443 @default.
• W4318464273 cites W3093982442 @default.
• W4318464273 cites W3104369241 @default.
• W4318464273 cites W3118184536 @default.
• W4318464273 cites W3122136615 @default.
• W4318464273 cites W3138731435 @default.
• W4318464273 cites W3143534982 @default.
• W4318464273 cites W3178681001 @default.
• W4318464273 cites W3193906736 @default.
• W4318464273 cites W3214868319 @default.
• W4318464273 cites W4200451791 @default.
• W4318464273 cites W4221003122 @default.
• W4318464273 cites W4234046016 @default.
• W4318464273 cites W4281732381 @default.
• W4318464273 doi "https://doi.org/10.3390/s23031516" @default.
• W4318464273 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36772556" @default.
• W4318464273 hasPublicationYear "2023" @default.
• W4318464273 type Work @default.
• W4318464273 citedByCount "0" @default.
• W4318464273 crossrefType "journal-article" @default.
• W4318464273 hasAuthorship W4318464273A5022043107 @default.
• W4318464273 hasAuthorship W4318464273A5041117762 @default.
• W4318464273 hasAuthorship W4318464273A5042241183 @default.
• W4318464273 hasBestOaLocation W43184642731 @default.
• W4318464273 hasConcept C111919701 @default.
• W4318464273 hasConcept C119857082 @default.
• W4318464273 hasConcept C132868160 @default.
• W4318464273 hasConcept C149635348 @default.
• W4318464273 hasConcept C150594956 @default.
• W4318464273 hasConcept C154945302 @default.
• W4318464273 hasConcept C160403385 @default.
• W4318464273 hasConcept C169258074 @default.
• W4318464273 hasConcept C26324664 @default.
• W4318464273 hasConcept C31258907 @default.
• W4318464273 hasConcept C41008148 @default.
• W4318464273 hasConcept C44154836 @default.
• W4318464273 hasConcept C79403827 @default.
• W4318464273 hasConcept C79974875 @default.
• W4318464273 hasConcept C81860439 @default.
• W4318464273 hasConcept C89805583 @default.
• W4318464273 hasConceptScore W4318464273C111919701 @default.
• W4318464273 hasConceptScore W4318464273C119857082 @default.
• W4318464273 hasConceptScore W4318464273C132868160 @default.
• W4318464273 hasConceptScore W4318464273C149635348 @default.
• W4318464273 hasConceptScore W4318464273C150594956 @default.
• W4318464273 hasConceptScore W4318464273C154945302 @default.
• W4318464273 hasConceptScore W4318464273C160403385 @default.
• W4318464273 hasConceptScore W4318464273C169258074 @default.
• W4318464273 hasConceptScore W4318464273C26324664 @default.
• W4318464273 hasConceptScore W4318464273C31258907 @default.
• W4318464273 hasConceptScore W4318464273C41008148 @default.
• W4318464273 hasConceptScore W4318464273C44154836 @default.
• W4318464273 hasConceptScore W4318464273C79403827 @default.
• W4318464273 hasConceptScore W4318464273C79974875 @default.
• W4318464273 hasConceptScore W4318464273C81860439 @default.
• W4318464273 hasConceptScore W4318464273C89805583 @default.
• W4318464273 hasIssue "3" @default.
• W4318464273 hasLocation W43184642731 @default.
• W4318464273 hasLocation W43184642732 @default.
• W4318464273 hasLocation W43184642733 @default.
• W4318464273 hasOpenAccess W4318464273 @default.
• W4318464273 hasPrimaryLocation W43184642731 @default.
• W4318464273 hasRelatedWork W2911455822 @default.
• W4318464273 hasRelatedWork W3043796182 @default.
• W4318464273 hasRelatedWork W3174196512 @default.
• W4318464273 hasRelatedWork W3211546796 @default.
• W4318464273 hasRelatedWork W4205275965 @default.
• W4318464273 hasRelatedWork W4281616679 @default.
• W4318464273 hasRelatedWork W4293525103 @default.
• W4318464273 hasRelatedWork W4308191010 @default.
• W4318464273 hasRelatedWork W4318350883 @default.
• W4318464273 hasRelatedWork W4323021782 @default.
• W4318464273 hasVolume "23" @default.
• W4318464273 isParatext "false" @default.
• W4318464273 isRetracted "false" @default.
• W4318464273 workType "article" @default.