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10.3934/mbe.2021078 http://scihub22266oqcxt.onion/10.3934/mbe.2021078 33757196!ä!33757196 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Biosci+Eng 2021 ; 18 (2): 1513-1528 Nephropedia Template TP {{tp|p=33757196|t=2021. A deep learning-based medication behavior monitoring system |pdf=|usr=}}{{33757196}} gab.com Text A deep learning-based medication behavior monitoring system JO: Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=33757196 #FOAvip The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior. Twit Text FOAVip A deep learning-based medication behavior monitoring system ????Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=33757196 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33757196 #FOAvip English Wikipedia <ref>{{Cite pmid|33757196}}</ref> A deep learning-based medication behavior monitoring system #MMPMID33757196 Roh H; Shin S; Han J; Lim S Math Biosci Eng 2021[Jan]; 18 (2): 1513-1528 PMID33757196show ga The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior. |*COVID-19 Drug Treatment[MESH] |*Deep Learning[MESH] |*Medication Adherence/psychology/statistics & numerical data[MESH] |*SARS-CoV-2[MESH] |Biomedical Engineering[MESH] |Computer Systems[MESH] |Directly Observed Therapy[MESH] |Equipment Design[MESH] |Humans[MESH] |Internet of Things[MESH] |Monitoring, Physiologic/instrumentation/*methods/statistics & numerical data[MESH] |Neural Networks, Computer[MESH] |Pandemics[MESH] |Software[MESH]A deep learning-based medication behavior monitoring system (epmc).pdf DeepDyve Pubget Overpricing