Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ECG) monitoring. In theECG monitoring system, classification plays an important role because it canselect...Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ECG) monitoring. In theECG monitoring system, classification plays an important role because it canselect useful data (i.e., reduce the size of the dataset) and identify abnormaldata that can be used to detect the clinical diagnosis and guide furthertreatment. Since the classification requires computing capability, the ECGdata are usually delivered to the gateway or the server where the classificationis performed based on its computing resource. However, real-time ECG datatransmission continuously consumes battery and network resources, whichare expensive and limited. To mitigate this problem, this paper proposes atiny machine learning (TinyML)-based classification (i.e., TinyCES), wherethe ECG monitoring device performs the classification by itself based onthe machine-learning model, which can reduce the memory and the networkresource usages for the classification. To demonstrate the feasibility, afterwe configure the convolutional neural networks (CNN)-based model usingECG data from the Massachusetts Institute of Technology (MIT)-Beth IsraelHospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt(PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupportedArduino prototype. The performance results show that TinyCEScan have an approximately 97% detection ratio, which means that it has greatpotential to be a lightweight and resource-efficient ECG monitoring system.展开更多
We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extr...We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extracts features,and classifies the distress into a variety of types,while the road manager is driving.Every day for a year,we have tested PotholeEye+on real highway involving real settings,a camera,a mini computer,a GPS receiver,and so on.Consequently,PotholeEye+detected the pavement distress with accuracy of 92%,precision of 87%and recall 74%averagely during driving at an average speed of 110 km/h on a real highway.展开更多
基金supported by National Research Foundation (NRF)of Korea Grant funded by the Korean Government (MSIP) (No.2022R1F1A1063183).
文摘Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ECG) monitoring. In theECG monitoring system, classification plays an important role because it canselect useful data (i.e., reduce the size of the dataset) and identify abnormaldata that can be used to detect the clinical diagnosis and guide furthertreatment. Since the classification requires computing capability, the ECGdata are usually delivered to the gateway or the server where the classificationis performed based on its computing resource. However, real-time ECG datatransmission continuously consumes battery and network resources, whichare expensive and limited. To mitigate this problem, this paper proposes atiny machine learning (TinyML)-based classification (i.e., TinyCES), wherethe ECG monitoring device performs the classification by itself based onthe machine-learning model, which can reduce the memory and the networkresource usages for the classification. To demonstrate the feasibility, afterwe configure the convolutional neural networks (CNN)-based model usingECG data from the Massachusetts Institute of Technology (MIT)-Beth IsraelHospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt(PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupportedArduino prototype. The performance results show that TinyCEScan have an approximately 97% detection ratio, which means that it has greatpotential to be a lightweight and resource-efficient ECG monitoring system.
文摘We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extracts features,and classifies the distress into a variety of types,while the road manager is driving.Every day for a year,we have tested PotholeEye+on real highway involving real settings,a camera,a mini computer,a GPS receiver,and so on.Consequently,PotholeEye+detected the pavement distress with accuracy of 92%,precision of 87%and recall 74%averagely during driving at an average speed of 110 km/h on a real highway.
