With the rise in computing power, deep-learning based computer-aided diagnosis systems have gained interest in the research community. Our system process the images to assist doctors to determine whether the patients have nodules in lungs. Meanwhile, we utilized the Feature Pyramid Network to extend the receptive field on the convolutional kernel, which improved the performance on the nodule detection with various locations in CXR. The semi-supervised learning mechanism also achieves the way of soft-annotation to reduce human effort in medical image annotation.

BeDIS has the following distinguishing features:1.Is easy to configure, deploymaintain 2.Location accuracyresponse time not affected by crowds 3.Can function without Internet 4.Supports waypoint-based indoor navigation 5.Supports diverse indoor location-based APPsservices

The self-powered sensing system realizes self-powered wireless wearable pressure sensing modules, self-powered intelligent environment sensing robots,self-powered smart window, which can carry out self-powered sensing signals combined with the transmission of the Internet of Things. Functions such as medical monitoring aidsautomated environmental quality sensing. In the future, this system opens a window towards automatic monitoring of environmental analysispersonal smart healthcare.

The principle of the image-based AFib discriminating system is based on the consistency that irregular cardiac cycles can both be detected on the waveforms of electrocardiographyrPPG. The quantity of the blood varies from time to time is captured by a general camera. Signals from RGB channels are then synthesized through the core algorithm to eliminate noisegenerate stable rPPG signals. The processed signals are then classified as AFibnot by a sample-size model. According to the IRB evaluation in En Chu Kong Hospital, the AFib can be well detected with an accuracy of 97.1.

This project aims to incorporate the state-of-the-art InformationCommunication Technologies (ICT), the Internet of Things (IoT), Big Data techniques in linking every aspect of community-based integrated long-term care (LTC), improving the efficiency of care servicesadministrative process,to realize the idea of Aging in Place emphasized in the Long-term Care Policy 2.0 in Taiwan.

The technology is video-based blood pressure monitoring which will help people know the continuous BP status in real time. Also, the contactless measurement can make a daily health record. We have conducted clinical trials in hospitalsestablished a large database to optimize AI solution. The system joins the physicians＇ specialty to analyze the users＇ health status with AI,takes care of human life with technology.

We develops a precision smart healthcare platform to deliver a comprehensive medical care experience to the elderly by Artificial Intelligence of Things (AIoT) technology. The platform offers health education, community interaction, personal health information,rehabilitation logs. It can be used as a basis for clinical staff to conduct diagnosistreatment. The system development tools use PHP programming languageLaravel framework to develop all arithmetic logic,gradually complete the dynamic association databaseinteractive web page functions.