By collecting the changes of kinds of vital signs(including heart beat / blood pressure / steps), we combined the results with the testers’ fatigue scores which belonged to the matched fatigue typestheir basic profiles. We extracted 780 types of structural features sets by cleaning raw data, then separating into training set, validation settesting set. With machine learning algorithm to auto-optimize regression analysis, finally we come out the correlative features between vital signsfatigue ,the overwork model as our deliverables.

The proposed indoor radar sensing system includes two enabling technology, activity recognitionrespiration rate estimation. In the former, the proposed framework consists of four major components: denosing, enhanced voxelization, data augmentation,dual-view machine-learning to lead to accurateefficient human-activity recognition. In the latter, the proposed system leverages the variation of the phase information of a specific frequency bin of the range profiles,proposes a dynamic adaptive respiration waveform filtering algorithm to improve accuracy.

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.

The high-frequency jet ventilator uses the solenoid valve and feedback techniques to control the operational parameters of the ventilator. The proposed dual-mode control mechanism allows dynamic adjustment of the tidal volume, pressure, and duration of ventilation simultaneously. The system has been verified in animal experiments and demonstrated a significant improvement in the gas exchange efficiency. Besides, the bioelectrical impedance measurement can provide real-time and accurate regional information of the lungs, which helps to monitor the patient’s condition during respiratory treatment. This work also adopts wireless communication and remote-control technology to reduce the risk of infection of medical professionals.

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

This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.

Current mainstream pharmacotherapy of glaucoma is the delivery of intraocular pressure-lowering medication via topical instillation. But, this dosage form has practical limitations due to well-known ocular barriers to drug transport. For the first time, highly efficient compound eye drop formulation is designed based on the idea of innovative glutathione-functionalized gelling materials. Our biomaterials-based ophthalmic drops can better improve drug bioavailabilityantiglaucoma efficacy than currently marketed eye drops, thereby showing future clinical translational potential.

The objective of this project is to develop a portablewireless urine detection systemplatform for prevention of cardiovascular disease. The main idea is to develop a system-on-chip, a microelectrodemicrochannel chip to detect biomarkers concentrations in urine. And then, it will be wirelessly transmitted to a smart application platform to evaluate user’s cardiovascular status.

An IOT sensor was presented to detect human motionvital signs with Wi-Fi signals. It mainly combines MIMO beamforming, injection-locking radarmachine learning techniques to recognizetrack physicalphysiological activities for health status monitoring. As a proof of concept for pandemic prevention, the presented sensor aims to inspect whether someone is exhibiting signssymptoms of COVID-19 infection within the Wi-Fi coverage area, such as fatigue, cough, shortness of breathirregular heart rhythm,even to detect sudden death.

We use features such as asymmetric expression and crooked eyes to assess the risk of facial stroke. Observing the micro vibration of the head caused by the contraction of the heart, and develop a zero-contact facial heart rate and respiration rate detection technology in conjunction with the camera. The technology can accurately measure heart rate and respiration rate in real time, thereby reducing the risk of infection. This technology has obtained two ROC patents (M590433, I689285), two US patents (HEART RATE DETECTION METHOD AND DEVICE THEREOF,MOUTH AND NOSE OCCLUDED DETECTING METHOD AND SYSTEM THEREOF). The possibility of detecting strokes through AI machine learning methods is not only accurate, but also find out signs of stroke early to grasp the best time to seek medical treatment.

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.

A highly sensitivelinear ultrasonic radar was realized using digital self-injection-locking (SIL) technique. It can remotely monitor the vital signs of a human in motion, which will be demonstrated in this expo by upgrading a low-cost ultrasonic radar for reverse parking to support high-value telemedical applications. In the future it will further combine phased-array technology to implement non-contact 4D ultrasound system, which will bring disruptive innovation to medical technologies.