Ted-ICU AI Platform: (1)Provide a single view of patients＇ EMRsvital signs (2)Support remote ICU monitoring (3)Online labeling tool & Expandable AI algorithms repository (4)Disease-specific prediction models (5)Standardized EMR templates

The technology provides a new experience for remote respiratory rehabilitation care,let patients realize self-health management at home. The system has three characteristics: First, daily home pulmonary function tests to achieve early warning of disease. Second, the design of automatic feedback aperture valve provides the best positive expiratory pressure (PEP)for pulmonary rehabilitation,APP helps user to do breathing exercise. Third, electronic monitoring of inhaled medications with a two-way flow sensor module,APP helps user to guide the inhalation process.

We have developed a low-costportable reader for thermometric lateral flow immunoassay (TLFIA) using colloidal goldlatex beads as reportersa continuous-wave green laser as the heating source. Instead of using infrared cameras, a single-element infrared sensor is employed for temperature sensing without compromising the sensitivity of the reader. The thermometric assay provides a 10× higher sensitivity than color visualizationthe results are sufficiently quantitative to support decision making in point-of-care testing for infectious diseases (such as COVID-19).

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.

Our PEDD has been used to detect the HNE level in both tearwound fluids of 9 patients,the limitations of detection were about 0.76 μg/mL0.63 μg/mL respectively. Our device requires only 15 minutes, 3 μL of clinical sample for each test,offers superior sensitivity compared to other current HNE detection methods. The HNE concentrations in the patient samples have shown that the elevated HNE levels might suggest an abnormal healing of either acutechronic wounds, consistent with previous studies that showed excess protease levels in all non-healing wounds.

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screeninterpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks,improving overall efficiency. Combining portable X-RayAI algorithms, the system performs real-timeaccurate preliminary screeningdiagnoses of diseases, such as pneumothorax, pneumonia,tuberculosis. It can also locate the nasogastric tube, endotracheal tube,central venous catheter, while misplacement of that is sent to the physicians when detected.

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.