1.Real-time monitoring of air quality in indoor and outdoor environments: 
Sensors based on laser light scattering and electrochemistry are used for monitoring PM2.5 and other air pollutants in indoor and outdoor environments. The collected data from the sensors are transferred to the data center at NTU PHRC by 4G LTE. The residents can obtain the real-time air quality information through the website.
2. Precision Prevention Application: an APP of CVD Risk Prediction:
We developed an application for predicting CVD risk based on a Bayesian predicting model with personalized factors. With the precision risk of CVD, the health care workers can provide personalized suggestion for patients.

1. Indoor monitors were designed through miniaturizing outdoor monitors with sensors having good performance. The monitoring data are automatically transferred into the data center at NTU PHRC every minute. The residents can log in our website and obtain the real-time air quality condition for the past 3 days.
2. Based on a Bayesian model with general factors (ex: lifestyle behavior) as well as personal factors (ex: history of MetS), we can predict and update the risk of CVD for each person step by step with the available information.

1. Our study collaborated with a manufacturing company to create a miniaturized monitor with smaller volume and weight for indoor environment. Also, sensors related to other air pollutants indoors are added. With the IoT technology, we achieve collecting long-term and real-time environmental monitoring data, which is helpful for the indoor environmental management of long-term care institutions.
2. With the integration of the precision predicting risk into the APP, the health workers can provide a better suggestion for preventing CVD for patient according to their personal information.

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