Different from the traditional smart home iot system which aims to capture user’s health and daily life data. Our project import machine learning model and focus on the urban citizens who works with computer for a long time everyday. By helping users be aware of how long they can work efficiently, they can manage the best schedule even though confronted with various tasks and their health is well protected.
The platform we used is Intel Edison, we also used light, sound, temperature sensors to collect the environment information and used webcam to capture and the work state of users. A web app is designed for data and result visualization, and help users to arrange the working time.
For users monitoring real-time work environment condition, the web app displays current sensors data and user’s fatigue level.
The platform we used is Intel Edison, we also used light, sound, temperature sensors to collect the environment information and used webcam to capture and the work state of users. A web app is designed for data and result visualization, and help users to arrange the working time.
For users monitoring real-time work environment condition, the web app displays current sensors data and user’s fatigue level.
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The web app back-end download data from the dynamoDB and takes advantage of existing ML model , which is built from history data, to predict users remaining working time.
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