The Patients in Critical condition need intense monitoring and care. With ICU an Intensive Care unit we can provide this type of care to the patient. But nowadays shortage of Intensivist and Critical Care nurses is the major problem faced by the hospitals. To overcome such problems TeleICU (remotely handled ICU) centers are currently available. With the help of TeleICU control center, one can monitor the patients in Critical Care unit and can assist the person or doctor available at the physical location. TeleICU can provide round the clock monitoring. The person who is monitoring the patient from TeleICU control center should be proactive in monitoring. Another issue is one person can only able to monitor one patient at a time. So, this research aims to develop the system which overcomes the issues in current TeleICU system. For reducing the workload of the person in the control center and to automate some of the humans handled task we need the machine based interface which will take the decisions automatically and can able to collaborate with the existing system. The proposed system in this paper is developed for such TeleICU systems. The system presented in this research is able to identify the type of persons available in the ICU room with that it can automatically detect the several unusual activities done by the patient. As soon as any unusual activity is detected system will take real time decision and will notify the same to control center based on the type of activity and persons available in the ICU Room. To develop this system video processing and deep learning networks are used.
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