In the current scenario, with a large amount of unstructured data, Health Informatics is gaining traction, allowing Healthcare Units to leverage and make meaningful insights for doctors and decision-makers with relevant information to scale operations and predict the future view of treatments via Information Systems Communication. Now, around the world, massive amounts of data are being collected and analyzed for better patient diagnosis and treatment, improving public health systems and assisting government agencies in designing and implementing public health policies, instilling confidence in future generations who want to use better public health systems. This article provides an overview of the HL7 FHIR Architecture, including the workflow state, linkages, and various informatics approaches used in healthcare units. The article discusses future trends and directions in Health Informatics for successful application to provide public health safety. With the advancement of technology, healthcare units face new issues that must be addressed with appropriate adoption policies and standards.

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In this project, a new approach of a Humanoid robot controlled mobile platform is presented. The robot we are using is Nao by Aldebaran Robotics. The main reason for choosing Nao is for its flexibility and versatility. Nao’s wide range of movements allows it to perform a steering mechanism in a real world. The Driving behavior for Nao is done by using NAOqi python SDK and through using Raspberry Pi controller interface. We use the HC-SR04 ultrasonic sensor for obstacle range detection in the navigation system to avoid the obstacle in real time. The system architecture and algorithms used in each stage are described in this project.

Facial emotion recognition (FER), because of its significant academic and business potential, is an important subject in the fields of computer vision and artificial intelligence. The purpose of this project is to develop an emotion detection pipeline using video frames. In particular, we detect and analyse the faces of the video through deep neural networks for the recognition of emotions. We use a CNN and an RNN based on documents submitted in the Wild Challenge for emotional recognition. An input video is divided into small segments. We will detect, crop and align faces for each segment. This gives an image sequence. A CNN will extract relevant features in the sequence for each image. These features will be sequentially feed to an RNN that encodes emotional movement and facial expressions. The entire process is carried out as a Python.

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