Retracted: An IoT Based Smart Healthcare Medical System using Deep Learning Algorithm

The Internet of Things (IoT) has developed over the past ten years, integrating technological advances like machine learning, deep learning, distribution networks of the supply chain, edge computing, cybersecurity, and datasets. This evolution has expanded industrial acceptance, primarily with in healthcare sector. Incorporating machine learning approach, the IoT assists in providing analysis among real-time data and historical data. In this work, a deep-learning-based method is used to design and develop an IoT-based noninvasive automated patient distress monitoring and surveillance mechanism. The system relies on an IP video camera to track the participant’s strength and mobility without the need for any smart wearables. The retrieval of various important locations on the body of the patient is done using the Mask-RCNN approach. Deploying these rules of association from data mining, these six primary body parts are then created from the important aspects that were discovered. Additionally, data about the identified crucial point dimensions is collected to analyses the patient’s pain. The spatial and temporal thresholds are then used to categorize gestures as being related to comfortable or uncomfortable situations. These crucial details are also utilized to ascertain how the patient is positioned while resting on the mattress. Continuous monitoring of the person’s postural and bodily position allows for the differentiation of comfort from discomfort. Separate video clips spanning the beds of 2 patients are taken for exploratory examination.