The speed and accuracy of data transfer in an artificial intelligent [AI] infused machine learned [ML], mobile, interactive, smartphone application (app) can vary significantly depending on network conditions, server response times, optimization, and device performance. Currently, selective search engines embedded in mobile, iOS, health apps merges data based on engineered low-level features and has an order of magnitude of up to 91.3 s per information text in a CPU implementation. Faster R-CNN enables end-to-end detector training on shared convolutional features and shows compelling accuracy and speed. Unlike traditional batch processing, where data is collected and processed periodically in predefined portions, real-time R-CNN integration operates on a near-instantaneous basis. This means that prevention, timeliness diagnosis and rehabilitation of cardiac emergency intelligent smartphone health data captured can be retrieved, transferred, and archived in milliseconds. Currently mobile heart health apps may only employ fixed computational models for various applications due to non-real time data transfer leading to misspecifications in prevention, and rehabilitation of cardiac emergency-related injuries. Non-inclusion of real time vital in cardiovascular signs [e.g., chest pressure, nausea, shortness of breath, vertigo, unilateral facial paralysis etc.] can cause premature death in high-risk cardiac patients. This proposal introduces a Region Proposal Network (RPN) that shares full-image real-time convolutional features within an infused, intelligent, AI-ML detection network in an interactive, continuously self-learning wrist-wearable, smartphone, mobile app for enabling cost-free region proposals [e.g., instantaneous body physiological responses, changes in symptoms, incentivized protocols for adherence to prescribed medication, diet, physical activity, exercise stress tests and follow-up care, etc.]. A fully merged RPN and Faster R-CNN deep convolutional unified network infused into a mobile iOS app dashboard will be employed to simultaneously train, aggregate and predict object bounds and objectness scores for implementing real-time prevention, timeliness diagnosis and rehabilitation of cardiac emergency protocols whose effective running time for proposals is estimated at 10.3 ms. Advances like R-CNN have reduced the running time of real times detection mobile networks, exposing RPN computation as a bottleneck. However, currently there are no prevention, timeliness diagnosis and rehabilitation of cardiac emergency, heart, health-related, iOS mobile apps in the literature, or on the on-line commercial market that has a real time, R-CNN network infused into a continuously self-learning wrist-wearable for enabling cost-free region proposals which may have multiple applications [e.g., instantaneous detection of quivering or irregular heartbeat, due to arrythmias due to coronary heart disease or cardiomyopathy, creating an interactive lifetime visualization of blood pressure trends, medication compliance, GPS maps of closet locations of defibrillators for public use, cloud storage, security, etc.,]. We introduce a real time RPN, intelligent AI-ML infused interactive mobile app that is trainable end-to-end to generate high-quality region proposals, for real time data retrieval, tracking, transference and archiving of prevention, timeliness diagnosis and rehabilitation of cardiac emergencies within a R-CNN. This context-aware augmented and virtual reality mobile application utilizes location data, object recognition software, and 3D features in a R-CNN/RPN unified real time network to provide state of the art data retrieval, tracking, transference and archivability. The app dashboard enables a unified, deep-learning-based detection system to run at real-time frame rates. The learned RPN improves region proposal quality and thus the overall object detection accuracy.