Predictmedix AI Inc. announced significant technological advances within its Safe Entry Station (SES) technology, featuring innovations that solidify the company's commitment to pioneering advancements in the field. These breakthroughs set the stage for highly anticipated commercial deals, further establishing Predictmedix AI's position as a leader in the industry. The enhanced capabilities of SES include: Emotional State Identification: SES can now discern the emotional state of individuals, introducing a new dimension to health assessments by accurately identifying happiness, neutrality, surprise, joy, fear, and frustration.

PRQ Assessment: PRQ is now seamlessly integrated into the scanning process, providing a comprehensive health evaluation beyond previously announced parameters. The pulse-respiration quotient (PRQ) reflects properties of the complex interplay between cardiac and respiratory activities. The pulse-respiration quotient metric measures to what extent this interplay is functioning normally.

A low or high score would indicate that heart rate and/or breathing rate are disproportionate, which may indicate that both the heart and the lungs are working inefficiently. Stress Monitoring: A stress monitoring code has been successfully deployed, enabling SES to identify stress or a stress-free state in individuals undergoing scanning, offering valuable insights into mental well-being. Face Verification Accuracy:igorously tested on more than 500 subjects, face verification technology achieves an impressive accuracy rate of over 99%, ensuring robust and reliable identity verification.

Publishing of peer reviewed publication on Facial PPG Signals for Blood Pressure Estimation. In a move towards personalized healthcare, Predictmedix AI introduces a novel machine learning approach utilizing Photo Plethysmography (PPG) signals for precise blood pressure estimation. Recognizing the global prevalence of hypertension, the company leverages wearable devices to obtain facial PPG signals, offering valuable physiological information related to cardiovascular activity.

Through advanced machine learning algorithms, including deep learning architectures and feature extraction methods, Predictmedix AI aims to establish a robust model for blood pressure estimation using facial image analysis. The methodology involves preprocessing PPG signals, extracting relevant features, and employing sophisticated machine learning models for regression analysis. Comprehensive experimentation with diverse datasets ensures the efficacy of this approach across various demographic groups and conditions.

Results demonstrate promising accuracy and reliability in estimating blood pressure values, suggesting the potential for practical implementation in healthcare settings. The proposed technique opens a promising avenue for non-invasive and accessible blood pressure monitoring, contributing significantly to personalized healthcare and continuous health monitoring systems.