Integrating Deep Neural Networks and AI for Healthcare: Face Recognition and Vital Sign Monitoring

Enhancing AI Solutions: From Deep Neural Networks to Face Recognition in Healthcare

How Deep Neural Networks Improve Recommendations

Deep neural network (DNN) models offer significant advantages over traditional matrix factorization methods in recommendation systems. DNNs can seamlessly incorporate both query features and item features thanks to the flexibility of their input layers. This allows them to capture a user’s specific interests more accurately, leading to improved recommendation relevance.

DNN Face Detector in OpenCV

In computer vision, the OpenCV library provides a powerful face detection model based on deep learning. The DNN face detector utilizes a Caffe model built on the Single Shot-Multibox Detector (SSD) framework with a ResNet-10 backbone. Introduced in OpenCV 3.3, this model has become a reliable tool in various AI applications.

The image above shows our solution recognizing patients and people in religious events.

Real-World Application: AI-Driven Face Recognition and Health Monitoring

Our solution leverages this technology in a healthcare prototype designed to recognize patients and individuals during large religious gatherings. The system integrates a smart watch and a mobile app to continuously monitor vital health signs like blood pressure, pulse rate, body temperature, and oxygen levels. The app can track these metrics for up to seven users simultaneously via Bluetooth, sending the data to the cloud for real-time analysis.

AI-Enabled Wearable Technology

Each user’s smart bracelet is registered to their unique ID. A smart glass equipped with AI face recognition technology then identifies the patient or user, retrieves their medical data from the cloud, and displays it on the smart glass screen. This contactless solution enables healthcare providers and government personnel to monitor people’s health efficiently and safely.

Key AI Components of the Solution:

1. Face Detection: The model uses dnn.readNetFromCaffe to load the face detector, enabling quick and accurate identification.
2. Face Recognition: The model loads the face recognizer using dnn.readNetFromTorch, which reads a network model stored in TensorFlow format.

This comprehensive AI solution was submitted to a healthcare competition and was nominated among the top 11 solutions. The technology demonstrates how smart glasses and smart watches can be effectively combined to monitor public health in contactless environments.