SYSTEM AND METHOD FOR CONTACTLESS PREDICTIONS OF VITAL SIGNS, HEALTH RISKS, CARDIOVASCULAR DISEASE RISK AND HYDRATION FROM RAW VIDEOS

Smart overview of the Invention

A novel system and method are introduced for predicting vital signs, health risks, blood biomarker values, and hydration status using raw video inputs. This contactless approach leverages advanced machine learning models to analyze video data of a human subject, thereby determining various health metrics without the need for invasive procedures or expensive equipment. The system is designed to operate on one or more processors and utilizes a trained machine learning model that has been developed using extensive training videos with known ground truth values.

Technical Approach

The core of the system involves a machine learning model that processes raw video data to predict health-related information. The model is trained on a dataset comprising numerous training videos where actual values for vital signs and health risks were known. Different configurations of the model include the use of convolutional neural networks, ensembles of models, and deep learning artificial neural networks. These configurations enhance the model's ability to accurately predict outcomes such as cardiovascular disease risk and hydration status.

System Components

The system architecture consists of several key components: an input module for receiving raw video data, a machine learning module for processing this data, and an output module for delivering predictions. The machine learning module can employ various models, including convolutional neural networks combined with deep learning networks like XGBoost. Additionally, the system may preprocess video data through compression before analysis, optimizing performance and resource usage.

Predictive Capabilities

This innovative system can predict a range of health metrics from video data. It can estimate vital signs such as pulse rate and respiration rate while also assessing the likelihood of developing certain conditions, like cardiovascular diseases. The predictions are quantified as percentage likelihoods over specified future timeframes, providing valuable foresight into potential health issues. This capability is particularly beneficial for early diagnosis and preventive healthcare strategies.

Advantages and Applications

The described method offers significant benefits by eliminating the need for direct contact between the subject and diagnostic equipment. This makes it suitable for remote monitoring scenarios where traditional methods are impractical or impossible. By utilizing readily available imaging devices like video cameras, this system provides an accessible and cost-effective solution for continuous health monitoring, potentially transforming how healthcare services are delivered.