Client-Side Video Vital Sign Estimation for Live Telemedicine
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Solution Overview
Problem
Existing telemedicine systems lack the ability to accurately assess an individual's vital signs in real-time during a consultation session, often requiring multiple sensors that are cumbersome and not always synchronized with the consultation, leading to inaccurate measurements and diagnoses.
Innovation Solution
A system and method that utilizes a client-side neural network trained on audio, image, or video data to estimate physiological conditions, allowing for real-time physiological condition estimations during a live video session, using a camera to obtain a live video stream and provide the data as input to the neural network for immediate presentation to a clinician.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to measure vital signs, then measurement coverage is improved, but device complexity and ease of operation deteriorate due to individual powering and maintenance requirements
Solution Approach 1:
The patent combines multiple sensing functions into a single camera device. The camera captures video data that is then processed to extract multiple physiological parameters (heart rate, respiratory rate, oxygen saturation, temperature) simultaneously, eliminating the need for multiple separate sensors and their associated power and maintenance requirements.
Solution Approach 2:
The camera system is designed to perform multiple measurement functions through a single device. By using machine learning models to analyze video data, the system can detect various vital signs and physiological conditions using one universal tool, improving ease of operation while maintaining measurement coverage.
2Measurement precision
If multiple sensors are deployed for comprehensive vital sign monitoring, then measurement coverage is improved, but ease of operation worsens due to individual powering and maintenance requirements
Solution Approach 1:
The system merges multiple sensing operations into a single camera-based platform. The camera captures comprehensive video data that is processed through machine learning algorithms to extract multiple physiological parameters, eliminating the need for users to manage multiple separate sensors, their power sources, and maintenance schedules.
Solution Approach 2:
The machine learning models automatically process the video data to extract physiological information without requiring manual sensor placement or calibration. The system performs self-calibration and automatic detection of vital signs, reducing the operational burden on users while maintaining accurate measurements.
3Device complexity
If traditional telemedicine systems are used without real-time physiological analysis, then system simplicity is maintained, but diagnostic accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of video data in real-time during the telemedicine consultation. Machine learning models continuously process the video stream to detect physiological parameters and emotional stress indicators, providing diagnostic support alongside the video consultation without requiring separate measurement sessions or complex pre-setup procedures.
Data Source
AI summary
The present disclosure pertains to a system for providing client-side physiological condition estimations during a live video session. In some embodiments, the system includes a first client computer system that is caused to: (i) store a neural network on one or more computer-readable storage media of the first client computer system, (ii) obtain a live video stream of an individual via a camera of the first client computer system during a video streaming session between the first client computer system and a second client computer system, (iii) provide, during the video streaming session, video data of the live video stream as input to the neural network to obtain physiological condition information from the neural network, and (iv) provide, during the video streaming session, the physiological condition information for presentation at the second client computer system.