Exercise Device Camera Health Detection Without Medical Equipment
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Solution Overview
Problem
Accurate prediction and detection of health conditions and diseases, such as stroke, cardiovascular health, and diabetes, are hindered by the need for expensive medical equipment and the inaccuracy of traditional techniques, which are often inaccessible and limited by small data-sets and design flaws.
Innovation Solution
A machine learning system integrated into everyday devices like exercise equipment and automobiles captures images or videos to detect health indicators, using cameras and processors to analyze vital signs and physiological processes, enhancing detection through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical equipment is used for health detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces traditional mechanical medical equipment (scanners, blood pressure monitors) with an optical system using cameras and image processing algorithms. The camera captures images that are analyzed through machine learning models to detect health indicators, substituting mechanical measurement devices with optical sensing and computational analysis.
Solution Approach 2:
The patent creates a digital copy of the user's physical state through captured images. Instead of requiring physical contact with medical devices, the system captures visual representations (images/videos) of the user and extracts health information from these digital copies through image analysis and pattern recognition.
2Measurement precision
If traditional medical equipment is used for health detection, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent integrates health detection functionality into common everyday devices such as smartphones, tablets, or dedicated kiosks with cameras. These devices serve multiple purposes (communication, entertainment, health monitoring) rather than being specialized medical equipment, making them more accessible and easier to operate for the general population.
Solution Approach 2:
The system enables users to perform self-health assessments by simply positioning themselves in front of the camera. The automated image analysis and machine learning algorithms perform the detection without requiring users to operate complex medical equipment or interpret technical measurements, making health screening accessible to non-experts.
3Reliability
If traditional techniques are used for health prediction, then reliability is improved, but loss of information deteriorates
Solution Approach 1:
The patent transitions from traditional one-dimensional or limited biometric measurements to multi-dimensional image data. Cameras capture rich visual information including facial features, skin color variations, body posture, and other visual cues that provide numerous data dimensions simultaneously, enabling more comprehensive health assessment without requiring separate measurements for each parameter.
Data Source
AI summary
A method can include capturing an image, images, or a video of a user with the image, images or video capturing system of an exercise device and detecting a health or disease indicator from the images or video. An apparatus includes a camera, a processor, and computer readable medium containing programming instructions that, when executed, will cause the processor to use a machine learning model and one or more images or videos of a subject captured by the camera to detect a health or disease indicator.