Facial Image Analysis for Human Body Weight Prediction
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Solution Overview
Problem
In pediatric medicine, accurately predicting body weight is crucial for drug dosages and resuscitation procedures, but emergency situations often prevent weighing children on scales, and accurate weight scales are limited in many locations, especially in developing areas.
Innovation Solution
A method and system using image analysis to predict body weight by capturing facial images, applying age-based weight prediction models, and utilizing a trained neural network with facial feature measurements to estimate weight, integrated with a database for comparative analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight scales are used to measure body weight, then measurement accuracy is improved, but device availability and ease of operation deteriorate in emergency situations and resource-limited settings
Solution Approach 1:
The patent replaces the mechanical weighing scale system with an optical imaging system combined with neural network analysis. Instead of using mechanical or electronic scales to measure body weight, the system captures facial images and uses computer vision algorithms to predict weight, thereby eliminating the need for physical weighing equipment while maintaining measurement capability.
Solution Approach 2:
The patent creates a virtual model of body weight prediction based on facial image data. Rather than directly measuring physical weight, the system captures a visual copy (facial image) and uses machine learning models trained on correlated data to infer the weight parameter, enabling indirect measurement through digital representation.
2Device complexity
If traditional weight prediction methods are used, then device simplicity is improved, but measurement precision deteriorates in emergency and resource-limited settings
Solution Approach 1:
The patent transforms the input parameters for weight prediction from simple anthropometric measurements to detailed facial image features. By analyzing multiple facial landmarks, distances, and geometric parameters captured in images, the system extracts richer information than traditional methods, enabling more accurate predictions while keeping the overall approach accessible through mobile devices.
Solution Approach 2:
The patent implements pre-trained neural network models that have been trained beforehand on large datasets of facial images and corresponding body weights. This preliminary training allows the system to make accurate predictions without requiring complex real-time computations or additional training data collection during emergency situations, thereby maintaining both accuracy and operational simplicity.
Data Source
AI summary
Systems and methods for determining body weight predictions and human conditions are disclosed. A body weight may be predicted by capturing at least one image of a human, and determining, from the image, a body weight prediction of the human by processing the at least one image with a data processor. The body weight prediction may further be based on an age-based weight factor. A model such as a neural network model may be used to predict body weight.