Body Composition Analysis Circuitry Using Depth Sensors
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Solution Overview
Problem
Users often need to rely on multiple electronic devices to obtain comprehensive health-related information, making it inconvenient and cumbersome.
Innovation Solution
An electronic device equipped with body composition analysis circuitry that uses depth sensors and image analysis to estimate body composition based on captured images of the face, neck, and body, employing user-study-trained models to map image data to body composition information, including fat distribution and relative amounts of visceral and subcutaneous fat.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional electronic devices are used to obtain health-related information, then specific health metrics can be measured, but multiple devices are required which makes the process inconvenient and cumbersome
Solution Approach 1:
The patent combines multiple health measurement functions into a single electronic device. The device integrates a camera for capturing images, processing circuitry for analyzing those images, and algorithms for determining body composition metrics, thereby consolidating what previously required multiple separate devices into one unified system that can provide comprehensive health information
Solution Approach 2:
The electronic device is designed with multi-functionality to perform various health-related measurements including body fat percentage, muscle mass, bone density, and visceral fat assessment all through a single device using image capture and analysis, making it a universal health monitoring tool that replaces multiple specialized devices
2Loss of information
If body composition analysis is performed using multiple devices, then comprehensive health information can be obtained, but the process becomes cumbersome and user experience deteriorates
Solution Approach 1:
The patent merges multiple health assessment functions into one device that captures comprehensive body composition data through image analysis, providing complete health information (body fat, muscle mass, bone density, visceral fat) without requiring users to switch between multiple devices, thus maintaining information completeness while improving ease of operation
3Measurement precision
If simple image capture is used for body composition analysis, then the device remains simple, but measurement precision and accuracy of body composition estimates deteriorate
Solution Approach 1:
The patent introduces processing circuitry and analytical algorithms as intermediaries between the simple image capture and the body composition measurement. These intermediaries process the captured images through trained models and computational algorithms to extract accurate body composition metrics, thereby maintaining measurement precision while keeping the overall device architecture relatively simple and integrated
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables convenient and comprehensive body composition analysis in a single device, providing accurate health-related information such as body mass index, body fat percentage, and fat distribution without the need for multiple devices.
Implementation Method 1
a depth sensor in the electronic device may include an infrared light emitter that illuminates a face and neck with structured infrared light and an infrared light detector that detects infrared light reflected from the face and neck
Implementation Method 2
an infrared light detector that detects infrared light reflected from the face and neck
Data Source
AI summary
An electronic device may include body composition analysis circuitry that estimates body composition based on captured images of a face, neck, and/or body (e.g., depth map images captured by a depth sensor, visible light and infrared images captured by image sensors, and/or other suitable images). The body composition analysis circuitry may analyze the image data and may extract portions of the image data that strongly correlate with body composition, such as portions of the cheeks, neck, waist, etc. The body composition analysis circuitry may encode the image data into a latent space. The latent space may be based on a deep learning model that accounts for facial expression and neck pose in face/neck images and that accounts for breathing and body pose in body images. The body composition analysis circuitry may output an estimated body composition based on the image data and based on user demographic information.