Smart Glasses Food Intake Tracking via Image Analysis
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Solution Overview
Problem
Existing food intake tracking applications rely on self-reporting, which leads to underreporting of snacks and food volumes, and requires manual entry of food items, making the process inconvenient and inaccurate.
Innovation Solution
The use of smart glasses equipped with an image capture device and machine learning algorithms for object recognition and volume estimation, allowing for automatic tracking of food intake by identifying food types and estimating food volumes through hand-to-mouth motions and image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual self-reporting is used for food intake tracking, then users can control what is tracked, but accuracy deteriorates due to underreporting of snacks and food volumes
Solution Approach 1:
The system performs food intake tracking automatically without requiring user intervention. The wearable device detects hand-to-mouth motions and captures images to identify food items and estimate volumes, allowing the system to serve itself in collecting accurate data without user reporting
Solution Approach 2:
The patent replaces manual mechanical input (typing food items into an app) with automated optical and motion detection systems. Image capture devices and machine learning algorithms automatically identify food types and estimate quantities, substituting human effort with automated technological systems
2Measurement precision
If manual entry of food items is required, then users can specify exact food types, but convenience deteriorates making the process inconvenient
Solution Approach 1:
The system automatically identifies food types and estimates volumes by capturing images and detecting hand-to-mouth motions, eliminating the need for users to manually search databases or enter food information while maintaining accurate identification
Solution Approach 2:
The system prepares and processes food identification in advance by capturing images and analyzing motion patterns before the user needs to log the food intake, automatically preparing the data entry so users simply need to wear the device during eating
3Ease of operation
If automated image capture and analysis is used, then tracking convenience improves, but device complexity increases
Solution Approach 1:
The wearable device integrates multiple functions including motion sensing, image capture, machine learning-based food identification, and nutritional data processing into a single unified system, allowing one device to perform what would otherwise require multiple separate systems
4Device complexity
If manual tracking is used, then device complexity remains low, but productivity deteriorates due to time-consuming entry processes
Solution Approach 1:
The system automatically collects and processes food intake data without requiring user time for manual entry, capturing images and analyzing motions in real-time during eating, thereby dramatically improving productivity while keeping the user experience simple
Data Source
AI summary
Aspects of the present disclosure are directed to quantitatively tracking food intake using smart glasses and/or other wearable devices. In some implementations, the smart glasses can include an image capture device, such as a camera, that can seamlessly capture images of food being eaten by the user. A computing device in communication with the smart glasses (or the smart glasses themselves) can identify the type and volume of food being eaten by applying object recognition and volume estimation techniques to the images. Additionally or alternatively, the smart glasses and/or other wearable devices can track a user's eating patterns through the number of bites taken throughout the day by capturing and analyzing hand-to-mouth motions and chewing. The computing device can log the type of food, volume of food, and/or number of bites taken and compute statistics that can be displayed to the user on the smart glasses.