Augmented Reality Appetite Enhancement System
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Solution Overview
Problem
Individuals, particularly those with dementia, often experience a loss of appetite for nutritious foods like green leafy vegetables, leading to inadequate vitamin and mineral intake, which can be detrimental to their health.
Innovation Solution
An augmented reality system that uses machine learning to enhance appetite by collecting data from users, their environment, and consumable items, applying models to determine appropriate sensory enhancements such as visual, auditory, and olfactory stimuli to increase appetite through AR devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If augmented reality devices are used to enhance appetite through sensory stimuli, then the user's appetite and nutritional intake are improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex appetite enhancement task into distinct functional modules: data collection from multiple sensors, feature extraction from collected data, model-based determination of appropriate enhancements, and delivery of specific sensory stimuli. This modular segmentation manages system complexity while maintaining effectiveness.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes feature extraction algorithms and machine learning models. This intermediary layer sits between raw sensor data and the final appetite enhancement output, transforming complex multi-source data into actionable enhancement decisions without requiring direct complex interactions between all system components.
2Reliability
If multiple types of sensory enhancements are applied through AR devices, then the appetite stimulation effectiveness is improved, but the loss of information and data management challenges increase
Solution Approach 1:
The system extracts only the essential features from the collected user data, environmental data, and food item data. Rather than processing all raw data, the feature extraction component identifies and isolates relevant characteristics (such as user preferences, environmental conditions, and food properties) that are necessary for determining appropriate appetite enhancements, thereby managing data complexity.
Solution Approach 2:
The system performs preliminary data processing and feature extraction before the actual appetite enhancement is applied. By pre-processing the data and identifying relevant features in advance, the system reduces the complexity of real-time data management during the enhancement process and ensures that only necessary information is retained and processed further.
Data Source
AI summary
The exemplary embodiments disclose a method, a computer program product, and a computer system for enhancing a user's appetite with one or more augmented reality devices. The exemplary embodiments may include collecting data of the user, the user's environment, and one or more consumable items, extracting one or more features from the collected data, determining one or more augmented reality appetite enhancements based on the extracted one or more features and one or more models, and enhancing the user's appetite by way of the one or more augmented reality devices according to the determined one or more augmented reality appetite enhancements.