Wearable Emitting Module Activation for Predicted Surface Interaction
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Solution Overview
Problem
Wearable smart devices lack an efficient method to predict and prepare for user interactions with surfaces in their environment, leading to suboptimal utilization of ultrasonic and infrared emitting modules.
Innovation Solution
A system that uses machine learning to analyze user and environment data to predict interactions, select the appropriate emitting module, and prompt the user to activate it, thereby optimizing the use of ultrasonic and infrared emitting modules for tasks like decontamination and illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the wearable device continuously monitors and activates emitting modules based on real-time prediction, then the user experience is enhanced through context-aware activation, but the system load and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously analyzing environment and user data to predict future interactions before they occur. The machine learning model forecasts user-surface interactions in advance, allowing the device to prepare and activate emitting modules proactively rather than reactively, enhancing user experience while managing system load through intelligent prediction
Solution Approach 2:
The emitting modules are activated automatically based on predicted user interactions, eliminating the need for manual user activation. The system serves itself by autonomously determining when and which modules to activate based on real-time data analysis, reducing the operational burden on users while maintaining context-aware functionality
2Adaptability or versatility
If multiple emitting modules are available for different functions, then the device versatility is improved, but the device complexity and selection difficulty increase
Solution Approach 1:
The wearable device integrates multiple emitting modules (ultrasonic and infrared) with different functions into a single universal platform. The machine learning system provides a unified interface that automatically selects and activates the appropriate module based on the predicted interaction context, allowing one device to serve multiple purposes without requiring users to understand or manage the complexity of individual module selection
Solution Approach 2:
The system continuously monitors environment data and user data, processes this feedback through machine learning algorithms, and uses the results to automatically select and activate the most appropriate emitting module. This closed-loop feedback mechanism ensures the device adapts to changing conditions and selects the correct module without user intervention, maintaining versatility while simplifying operation
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
This approach reduces system load by enabling real-time analysis and efficient module selection, improving user experience through timely and appropriate activation of emitting modules for tasks such as decontamination and illumination.
Implementation Method 1
ultrasound are sound waves with frequencies higher than the upper audible limit of human hearing. In general, ultrasonic devices are used to detect objects and measure distances
Implementation Method 2
electromagnetic radiation, commonly referred to as infrared light, is used in thermal efficiency analysis, environmental monitoring, and remote temperature sensing
Data Source
AI summary
A system for activating an emitting module is provided. A computer device identifies (i) environment data relating to an environment, and (ii) user data relating to a user located within the environment, wherein the user is wearing a wearable computing device. The computing device predicts that the user will interact with a surface in the environment based, at least in part, on the environment data and the user data. The computing device selects at least one emitting module from a plurality of emitting modules on the wearable device based, at least in part, on a predicted proximity of the at least one emitting module to the surface. The computing device prompts the user to activate the at least one emitting module.