Modular Haptic Tower for Variable Air Flow and Temperature
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Solution Overview
Problem
Current virtual reality systems lack a home-use, stand-alone device capable of delivering target-specific haptics with next-generation realism, integrating variable air intensity and temperature, and do not employ learning-based approaches to enhance user input processing for real-time haptic feedback.
Innovation Solution
A modular haptic system with a haptic tower that uses a fan assembly, temperature elements, and sensors to deliver variable air flow and temperature, integrated with a processor and memory for real-time data processing, and incorporates machine learning to dynamically learn and predict haptic commands for enhanced immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a modular haptic system with fan assembly and temperature elements is used to deliver variable air flow and temperature, then haptic realism and immersion are improved, but device complexity increases
Solution Approach 1:
The haptic system is divided into modular components including fan assembly, temperature elements, and control systems that can be independently configured and integrated. This segmentation allows for standardized interfaces and reduces overall system complexity while maintaining haptic realism through coordinated operation of discrete modules.
Solution Approach 2:
The system employs multi-functional components that can serve multiple haptic delivery purposes. The fan assembly and temperature elements can be configured for different haptic effects across various virtual reality scenarios, reducing the need for specialized components for each function and thereby simplifying the overall device architecture.
2Loss of time
If machine learning is implemented to dynamically learn and predict haptic commands, then latency is reduced and real-time processing is improved, but computational requirements and device complexity increase
Solution Approach 1:
The machine learning model is trained offline on extensive datasets to learn patterns and relationships between virtual reality events and appropriate haptic responses. This preliminary training allows the system to make rapid predictions during real-time operation without performing complex computations at runtime, thereby reducing latency while managing computational complexity through pre-processing.
Solution Approach 2:
Traditional rule-based haptic control systems are replaced with machine learning-based predictive models. This substitution enables the system to automatically adapt to varying conditions and predict user expectations, reducing latency by eliminating manual rule configuration and enabling more natural, responsive haptic feedback.
3Speed
If audio and video input processing is performed in real-time for haptic command generation, then haptic responsiveness is improved, but computational load increases
Solution Approach 1:
The system extracts only the critical features and events from audio and video input streams that are most relevant for triggering haptic responses. By filtering and selecting only essential information rather than processing complete high-definition streams, the system maintains haptic responsiveness while significantly reducing computational load and energy consumption.
Solution Approach 2:
The system processes audio and video inputs at selectively reduced resolutions or frame rates for haptic command generation, focusing computational resources only on the portions of the input stream that directly influence haptic output. This partial processing approach maintains adequate haptic responsiveness while avoiding the excessive computational burden of full-resolution real-time processing.
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
The system provides precise and immersive haptic experiences, reducing latency and computational load, allowing for real-time interaction and universal integration into various environments, enhancing the realism of virtual reality experiences.
Implementation Method 1
control an air flow intensity; based on the haptic output command, direct the air flow through at least one duct
Implementation Method 2
based on the haptic output command, control a temperature element to heat or cool the air flow
Implementation Method 3
based on the haptic output command, control a temperature element to heat or cool the air flow
Data Source
AI summary
The present embodiments disclose apparatus, systems and methods for allowing users to receive targeted delivery of haptic effects—air flow of variable intensity and temperature—from a single tower or surround tower configuration. The haptic tower may have an enclosed, modular assembly that manipulates air flow, fluid flow, scent, or any other haptic or sensation, for an immersed user. Moreover, the system has an application of sensor technology to capture data regarding a user's body positioning and orientation in the real environment. This data and, or data from a program coupled to the system, and, or audio-video data corresponding to a user in a virtual environment, is relayed to a haptic engine; recognized; scored along a plurality of parameters; and converted into a haptic output command for haptic output expression corresponding to the user in the virtual environment.