Video-Based Motion Analysis for Sexual Stimulation Control
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Solution Overview
Problem
Existing control systems for sexual stimulation devices are limited in functionality, unable to automatically synchronize with any video of sexual activity, require manual pre-programming, and cannot customize the experience using biometric data, leading to a lack of user-specific and realistic stimulation.
Innovation Solution
A system and method that uses pixel-by-pixel color change analysis to estimate movement speed in videos, generating control signals for sexual stimulation devices, allowing for automated real-time video analysis, customization based on user preferences and biometric data, and synchronization with any video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing control systems use manual pre-programming for synchronization, then the device can be controlled with limited libraries of predefined routines, but the system lacks adaptability and cannot automatically recognize or customize for different users
Solution Approach 1:
The system automatically analyzes video content and generates control signals without manual programming. The machine learning model processes video frames, detects movement and rhythm patterns, and autonomously creates synchronized stimulation routines, eliminating the need for users to manually program control sequences.
Solution Approach 2:
The system dynamically adjusts stimulation parameters based on real-time video analysis. By detecting movement speed, rhythm, and patterns from video frames, the system automatically modifies device operation parameters to match the video content, enabling adaptation to different scenarios without manual intervention.
2Measurement precision
If existing systems use complicated algorithms for controlling stimulation speed, then the control can be precise, but the computational intensity becomes excessive
Solution Approach 1:
The patent replaces complex traditional algorithms with machine learning models trained on video data. The system uses pre-trained neural networks that can efficiently process video frames and extract movement patterns, reducing computational requirements compared to real-time complex algorithm processing while maintaining precision in speed control.
3Measurement precision
If the system analyzes every pixel color change in detail, then the movement speed estimation becomes accurate, but the processing time and computational load increase
Solution Approach 1:
The system pre-processes video frames by extracting key features and preparing data structures before final analysis. By pre-loading video frames into memory and pre-processing them into usable formats, the system reduces the time required for actual movement speed calculation, enabling accurate pixel-level analysis without excessive processing delays.
Data Source
AI summary
A system and method for controlling the speed of operation of sexual stimulation devices from videos of sexual activity using a less computationally intensive method of estimating movement speed from a video. In an embodiment, the system and method involve estimating movement in a video using pixel-by-pixel color change over time, calculating a rate of color change in the video, estimating a speed of movement in the video from the rate of color change, and applying an algorithm to convert the estimated speed of movement in the video to a speed of operation of a sexual stimulation device.