Massage Chair Control via Deep Neural Network Multimedia Synchronization
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Solution Overview
Problem
Existing massage chair control methods fail to effectively determine massage control information based on mood from image contents, rely on obscure algorithms, and lack a method for synchronizing content playback timing with massage chair control when using different routes for the chair and control apparatus.
Innovation Solution
A massage chair control method that extracts multimedia information from input images through content analysis, detects matching action items for the massage chair using a deep neural network, and synchronizes the timing of multimedia effects and massage chair actions via feedback signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If massage control information is determined only by mood corresponding to image contents, then the control system is simple, but the accuracy and comprehensiveness of massage control is insufficient
Solution Approach 1:
The content analysis is divided into multiple independent modules: video signal analysis module, audio signal analysis module, and subtitle analysis module. Each module extracts specific features (emotions, moods, situations) from different content types, and these modules work in parallel to comprehensively determine massage control information, thereby improving accuracy without significantly increasing overall system complexity.
Solution Approach 2:
The content analysis apparatus is designed to universally analyze multiple types of content (video, audio, subtitles) through a unified framework. The same deep neural network architecture processes different input types, extracting relevant features for massage control, thus maintaining system simplicity while enhancing control comprehensiveness.
2Ease of manufacture
If an obscure algorithm is used to generate massage control information, then the algorithm is easy to implement, but the transparency and reliability of control is poor
Solution Approach 1:
The system incorporates feedback mechanisms where the deep neural network continuously learns from the relationship between content features and appropriate massage responses. The network adjusts its parameters based on training data, improving the reliability of control decisions over time while maintaining the same implementation framework.
Solution Approach 2:
The patent replaces traditional rule-based algorithms with a deep neural network model. This substitution transitions from explicit mechanical rule-setting to a learned statistical model that automatically identifies patterns in content and determines appropriate massage control, improving reliability through data-driven decision-making.
3Adaptability or versatility
If massage chair and control apparatus are provided through different routes, then the system architecture is flexible, but the synchronization of control signals is difficult
Solution Approach 1:
The content analysis and massage control determination are performed in advance during content playback. The system pre-processes video, audio, and subtitle content to extract features and generate control signals before they are needed, reducing synchronization delays when content is delivered through different routes.
Solution Approach 2:
The patent introduces a standardized communication interface and protocol as an intermediary between the content analysis apparatus and the massage chair. This intermediary layer handles signal formatting, timing adjustment, and error correction, enabling reliable synchronization even when components are distributed through different routes.
Data Source
AI summary
A method and apparatus for controlling a massage chair have been disclosed. The method for controlling a massage chair comprises extracting multimedia information, detecting an action item, and controlling an action item. According to the present disclosure, the timing of the multimedia effect and the timing of the action item may be synchronized based on analysis using a deep neural network model through a 5G network.