Customized Gesture Mapping for Hands-Free Media Player Control
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Solution Overview
Problem
Existing gesture recognition technologies in media systems lack efficiency and user-friendly customization for improved user experience.
Innovation Solution
A computing system that maps customized gestures to media player actions by detecting them through a camera, allowing users to create and recognize personalized gestures, and trains to recognize gestures outside the viewing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture recognition technology is implemented in media systems, then user experience is improved through hands-free operation, but the system complexity and difficulty of detecting gestures increase
Solution Approach 1:
The patent introduces a camera as an intermediary device to capture gestures and a processing system to translate camera input into media player commands. This intermediary layer handles the complexity of gesture detection, keeping the media player interface simple while enabling hands-free control through captured video analysis.
Solution Approach 2:
The patent replaces traditional mechanical remote control operations with optical-based gesture recognition. Instead of requiring physical buttons or remote controls, the system uses camera-based visual detection to capture and interpret hand gestures, substituting mechanical interaction with optical field-based detection.
2Measurement precision
If gesture recognition is limited to the viewing environment, then detection accuracy is improved, but the versatility and adaptability of the system deteriorates
Solution Approach 1:
The patent makes the gesture recognition system universal by enabling it to detect and recognize gestures performed both within and outside the immediate viewing environment. The camera system captures gestures from extended ranges, and the processing system adapts to interpret gestures regardless of their spatial location, allowing the same system to function in multiple environmental contexts.
Data Source
AI summary
In one aspect, an example method includes (i) receiving, by a computing system and from an input device associated with the computing system, a command to map a customized gesture with a particular action of a plurality of actions that a media player is configured to perform; (ii) in response to receiving the command, monitoring, by the computing system and using a camera, a viewing environment of the media player to detect performance by a person of the customized gesture; and (iii) in response to detecting performance of the customized gesture: generating, by the computing system, a classification for use by the computing system for detecting the customized gesture, and storing, by the computing system, in memory, mapping data that correlates the detected customized gesture with the particular action.