SYSTEMS AND METHODS FOR ELECTRONIC GAME CONTROL AND GAME CONTROLLER CONFIGURATIONS WITH EMG SENSING AND USER IDENTIFICATION

Smart overview of the Invention

The patent describes systems and methods for electronic game control using electromyography (EMG) sensing and user identification. EMG sensors, such as those embedded in wrist straps, detect electrical activity from user muscle movements. This data is used to identify user profiles, which can then be employed to customize game content and control game elements. Machine learning models are utilized to enhance the accuracy of user identification by analyzing the EMG signals.

Field of Use

The technology is applicable to various gaming systems, including consoles, handheld devices, and mobile platforms. It enhances user interaction by leveraging EMG signals for more immersive and personalized gaming experiences. The principles of EMG sensing can also be extended to virtual and augmented reality systems, making it relevant beyond traditional gaming applications.

User Identification

User identification is achieved by comparing real-time EMG signals with stored EMG data to recognize individual users. This process allows for the creation of personalized user profiles, which include user login information, game settings, and recorded EMG signatures. The system can prompt users to perform specific movements for identification, ensuring a more secure and customized gaming experience.

Game Control and Customization

The system allows for dynamic control of game elements based on user identification. Game features can be unlocked and personalized content can be presented according to the user's profile. This approach reduces latency in device operations by anticipating user actions through EMG signal detection, offering a more seamless interaction between the user and the game.

Controller Configurations

Game controllers are designed to integrate with EMG sensors, enhancing their functionality. These configurations support interactive entertainment by improving the responsiveness and accessibility of control devices. The system includes methods for training machine learning models to recognize EMG signals, which can be used to refine touch-sensitive inputs and optimize controller commands.