Adaptive Neuromuscular Control Mapping for Non-Invasive Device Interfaces
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Solution Overview
Problem
Conventional brain-computer interfaces require invasive direct neural interaction with cortical neurons, limiting their adaptability and user preference in controlling devices, as they are inflexible and cannot be tailored to individual user interactions.
Innovation Solution
A system using neuromuscular sensors to record continuous time series signals, which are then used to train a statistical model to associate user-specific body movements with control signals, allowing for real-time adaptation and flexible control mapping without the need for cortical interfaces, utilizing EMG or other neuromuscular signals for adaptive feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional brain-computer interfaces use direct neural interaction with cortical neurons, then control capability is achieved, but adaptability to user preferences and flexibility are limited
Solution Approach 1:
The patent uses neuromuscular signals as an intermediary between the user's intent and device control, avoiding direct cortical neural interaction. The system captures muscle activity patterns that naturally occur during movement and uses these as control inputs, serving as a non-invasive mediator that preserves user preference adaptability while eliminating the complexity and invasiveness of cortical interfaces.
Solution Approach 2:
The patent replaces the biological/mechanical cortical neural interface with an electrical signal-based system that captures neuromuscular activity. By substituting direct neural recording with electrical sensing of muscle activity, the system achieves comparable control capability without the invasiveness and complexity of cortical implantation or direct neural interfacing.
2Ease of operation
If control systems require users to learn fixed device operations, then device programming is simplified, but user flexibility and customization are reduced
Solution Approach 1:
The patent implements dynamic control mapping that adapts to individual users' natural movement patterns and preferences. The system continuously learns and adjusts the relationship between neuromuscular signals and control outputs based on user behavior, allowing the control interface to evolve and customize itself rather than requiring users to adapt to fixed programming.
Solution Approach 2:
The system performs self-calibration and self-customization by automatically learning each user's unique neuromuscular signaling patterns and preferences. Rather than requiring manual configuration or fixed programming, the control system serves itself by adapting to the user naturally, eliminating the need for complex setup procedures while maintaining ease of operation.
3Ease of manufacture
If fixed control mappings are used in devices, then manufacturing and programming are easier, but adaptability to individual users is reduced
Solution Approach 1:
The patent performs preliminary learning and adaptation during an initial calibration phase where the system captures and analyzes the user's neuromuscular signals for various movements and intentions. This preliminary action establishes a personalized control mapping specific to each user before actual device operation begins, combining the simplicity of pre-programmed controls with the benefit of user-specific customization.
Solution Approach 2:
The system dynamically adjusts control mapping parameters based on individual user characteristics and preferences. By changing the parameters that define the relationship between neuromuscular inputs and device controls to match each user's unique physiology and preferences, the system achieves both ease of manufacture through standardized hardware and high adaptability through personalized parameter configuration.
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
This approach provides a highly configurable control interface that allows users to control devices according to their preferences, reducing user effort and enabling adaptation to changes in signaling behavior, with increased control fidelity and reduced invasiveness.
Implementation Method 1
The system uses EMG or other neuromuscular signals for adaptive feedback
Data Source
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AI summary
Methods and apparatus for adapting a control mapping associating sensor signals with control signals for controlling an operation of a device. The method comprises obtaining first state information for an operation of the device, providing the first state information as input to an intention model associated with an operation of the device and obtaining corresponding first intention model output, providing a plurality of neuromuscular signals recorded from a user and/or signals derived from the neuromuscular signals as inputs to a first control mapping and obtaining corresponding first control mapping output, and updating the first control mapping using the inputs provided to the first control mapping and the first intention model output to obtain a second control mapping.