Biopotential Gesture Control for Intuitive Responsive Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing user interfaces often require hardware that is not intuitive and may not be accessible to a wide range of users, lacking the ability to accurately interpret nuanced hand gestures and muscle contractions for responsive device control.
Innovation Solution
A system that processes first signals representing physical feature manipulations and second signals representing tissue electrical activity, such as nerve signals, to identify and send control signals to applications, utilizing classifiers trained on user interactions for precise gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional hardware-based user interfaces are used, then device control functionality is provided, but the interface is not intuitive and accessibility is limited
Solution Approach 1:
The patent replaces mechanical hardware interfaces (keyboards, mice, buttons) with a physiological signal-based system that detects muscle contractions and nerve electrical activity. This substitution eliminates the need for physical manipulation of hardware controls, making the interface more intuitive and accessible to users with diverse physical abilities.
Solution Approach 2:
The system utilizes the user's own physiological signals (muscle contractions, nerve activity) as the input mechanism. The body's natural electrical and mechanical responses serve as the control interface, eliminating the need for external hardware mediators and enhancing both intuitiveness and accessibility.
2Measurement precision
If physiological signal processing is implemented, then gesture recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary classification and processing of physiological signals before they reach the main control logic. By pre-processing muscle contraction signals and nerve electrical activity data, the system reduces the complexity of subsequent gesture recognition while maintaining high accuracy.
Solution Approach 2:
The patent introduces intermediary processing layers including signal conditioning circuits, feature extraction algorithms, and classification modules that mediate between raw physiological signals and control commands. These intermediaries simplify the overall system architecture while enhancing measurement precision.
3Measurement precision
If multiple signal types are processed simultaneously, then control precision is enhanced, but processing time increases
Solution Approach 1:
The system employs periodic sampling and time-windowed analysis of physiological signals, processing data in discrete intervals rather than continuously. This periodic approach maintains high control precision through multiple signal types while reducing overall processing time through efficient time management.
Solution Approach 2:
The system selectively processes only the most relevant physiological signals for each specific gesture or control command, rather than continuously analyzing all possible signal types. This partial processing approach reduces time loss while maintaining sufficient precision for accurate control.
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
Enables more intuitive and accessible control of responsive devices by accurately interpreting muscle contractions and gestures, reducing hardware requirements and enhancing user interface flexibility.
Implementation Method 1
a sensor to detect tissue electrical activity, such as nerve signals, from a user
Data Source
AI summary
System and methods for gesture-based control are described. In some embodiments, a system may include a wearable device having a biopotential sensor and a wrist motion sensor. The biopotential sensor may be configured to output a first data stream indicating actions of a person's hand. The system may further include a second device configured to output a second data stream, which may also indicate the actions of the person's hand. The system may be configured to analyze the first and second data streams to train a machine learning interpreter to classify actions of a person's hand based on at least biopotential data.