Capacitive Button Finger Recognition Using Charging Signal Dynamics
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Solution Overview
Problem
Traditional capacitive sensing buttons cannot accurately identify which finger of a user is touching the button, and existing solutions that do require additional hardware, such as electromagnets, increase costs and reduce user experience.
Innovation Solution
A human-computer interaction method and system that uses capacitive charging signals to acquire and process Mel-frequency cepstrum coefficient characteristics, which are then input into a trained hidden Markov model to accurately identify the finger type touching the button, enabling improved interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional capacitive sensing buttons are used, then the device structure remains simple, but the button interaction capability is insufficient and cannot identify which finger is touching
Solution Approach 1:
The patent applies parameter changes by analyzing temporal characteristics of capacitive charging signals. Different fingers produce distinct charging curves with unique parameters such as charging time, peak voltage, and decay rate. The system extracts these temporal parameters and uses them to identify which finger is touching the button, thereby enhancing interaction capability without adding hardware complexity.
2Measurement precision
If electromagnets are worn on fingers to detect finger type, then finger identification accuracy is improved, but use cost increases and user experience deteriorates
Solution Approach 1:
The patent implements self-service by utilizing the body's own capacitive properties for identification. The human body naturally exhibits capacitive characteristics that vary by finger type. The system passively detects these inherent electrical properties through the capacitive button interface, eliminating the need for external sensors or wearable devices on the fingers, thus maintaining simplicity and good user experience while achieving accurate finger identification.
3Adaptability or versatility
If capacitive charging signals are used for finger identification, then interaction capability is improved, but the signal varies with touching duration making identification difficult
Solution Approach 1:
The patent applies dynamics by modeling the capacitive charging signal as a dynamic process rather than a static value. The system captures the entire charging curve over time and uses dynamic parameters such as charging time constant, peak voltage timing, and decay characteristics. This dynamic approach allows the system to distinguish different fingers based on their unique temporal response patterns, making the identification reliable even when touching duration varies.
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 solution allows for accurate finger identification, enhancing user interaction by enabling different fingers to control distinct functions, thus improving the usability and miniaturization of electronic devices like intelligent watches.
Implementation Method 1
acquiring a capacitive charging signal generated by a user finger-touching a capacitive button
Data Source
AI summary
The present application discloses a human-computer interaction method and an interaction system based on capacitive buttons. The method comprises the following steps: acquiring a capacitive charging signal generated by a user finger-touching a capacitive button; processing the capacitive charging signal to extract Mel-frequency cepstrum coefficient characteristics; and inputting the Mel-frequency cepstrum coefficient characteristics into a trained hidden Markov model, identifying a finger type of the user touching the capacitive button, and further implementing human-computer interaction according to an identification result. The present application utilizes the capacitive charging signals generated by touching the capacitive buttons with different fingers to perform button interaction, thus solving the problem of difficult interaction of capacitive buttons, and the present application trains a hidden Markov chain model by taking the Mel-frequency cepstrum coefficient of the capacitive charging signals as the characteristic, thereby improving the identification accuracy.