EEG EMG Movement Pattern Measuring Apparatus
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Solution Overview
Problem
Existing human-computer interface technologies using EEG and EMG signals face challenges in accurately analyzing user movement patterns due to noise and artifacts, and the need for individual normalization of brainwave patterns, which increases costs and complexity.
Innovation Solution
A movement pattern measuring apparatus and method that utilizes EEG and EMG sensors to eliminate noise, normalize signals using independent component analysis and artificial neural networks, and apply weight values to estimate user movements, reducing the need for extensive normalization and sensor usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG and EMG sensors are used together to accurately measure movement patterns, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines EEG and EMG sensors into an integrated measurement system that simultaneously captures brainwave signals and muscle electrical signals. The normalization unit processes both signal types together, comparing them to achieve more accurate movement pattern recognition than either sensor could provide alone.
Solution Approach 2:
The patent introduces a normalization unit as an intermediary component that processes and standardizes both EEG and EMG signals before analysis. This unit compares the two signal types and adjusts them to a common reference framework, enabling accurate integration of data from multiple sensor types without requiring complex separate processing systems.
2Measurement precision
If individual normalization of brainwave patterns is performed to accurately match EEG signals with specific activities, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary normalization of EEG patterns by comparing them with reference patterns stored in a database before actual movement analysis. The normalization unit pre-processes brainwave signals by matching them against known activity patterns, establishing a baseline that accelerates subsequent real-time analysis and reduces processing delays during actual use.
Solution Approach 2:
The patent implements a feedback mechanism where the normalization process uses previously normalized patterns and comparison results to refine subsequent normalizations. The system learns from past normalization operations and adjusts its processing accordingly, reducing the time required for individual normalization while maintaining or improving accuracy over time.
3Measurement precision
If multiple sensors are used to capture comprehensive bio-signals for HCI, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a multi-functional processing system where the normalization unit handles both EEG and EMG signals through a single integrated process. Rather than requiring separate specialized processing circuits for each sensor type, the system uses one universal normalization and comparison mechanism that works with multiple signal types, reducing overall system complexity while maintaining comprehensive measurement capabilities.
Data Source
AI summary
Disclosed is an apparatus for measuring movements of a user, which may include: a sample pattern storing unit for storing one or more sample patterns which are obtained by patterning EEG waveforms and EMG values; a communication unit for receiving an EEG waveform and an EMG value of the user measured by the EEG and EMG sensors; a pattern normalizing unit for selecting sample patterns with the highest similarity to the received user's patterns among the stored sample patterns, and for normalizing the received user's patterns so that their amplitudes and periods coincide with those of the selected sample patterns; a movement estimating unit for estimating movement of the user by inputting the normalized EEG waveform and EMG value in an artificial neural network algorithm; and a movement determining unit for calculating the user's movement by applying each weight value of the EEG and EMG to each movement estimation value.