Hand Trajectory Restoration From EEG and Proprioceptive Signals
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Solution Overview
Problem
Existing user interface devices face limitations such as spatial constraints, sensor dead spaces, and high concentration requirements due to the use of external devices and external stimuli, making it difficult to track complex hand movements accurately using electroencephalography and electromyography.
Innovation Solution
A method and device that utilize electroencephalography and proprioceptive data, combined with deep learning models, to decode a user's intrinsic biosignals without external stimulation, allowing for the restoration of complex hand movements like handwriting by training an encoder to extract proprioceptive latent information and a decoder to generate movement paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If external devices (cameras, sensors) are installed at a predetermined distance from the user to track hand movements, then movement tracking capability is provided, but spatial constraints and sensor dead spaces occur that limit accurate tracking of complex hand movements
Solution Approach 1:
The patent replaces external mechanical tracking devices (cameras, sensors) with a biosignal-based detection system. By using electroencephalography to detect brain waves related to imagined hand movements and combining them with proprioceptive data from inertial sensors worn on the body, the system eliminates the need for external tracking equipment positioned at specific distances, thereby removing spatial constraints and sensor dead spaces while maintaining movement tracking capability
Solution Approach 2:
The patent introduces proprioceptive data as an intermediary between the user's imagined movements and the final trajectory output. The proprioceptive information from inertial sensors serves as a bridge that connects the user's internal movement intention (detected via EEG) with the actual movement trajectory, enabling accurate reconstruction without external visual or mechanical feedback
2Ease of operation
If electroencephalography is used to detect brain waves for hand movement control, then non-contact control is achieved, but the signal-to-noise ratio is high making it difficult to determine exact signal origins and limit complex interaction capabilities
Solution Approach 1:
The patent merges electroencephalography data with proprioceptive data from inertial sensors to overcome the limitations of using EEG alone. By combining these two data sources, the system maintains the non-contact control advantage of EEG while adding the spatial precision and movement detail from proprioceptive sensors, enabling both simple and complex interaction capabilities
Solution Approach 2:
The patent creates a composite biosignal system by integrating multiple types of biosignals (EEG and proprioceptive data) into a unified control framework. This composite approach allows the system to leverage the complementary strengths of each signal type: EEG provides intent detection without contact, while proprioceptive data provides precise movement trajectory information
3Adaptability or versatility
If existing BCI-based communication devices use external stimuli to decode user intentions, then communication capability is provided, but high concentration and training requirements make it difficult to execute complex instructions
Solution Approach 1:
The patent enables the system to work with the user's intrinsic biosignals without requiring external stimuli or high levels of concentration. By using proprioceptive data from naturally occurring movement sensations combined with EEG, the system allows users to control interfaces through imagined movements without needing to maintain intense focus or undergo extensive training, thereby reducing operational complexity while maintaining communication capability
Data Source
AI summary
The present disclosure relates to a method, device, and program of restoring a trajectory of hand movement based on a biosignal. More specifically, the present disclosure relates to a method, device and program of providing a user's intended hand movement or character as a trajectory or text based on a biosignal when there is a hand movement such as handwriting so as to allow various interface interactions such as communication and drawing.