EEG Helmet for Accident Detection and Airbag Deployment
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Solution Overview
Problem
Personal mobility users face increased risk of severe injury due to exposure during accidents, as conventional helmets lack advanced safety features to detect and respond to accident situations in real-time.
Innovation Solution
A helmet equipped with an EEG detector to measure brain waves, a controller to determine accident-related situations, and a wearable airbag device that deploys based on EEG patterns, user images, and speech recognition, providing enhanced safety through automated safety device operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional helmets are used without advanced detection devices, then the device complexity is low, but the safety and injury protection capability is insufficient
Solution Approach 1:
The helmet system is divided into multiple functional modules: EEG detector for brain wave monitoring, accelerometer for impact detection, camera for visual verification, microphone for audio analysis, and controller for integrated decision-making. Each module independently performs specific detection tasks, and their combined output enables comprehensive accident detection and safety device activation.
Solution Approach 2:
The helmet integrates multiple detection functions (EEG monitoring, acceleration sensing, visual capture, audio recording) into a single multi-functional device. This universal approach allows the helmet to detect various types of accidents (collisions, falls, medical emergencies) using different sensing modalities, improving reliability while consolidating the device structure.
2Measurement precision
If multiple detection devices (EEG detector, camera, microphone) are integrated in the helmet, then the measurement precision and detection accuracy improve, but the device complexity increases
Solution Approach 1:
The controller receives continuous feedback from multiple sensors (EEG detector, accelerometer, camera, microphone) and processes this information to determine accident situations. The system uses feedback loops where sensor data is constantly monitored, analyzed, and used to trigger appropriate safety responses, improving detection precision through multi-source verification.
Solution Approach 2:
The patent combines multiple detection devices (EEG detector, camera, microphone, accelerometer) into a single integrated helmet system. By merging these devices and their data processing functions into one unified controller, the system achieves high measurement precision through multi-modal detection while managing complexity through integrated architecture.
3Device complexity
If the helmet uses EEG signals alone for accident detection, then the device complexity is reduced, but the reliability of accident situation determination is insufficient
Solution Approach 1:
The system performs preliminary detection using EEG signals to identify potential accident situations before full confirmation. The EEG detector continuously monitors brain wave patterns for signs of stress, impact, or medical emergencies, triggering preliminary alert states that can be confirmed by other sensors, enabling early intervention while maintaining reliable determination through progressive verification.
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
The helmet effectively improves user safety by detecting pre-accident and accident situations, deploying safety devices like airbags to mitigate injury, leveraging EEG signals, user imagery, and speech analysis for reliable and timely responses.
Implementation Method 1
an electroencephalogram (EEG) detector provided on the body and configured to detect EEG of the user
Data Source
AI summary
A helmet configured for improving the safety of a user of a personal mobility by measuring an electroencephalogram (EEG) of the user using an EEG detector provided in a helmet, determining an accident situation based on the measured EEG, and operating a safety device is provided. The helmet includes a body configured to form an exterior of the helmet and can be worn on a user's head; the EEG detector provided on the body and configured to detect EEG of the user; and a controller configured to determine an accident-related situation based on an EEG signal output from the EEG detector, and to generate a control signal for operating a safety device according to the determined accident-related situation.