Mobile Crash Detection for Motorcycles Using Multimodal Sensors
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Solution Overview
Problem
Motorcycle riders often cannot use their mobile devices to call for emergency assistance after an accident, especially in locations without bystanders.
Innovation Solution
A mobile device, such as a smartwatch or smartphone, detects a motorcycle crash using multimodal features from sensors like accelerometers, gyroscopes, and microphones, and if the user does not respond, it automatically contacts emergency services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a motorcycle rider uses their mobile device to call for emergency assistance after an accident, then emergency services can be contacted, but the rider is unable to use their mobile phone when injured or incapacitated
Solution Approach 1:
The system enables the mobile device to automatically detect crashes and contact emergency services without requiring user operation. The crash detection system uses sensors to identify accident events and autonomously initiates emergency calls, allowing the device to serve itself when the rider is incapacitated.
Solution Approach 2:
The system performs preliminary crash detection and assessment before emergency services are needed. By continuously monitoring sensor data and detecting crash events in advance, the system prepares emergency contact information and can automatically initiate calls before the rider loses consciousness or ability to act.
2Reliability
If the system automatically contacts emergency services without user confirmation, then timely assistance is ensured for incapacitated riders, but false alarms may occur
Solution Approach 1:
The system changes the parameters of crash detection by using multiple sensing modalities (accelerometers, gyroscopes, microphones) instead of relying on a single sensor threshold. This multi-parameter approach allows the system to distinguish between genuine crashes and false alarm conditions by analyzing patterns across different sensor types simultaneously.
Solution Approach 2:
The system incorporates feedback mechanisms where crash detection algorithms continuously analyze sensor data streams and adjust detection sensitivity based on contextual information. The system processes sensor inputs, determines whether a crash event has occurred, and only triggers emergency contact when the detection confidence exceeds predetermined thresholds, reducing false alarms.
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 system effectively detects motorcycle crashes and ensures emergency services are contacted if the rider is incapacitated, reducing false alarms and ensuring timely assistance.
Implementation Method 1
impact data as the user hits the ground captured by at least one accelerometer of the crash device
Implementation Method 2
user orientation data captured by at least one rotation sensor of the crash device
Implementation Method 3
sound pressure level of audio data captured by at least one microphone of the crash device
Data Source
AI summary
Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a motorcycle crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a motorcycle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.