Smart Helmet Fall Detection Using Three-Axis Acceleration
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Solution Overview
Problem
Existing technologies lack effective methods for detecting falling events during riding activities, which can lead to delayed rescue in accidents, especially in remote areas, due to the difficulty in wearing and positioning detection equipment on riders.
Innovation Solution
A smart helmet equipped with a three-axis acceleration sensor that monitors acceleration, calculates vector sums, and determines free fall, collision, and motionlessness events to generate emergency signals, utilizing predetermined conditions and thresholds to accurately detect and alert for falling incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection equipment is installed on riders, then falling events can be detected, but it is inconvenient for riders to wear detecting equipment during sports
Solution Approach 1:
The smart helmet integrates multiple functions including falling detection, collision detection, motionlessness detection, and emergency signaling into a single device that riders already wear for protection, eliminating the need for separate detection equipment
Solution Approach 2:
The system automatically detects falling events and generates emergency signals without requiring rider intervention, even when the rider is unconscious or unable to move, making the detection process self-service
2Ease of operation
If detection equipment is installed in other suitable positions, then it may be more convenient for riders, but there is no existing technology for detecting falling events of riders specifically
Solution Approach 1:
The system uses different detection thresholds and criteria for different types of events (free fall threshold of 0.3-0.6g, collision threshold of 1.5-2g) to accurately distinguish between various falling scenarios specific to riders
Solution Approach 2:
The system changes detection parameters based on the specific context, using vector sum of acceleration for free fall detection and individual axis acceleration for collision detection, with different time duration thresholds for each event type
3Reliability
If the system detects all types of falling events, then comprehensive protection is provided, but it may generate false alarms by not distinguishing between riding and walking fall patterns
Solution Approach 1:
The system dynamically adjusts detection sensitivity and criteria based on the detected acceleration patterns, using different thresholds and time durations for free fall, collision, and motionlessness events to accurately distinguish between genuine falling accidents and normal riding movements
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 smart helmet effectively detects falling events and triggers timely alerts, reducing hazards by accurately distinguishing between riding and walking fall patterns and ensuring prompt rescue through targeted alarm generation.
Implementation Method 1
a three-axis acceleration sensor, configured to detect acceleration of three axes
Data Source
AI summary
The present invention provides a smart helmet falling detection method and a smart helmet. The falling detection method includes following steps: initializing a system, determining a free fall, determining a collision, determining motionlessness, and generating an emergency signal according to a detection result that a motionlessness event occurs within a fourth duration after the collision event occurs. The smart helmet includes a three-axis acceleration sensor and a controller. For the smart helmet falling detection method and the smart helmet provided in the present invention, the acceleration is measured by the three-axis acceleration sensor installed on the smart helmet, and whether a riding falling event occurs is determined by analyzing change of the acceleration, and alarm is given according to the falling event, such that the problem of falling detection and calling for help in a riding process is solved in a targeted manner.