Smart helmet fall detection method and smart helmet

1. A smart helmet falling detection method, characterized by including following steps: monitoring acceleration of three axes according to a predetermined monitoring frequency; calculating a vector sum of the acceleration of the three axes, and determining whether a free fall event occurs according to whether the vector sum of the acceleration satisfies a first predetermined condition; determining whether a collision even occurs according to whether the acceleration of any axis of the three axes satisfies a second predetermined condition; determining whether a motionlessness event occurs according to whether an acceleration variation detected for each axis meets a third predetermined condition; and generating an emergency signal according to whether the motionlessness event meets a fourth predetermined condition.

2. The smart helmet falling detection method according to claim 1 , characterized in that the smart helmet falling detection method further including: comparing a time interval between the collision event and the free fall event with a predetermined invalidation duration; and executing the step of determining whether a motionlessness event occurs according to a comparison result that the time interval is smaller than or equal to the invalidation duration, or returning to the step of monitoring acceleration of three axes according to a comparison result that the time interval is greater than the invalidation duration.

3. The smart helmet falling detection method according to claim 1 , characterized in that the first predetermined condition is that the vector sum of the acceleration is continuously smaller than a first threshold and a duration of the acceleration is greater than a first duration, the second predetermined condition is that the acceleration of the any axis is continuously greater than a second threshold and a duration of the acceleration is greater than a second duration, the third predetermined condition is that a change variable of the acceleration is continuously smaller than a third threshold and a duration of the acceleration is greater than a third duration, and the fourth predetermined condition is that the motionlessness event takes place within a fourth duration after occurrence of the collision event, wherein the third duration is shorter than the fourth duration.

4. The smart helmet falling detection method according to claim 3 , characterized in that the first threshold is 0.3˜0.6 g, and the first duration is 400 ms˜500 ms; the second threshold is 1.5˜2 g, and the second duration is 300 ms˜450 ms; the third threshold is 0.3 g˜0.6 g, and the third duration is 8˜15 s; a difference between the fourth duration and the third duration is 2˜5 s.

5. The smart helmet falling detection method according to claim 4 , characterized in that the first threshold is 0.54 g, the first duration is 450 ms, the second threshold is 1.992 g, the second duration is 400 ms, the third threshold is 0.5 g, the third duration is 11 s, and the fourth duration is 15 s.

6. The smart helmet falling detection method according to claim 1 , characterized in that the falling detection method further including: detecting whether the motionlessness event occurs continuously; and generating an alarm cancellation signal to stop the emergency signal if the motionlessness event is not detected continuously three times.

7. A smart helmet, characterized in that the smart helmet comprising: a three-axis acceleration sensor, being configured to detect acceleration of three axes; and a controller, being configured to monitor the acceleration of the three axes according to a predetermined monitoring frequency; calculating a vector sum of the acceleration of the three axes, and determining whether a free fall event occurs according to whether the vector sum of the acceleration satisfies a first predetermined condition; determining whether a collision even occurs according to whether the acceleration of any axis of the three axes satisfies a second predetermined condition; determining whether a motionlessness event occurs according to whether an acceleration variation detected for each axis meets a third predetermined condition; and generate an emergency signal according to whether the motionlessness event meets a fourth predetermined condition.

8. The smart helmet according to claim 7 , characterized in that the first predetermined condition is that the vector sum of the acceleration is continuously smaller than a first threshold and a duration of the acceleration is greater than a first duration, the second predetermined condition is that the acceleration of the any axis is continuously greater than a second threshold and a duration of the acceleration is greater than a second duration, the third predetermined condition is that the acceleration variation is continuously smaller than a third threshold and a duration of the acceleration is greater than a third duration, and the fourth predetermined condition is that the motionlessness event takes place within a fourth duration after occurrence of the collision event, wherein the third duration is shorter than the fourth duration.

9. The smart helmet according to claim 7 , characterized in that the smart helmet further comprising a warning light being configured to turn on a falling warning lighting mode.

10. The smart helmet according to claim 7 , characterized in that the smart helmet further comprising a GPS positioning module and a wireless communication module, the GPS positioning module being configured to send current geographical position information to the controller, and the wireless communication module being configured to send emergency information to a predetermined number via a mobile terminal associated with the smart helmet, wherein the emergency information carries the current geographical position information.