Devices and methods for recognizing driving behavior based on movement data

1. An electronic device, comprising:

at least one sensor,

at least one processor-readable storage medium, including a set of instructions for detecting predefined driving behaviors; and

at least one processor in communication with the at least one processor-readable storage medium, wherein when executing the set of instructions, the at least one processor is directed to:

obtain first electronic signals encoding movement data associated with the electronic device from the at least one sensor at a target time point, wherein the first electronic signals encoding movement data is associated with a user behavior and the user behavior includes at least one of a sound-related behavior, a facial expression, a motion behavior, a dressing-up behavior, a ceremonial behavior;

operate logic circuits in the at least one processor to filter out unwanted information from the first electronic signals encoding movement data based on a first machine learning trained model;

operate the logic circuits to determine whether the first electronic signals encoding movement data meets a precondition based on a second machine learning trained model, the precondition including a frequency of a keyword or a facial expression mode;

upon the first electronic signals encoding movement data meeting the precondition, send second electronic signals encoding movement data within a predetermined time period associated with the target time point by a varying transmitting frequency; and

send the second electronic signals encoding movement data within the predetermined time period associated with the target time point to a remote server.

2. The electronic device of claim 1 , wherein the at least one sensor includes at least one of a gyroscope, an acceleration sensor, a global position system (GPS), or a gravity sensor.

3. The electronic device of claim 1 , wherein the at least one processor is further directed to:

determine that the electronic device is moving with a vehicle.

4. The electronic device of claim 1 , wherein the first electronic signals encoding movement data includes at least one of linear acceleration, angular acceleration, or gesture information, the gesture information including role information, yaw information, or pitch information.

5. The electronic device of claim 1 , wherein the at least one sensor periodically generates the first electronic signals encoding movement data, and the at least one processor periodically obtain the first electronic signals encoding movement data.

6. The electronic device of claim 1 , wherein the unwanted information is noise information associated with at least one of shaking the electronic device, dropping the electronic device onto a bottom of a vehicle, or an electronic malfunction of the electronic device.

7. The electronic device of claim 1 , wherein the precondition includes at least one of a threshold of a linear acceleration, a threshold of a lasting time of the linear acceleration, a threshold of an angular acceleration, a threshold of a lasting time of the angular acceleration, a number of changes of an acceleration direction during a predetermined length of time, or a number of changes of a gesture with a time length.

8. The electronic device of claim 1 , wherein to said send the second electronic signals encoding movement data within the predetermined time period associated with the target time point to the remote server, the at least one processor is further directed to:

determine a trigger time point once the first electronic signals encoding movement data meets the precondition; and

select the second electronic signals encoding movement data in the predetermined time period based on the trigger time point.

9. A method for detecting predefined driving behaviors, comprising:

obtaining, by an electronic device, first electronic signals encoding movement data associated with the electronic device at a target time point, wherein the first electronic signals encoding movement data is associated with a user behavior and the user behavior includes at least one of a sound-related behavior, a facial expression, a motion behavior, a dressing-up behavior, a ceremonial behavior;

filtering out unwanted information from the first electronic signals encoding movement data based on a first machine learning trained model;

determining, by the electronic device, whether the first electronic signals encoding movement data meets a precondition based on a second machine learning trained model, the precondition including a frequency of a keyword or a facial expression mode;

upon the first electronic signals encoding movement data meeting the precondition, sending, by the electronic device, second electronic signals encoding movement data within a predetermined time period associated with the target time point by a varying transmitting frequency; and

sending, by the electronic device, the second electronic signals encoding movement data within the predetermined time period associated with the target time point to a remote server.

10. The method of claim 9 , further comprising:

determining that the electronic device is moving with a vehicle.

11. The method of claim 9 , wherein the first electronic signals encoding movement data includes at least one of linear acceleration, angular acceleration, or gesture information, the gesture information including role information, yaw information, or pitch information.

12. The method of claim 9 , wherein the electronic device periodically obtain the first electronic signals encoding movement data.

13. The method of claim 9 , wherein the unwanted information is noise information associated with at least one of shaking the electronic device, dropping the electronic device onto a bottom of a vehicle, or an electronic malfunction of the electronic device.

14. The method of claim 9 , wherein the precondition includes at least one of a threshold of a linear acceleration, a threshold of a lasting time of the linear acceleration, a threshold of an angular acceleration, a threshold of a lasting time of the angular acceleration, a number of changes of an acceleration direction during a predetermined length of time, or a number of changes of a gesture with a time length.

15. The method of claim 9 , wherein the sending by the electronic device of the second electronic signals encoding movement data within the predetermined time period associated with the target time point to the remote server includes:

determining, by the electronic device, a trigger time point once the first electronic signals encoding movement data meets the precondition; and

selecting, by the electronic device, the second electronic signals encoding movement data in the predetermined time period based on the trigger time point.

16. A non-transitory computer readable medium comprising instructions configured to cause an electronic device to:

obtain first electronic signals encoding movement data associated with the electronic device from at least one sensor at a target time point, wherein the first electronic signals encoding movement data is associated with a user behavior and the user behavior includes at least one of a sound-related behavior, a facial expression, a motion behavior, a dressing-up behavior, a ceremonial behavior;

operate logic circuits in at least one processor to filter out unwanted information from the first electronic signals encoding movement data based on a first machine learning trained model;

operate the logic circuits to determine whether the first electronic signals encoding movement data meets a precondition based on a second machine learning trained model, the precondition including a frequency of a keyword or a facial expression mode;

upon the first electronic signals encoding movement data meeting the precondition, sending, by the electronic device, second electronic signals encoding movement data within a predetermined time period associated with the target time point by a varying transmitting frequency; and

send the second electronic signals encoding movement data within the predetermined time period associated with the target time point to a remote server.