Passive Mobile Sensor Monitoring for Driving Behavior Capture
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Solution Overview
Problem
Current technologies lack effective methods to understand and utilize the vast amount of passive data from mobile devices, such as movement patterns and device usage during inactive periods, which account for the majority of user interaction.
Innovation Solution
A system and method for passively capturing and monitoring device behaviors by utilizing the Operating System's activity identifiers and sensor data to track movements and activities, such as driving, without the need for external peripherals like OBD devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external peripherals like OBD devices are used to track driving behaviors, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the driving behavior tracking function from external OBD devices and implements it within the mobile device itself using built-in sensors (accelerometer, GPS, gyroscope) and OS activity identifiers. This eliminates the need for separate external peripherals while maintaining tracking capability.
Solution Approach 2:
The mobile device's existing sensors and processing capabilities are made multi-functional by using them for both standard mobile functions and driving behavior monitoring. The same accelerometer and GPS used for general navigation also detect driving patterns, eliminating the need for dedicated tracking hardware.
2Loss of information
If continuous monitoring of device activities is performed, then loss of information is reduced, but use of energy increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic sampling of sensor data and OS activity identifiers. Data collection occurs at intervals defined by timer expirations and activity transitions, reducing energy consumption while still capturing sufficient information about driving behaviors.
Solution Approach 2:
The system performs preliminary actions by having the OS service continuously generate activity identifiers in the background without active intervention. The monitoring application only processes these identifiers when timers expire or transitions occur, minimizing energy usage while maintaining data capture.
3Productivity
If passive data from mobile devices is captured and analyzed, then productivity is improved, but device complexity increases
Solution Approach 1:
The OS activity identifier service acts as an intermediary that continuously generates structured activity data without requiring complex monitoring logic. The monitoring application simply consumes these standardized identifiers along with sensor data, reducing the complexity of data collection and processing.
Solution Approach 2:
The system creates simplified copies of driving behavior data by recording essential parameters (activity identifiers, sensor readings, timestamps) without capturing complete raw sensor streams. This reduced-data approach maintains analytical value while simplifying storage and processing requirements.
Data Source
AI summary
A determination is made that a mobile device is associated with a reference activity of a user based on motion, orientation, rotational, magnetic field, and/or location data provided by sensors of the device. Activity data associated with the reference activity is obtained from the sensor-provided data. The activity data is recorded on the device for a configured period of time after which it is determined that the device is no longer performing the reference activity. The retained activity data for the reference activity is sent from the device to a network-based behavior analyzer when a network connection is available from the device. The network-based behavior analyzer derives user behaviors for the reference activity based on the activity data.