Mobile Sensor Data Collection via Mode Transition Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for tracking drivers' transportation modes using mobile devices are inefficient, requiring manual activation of sensors and consuming battery power, and lack accuracy in determining driving behaviors.
Innovation Solution
A system and method that utilize mobile devices with sensors, processors, and memory to analyze movement measurements, determine transportation modes, and create transition models to automatically activate sensors only when the probability of driving is above a threshold, thereby collecting driving data without user intervention and conserving battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are continuously activated to track driving behaviors, then data collection accuracy is improved, but battery consumption increases
Solution Approach 1:
The patent applies dynamics by making the sensor activation state changeable based on detected transportation modes. The system dynamically transitions between sensor-on and sensor-off states according to whether driving is detected, allowing the system to adapt its energy consumption characteristics to actual usage conditions rather than maintaining a fixed state.
Solution Approach 2:
The patent changes the operational parameter of sensor activation from a continuous state to a conditional state based on transportation mode detection. By modifying the activation parameter according to detected modes (walking, biking, driving, stationary), the system achieves accurate data collection only when needed while reducing overall energy consumption.
2Use of energy by moving object
If manual activation is required for sensor tracking, then battery consumption is reduced, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically detect transportation modes and autonomously activate sensors without user intervention. The mobile device serves itself by using its own sensors to detect mode transitions and trigger appropriate data collection, eliminating the need for manual user actions while maintaining energy efficiency.
Solution Approach 2:
The system uses feedback from sensor data to automatically determine transportation modes and control sensor activation. The detection system continuously monitors movement patterns, provides feedback about the current mode, and uses this feedback to automatically adjust sensor activation states, creating a closed-loop system that is both energy-efficient and easy to use.
3Productivity
If sensors are activated without mode detection, then data collection efficiency is reduced, but device complexity is simplified
Solution Approach 1:
The patent segments the data collection process by dividing it into mode-specific phases. Instead of uniform continuous collection, the system segments activation into distinct states corresponding to different transportation modes (walking, biking, driving, stationary), collecting data efficiently only during relevant segments while avoiding unnecessary collection during others.
Solution Approach 2:
The system performs preliminary action by detecting transportation modes before activating sensors for detailed data collection. This preliminary mode detection phase prepares the system by identifying when driving is occurring, allowing sensors to be activated only when needed, thereby improving overall data collection efficiency without requiring complex continuous monitoring from the outset.
Data Source
AI summary
A system for collecting vehicle data includes a mobile device comprising a plurality of sensors, a memory, and a processor coupled to the memory. The processor is configured to perform operations including obtaining a plurality of movement measurements from at least one of the plurality of sensors in the mobile device, determining a plurality of transportation modes using the plurality of movement measurements, and determining a mode transition using the plurality of transportation modes. The operations also include determining a transition probability using the mode transition. The operations further include creating a transition model using the transition probabilities, determining that the transition model indicates that a probability that the second transportation mode comprises driving is above a threshold, obtaining a plurality of driving movement measurements from at least one of the plurality of sensors, and determining the vehicle data using the driving movement measurements.