Mobile Device Movement Detection Using Accelerometer Gravity Separation
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Solution Overview
Problem
Existing methods for detecting mobile device movement within a vehicle are not accurate or power-efficient, and they fail to calculate a risk score or provide feedback to improve safety against distracted driving.
Innovation Solution
Collecting and processing acceleration data from a mobile device's accelerometer to separate the gravity component, determining mobile device movement events, and aggregating sensor data to calculate a risk score, which is then used to generate notifications for feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acceleration data is collected and processed to detect mobile device movement, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments the detection process into distinct phases: a first time period with continuous acceleration data collection at high precision, and a second time period with reduced or suspended collection. This temporal segmentation allows the system to maintain high measurement precision when needed while reducing energy consumption during periods when movement is less likely or already detected.
Solution Approach 2:
The system implements periodic action by alternating between active detection phases (first time period) and reduced activity phases (second time period). The processor periodically evaluates whether to switch between these states based on detected movement events, creating a rhythmic pattern of high and low energy consumption that maintains detection capability while managing power usage.
2Reliability
If sensor data is aggregated over a time window to calculate risk score, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-defining time windows for data aggregation and pre-establishing risk score calculation parameters. When a movement event is detected during the first time period, the system has already prepared the framework for rapid aggregation of sensor data from the second time period, enabling quick risk assessment without ad-hoc configuration delays.
Solution Approach 2:
The patent applies dynamics by making the time window duration and aggregation parameters adaptive rather than fixed. The system can adjust the length of the second time period and the amount of sensor data aggregated based on the severity and type of movement detected, allowing the risk calculation process to dynamically balance between thoroughness (reliability) and speed (time loss).
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
This approach provides accurate and power-efficient detection of mobile device movement, enabling the calculation of a risk score and subsequent feedback to improve safety by reducing distracted driving.
Implementation Method 1
collecting acceleration data from an accelerometer associated with a mobile device
Implementation Method 2
The acceleration data may be processed to separate and remove a gravity component
Data Source
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AI summary
One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.