Automatic Breadcrumb Generation via Movement Chronology
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Solution Overview
Problem
Existing mobile devices lack the ability to automatically mark locations of interest, requiring user intervention to generate breadcrumbs, which can lead to forgotten locations and difficulty in returning to them.
Innovation Solution
A system and method that detect movements of a computing device, determine their chronological order, and identify user activities to automatically mark geographic locations associated with these activities, allowing for automatic generation of breadcrumbs without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the user manually controls the device to generate breadcrumbs at locations of interest, then the location marking function is available, but the user may forget to mark locations and cannot return to them
Solution Approach 1:
The system automatically detects user activities (such as parking a vehicle) through movement data from sensors and GPS, and autonomously generates breadcrumbs without requiring manual user intervention. The device serves itself by interpreting its own movement patterns to identify significant locations and create markers, eliminating the need for the user to remember to manually mark each location.
Solution Approach 2:
The system performs preliminary analysis of movement data to identify potential locations of interest before the user needs to return to them. By continuously monitoring movement patterns and chronologically ordering detected movements, the system proactively marks locations that are likely to be significant, ensuring they are recorded before the user might forget them.
2Extent of automation
If the system automatically identifies user activities based on device movements, then breadcrumb generation becomes automatic, but the system complexity increases
Solution Approach 1:
The system uses existing multi-functional components (GPS receiver, accelerometer, gyroscope, magnetometer) that are already present in modern mobile devices for other purposes. By repurposing these existing sensors and processors to also detect movement patterns and identify user activities, the system achieves automatic breadcrumb generation without adding significant hardware complexity.
Solution Approach 2:
The system introduces a movement detection module as an intermediary layer between the raw sensor data and the breadcrumb generation function. This module chronologically orders detected movements and identifies activity patterns, serving as a mediator that translates complex sensor inputs into meaningful location markers, thereby managing system complexity through modular design.
Data Source
AI summary
Disclosed herein are methods and systems for identifying an activity of a user based on a chronological order of detected movements of a computing device. According to embodiments of the present disclosure, the method may include detecting movements of a computing device. The method also includes determining a chronological order of the detected movements. Further, the method includes identifying an activity of a user of the computing device based on the detected movements and chronological order. The method also includes determining a geographic location associated with at least one of the movements. Further, the method includes presenting identification of the activity and the geographic location.