Movement Data Collection for Automatic User Preference Matching
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Solution Overview
Problem
Current social software relies on manual input of user interests, which is cumbersome, and fails to recommend users with similar movement tracks, limiting social connections based on shared mobility patterns.
Innovation Solution
A data collection method and system that uses movement information collection devices equipped with sensors to automatically determine user preferences by analyzing movement data, such as speed and location, and sends this information to a server to assign users to groups with similar preferences, including those with similar movement tracks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual input of user interests is used, then user preferences can be collected, but the process becomes cumbersome and users may skip the input step
Solution Approach 1:
The system automatically collects movement data from sensors in the movement information collection device and determines user preferences without requiring manual user input. The device serves itself by autonomously gathering data through acceleration sensors, gyroscopes, and positioning modules, then processing this data to identify movement preferences, exercise preferences, and location preferences, thereby eliminating the need for cumbersome manual input while ensuring preference information is captured
Solution Approach 2:
The patent replaces the mechanical manual input process with an automated sensing and processing system. Sensors (acceleration sensors, gyroscopes, positioning modules) automatically detect and record user movement data, which is then processed by the control unit to determine preferences. This substitution of manual operation with automated sensing systems resolves the contradiction between information collection completeness and operational ease
2Adaptability or versatility
If only origins and destinations are used for user recommendation, then simple matching can be achieved, but users with similar movement tracks cannot be recommended
Solution Approach 1:
The patent segments the user preference data into three distinct categories: movement preferences (derived from acceleration and gyroscope data), exercise preferences (derived from movement patterns and duration), and location preferences (derived from positioning data). This segmentation allows the system to analyze and match users based on multiple independent dimensions, expanding recommendation scope from simple origin-destination matching to comprehensive movement track matching while organizing complex data into manageable segments
Solution Approach 2:
The patent adds temporal and behavioral dimensions to the recommendation system by incorporating movement speed, acceleration patterns, gyroscope swing angles, and exercise duration. This transforms the recommendation from static location-based matching to dynamic movement-track-based matching, enabling identification of users with similar movement characteristics even when their specific origins and destinations differ, thereby significantly expanding the adaptability of user recommendations
3Extent of automation
If movement data is collected through sensors, then automatic preference determination is achieved, but device complexity increases
Solution Approach 1:
The movement information collection device integrates multiple sensors (acceleration sensors, gyroscopes, positioning modules) that serve multiple functions: they collect raw movement data, track location, monitor exercise patterns, and provide input for preference determination. This multi-functionality allows the system to achieve comprehensive automation in preference determination while consolidating multiple data collection tasks into a single integrated device, thereby managing complexity through functional integration rather than separate systems
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
Enables automatic and accurate user preference determination, facilitating social connections based on real-time mobility patterns, reducing the need for manual input and improving the relevance of user recommendations.
Implementation Method 1
an acceleration sensor is arranged in the movement information collection device. The obtaining the movement data of the user includes: detecting, by the acceleration sensor, a movement speed of the user
Implementation Method 2
a gyroscope is arranged in the movement information collection device. The obtaining the movement data of the user includes: detecting, by the gyroscope, a swing angle of the user
Data Source
AI summary
A data collection method, a device, and a system are provided for automatically obtaining a user's movement interests. The data collection method includes: a movement information collection device obtaining a user's movement data; analyzing said user's movement data and determining the user's movement preferences according to said movement data; sending the user's information of the movement preference to a server, so that the server determines according to the user's information of the movement preference other users corresponding to said movement preferences. It is thus possible to automatically obtain a user's movement preferences.