Georeferenced Multimedia Guides Adapted to User Movement and Viewpoint
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Solution Overview
Problem
Existing digital tourist guides fail to consider user movement, speed, and importance scale of points of interest, leading to distractions and inefficiencies in content delivery, and lack cost-effective, dynamic adaptation in mobility.
Innovation Solution
A method utilizing artificial intelligence to generate and deliver mobile georeferenced digital content based on user position, direction, and speed, incorporating immersive maps and computer vision to optimize content delivery according to movement, scale, and user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital tourist guides deliver content based on elementary logics linked to points of interest or sequential itineraries, then content delivery is simple to implement, but the system lacks adaptability to user movement and contextual factors
Solution Approach 1:
The system dynamically adapts content delivery based on real-time user movement parameters (speed, direction, acceleration) detected by mobile device sensors. The guide transitions from static point-of-interest-based delivery to dynamic movement-aware delivery, adjusting content presentation according to user motion state without requiring complex manual configuration
Solution Approach 2:
The system continuously monitors user movement through accelerometer, gyroscope, and GPS data, using this feedback to automatically adjust content delivery timing, type, and presentation mode. This closed-loop feedback mechanism enables adaptive content delivery while maintaining system manageability through automated sensor-based decision making
2Measurement precision
If computer vision techniques recognize monuments, then visual identification capability is improved, but the system fails to consider user speed and importance scale of points of interest
Solution Approach 1:
The system merges computer vision monument recognition with movement sensor data (accelerometer, gyroscope, GPS) and contextual information about point of interest importance. This integration creates a unified system that simultaneously performs visual identification and adapts content delivery based on user movement and location significance, resolving the limitation of considering only visual recognition
3Loss of information
If the system provides written messages and visual messages to users in motion, then information delivery is comprehensive, but user safety and ergonomics are compromised due to distraction
Solution Approach 1:
The system changes the parameter of information presentation mode based on detected user movement state. During active movement, it transitions from visual/textual messages to audio-based delivery. This parameter change maintains information completeness while eliminating the harmful distraction effect, as audio delivery does not require visual attention from the moving user
4Adaptability or versatility
If content production systems are designed to be dynamically adaptable to mobility, then content relevance to user movement is improved, but development cost and complexity increase
Solution Approach 1:
The system uses the mobile device's existing sensors (accelerometer, gyroscope, GPS) to automatically detect user movement and trigger appropriate content delivery modes. This self-service approach leverages hardware already present in user devices, enabling dynamic mobility adaptation without requiring expensive external sensors or complex manual configuration, thereby reducing development cost while maintaining adaptability
Data Source
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AI summary
A method is described for producing, organizing and distributing mobile georeferenced digital content, carried out by means of electronic processing. The method comprises providing or accessing at least one geolocated digital map, adapted to provide a three-dimensional visual model, and/or comprising other digital multimedia content, of each of a plurality of points of interest present in a geographical area covered by the geolocated digital map. The method then comprises the steps of obtaining or determining geolocation data of a user, comprising at least one position of the user and information relating to the movement of the user; and also of determining, based on said geolocation data of the user, a digital point of view of the user, within the geolocated digital map, representative of a real point of view of the user in real space, dynamically variable depending on the movement of the user. Said digital point of view is associated with at least one of said three-dimensional models present in the geolocated digital map, corresponding to points of interest visible from the real point of view of the user. The method then provides for generating by means of artificial intelligence, and/or deriving from the geolocated digital map, digital data of points of interest, comprising at least one three-dimensional visual model and/or other digital multimedia content relating to at least one of said points of interest associated with the digital point of view of the user. Said digital data of points of interest are dynamically variable depending on the movement of the user. The method finally comprises the step of organizing and providing to the user, through a digital display interface of a user's mobile device, said digital data of points of interest, dynamically variable, generated and/or derived, associated with the digital point of view of the user. A corresponding method for the development of contextual multimedia guides is also described. A corresponding system for producing, organizing and distributing mobile georeferenced digital content is also described.