Navigation Cost Data Generation Using User Fitness Profiles
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Solution Overview
Problem
Current navigation systems lack the ability to effectively generate cost data for routes that involve off-road segments, which are challenging due to varying levels of difficulty for different users based on their fitness and experience, and are not dynamically updated to reflect changes in trail conditions.
Innovation Solution
A method and system that collect probe data from users on their movements and physical exertion along off-road segments, process this data using ability profiles to generate normalized routing cost data, and associate this data with the electronic map, allowing for dynamic updating of trail conditions and user-specific difficulty assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional navigation systems use fixed cost data for road segments, then routing efficiency is improved, but the system cannot adapt to varying user fitness levels and trail conditions
Solution Approach 1:
The system pre-processes probe data from multiple users and stores normalized routing cost data in a database before actual route generation. This preliminary action allows the system to quickly retrieve and apply user-specific cost data without complex real-time calculations, resolving the contradiction between adaptability and system complexity
Solution Approach 2:
The system changes the parameter of routing cost from fixed values to dynamic values that vary based on user fitness level, trail conditions, and environmental factors. By normalizing probe data from multiple users with different abilities, the system creates adaptable cost parameters that resolve the contradiction between adaptability and complexity
2Measurement precision
If the system collects probe data from multiple users to generate accurate cost data, then measurement precision is improved, but data processing time increases
Solution Approach 1:
The system performs data normalization and cost data generation in advance, storing the processed results in a database. When generating routes, it simply retrieves pre-computed cost data rather than processing raw probe data in real-time, thus maintaining high measurement precision while minimizing data processing time
Solution Approach 2:
The system creates normalized copies of probe data that represent average cost characteristics for different trail segments. These copied and normalized data structures enable quick retrieval and application without re-processing the original extensive probe data, resolving the time-accuracy contradiction
3Ease of operation
If the system uses normalized routing cost data for route generation, then route suitability for specific users is improved, but the complexity of data normalization increases
Solution Approach 1:
The system transforms raw probe data into normalized routing cost data by changing parameters such as user ability levels, trail difficulty, and environmental conditions into standardized cost metrics. This parameter transformation enables easy retrieval of suitable routes while the normalization complexity is managed through systematic data processing
Solution Approach 2:
The system introduces normalized routing cost data as an intermediary between raw probe data and route generation. This intermediary layer simplifies the interface between user characteristics and route selection, making the system easier to operate while managing normalization complexity in the background
4Reliability
If the system updates cost data dynamically based on trail conditions, then reliability is improved, but the frequency of data updates increases system load
Solution Approach 1:
The system implements periodic updates of routing cost data based on accumulated probe data from users, rather than continuous real-time updates. This periodic approach maintains reliable and accurate trail condition information while reducing the computational load and energy consumption of the system
Data Source
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AI summary
A method is disclosed involving receiving GPS data from persona portable training devices of users when traversing an off-road segment of an electronic map together with associated data indicative of a heart rate of a user during the movements. The position and heart rate data for each user traversing the segment are processed using data indicative of a fitness profile for the user. The resulting data is used to determine a normalized cost to be associated with the segment, indicative of the difficulty in traversing the segment. The cost data is generated using a neural network. The resulting cost data for different segments in a network of segments is used to generate route suggestions for users based upon desired workout intensity, fitness levels, etc.