System and method for generating a map from activity data
First Claim
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1. A method for generating a graph, the method comprising:
- receiving GPS data points for a plurality of GPS tracks;
simplifying the GPS tracks to provide GPS data for simplified GPS tracks, wherein simplifying the GPS tracks includes reducing the GPS data points for each of the GPS tracks by applying an iterative end-point fit algorithm to said GPS track, the iterative end-point fit algorithm using an error parameter that is dependent upon a sport associated with said GPS track, and then identifying points of greater curvature in the GPS tracks;
clustering the identified points of greater curvature;
determining a plurality of nodes for the graph based on the clustered identified points of greater curvature in the GPS tracks; and
determining a plurality of connections between the nodes, the nodes and the connections defining the graph.
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Abstract
A method for generating a graph includes receiving GPS data points for a plurality of GPS tracks. Thereafter, points of greater curvature in the GPS tracks are identified. The method further includes determining a plurality of nodes for the graph based on the identified points of greater curvature in the GPS tracks. Additionally, the method includes determining a plurality of connections between the nodes, the plurality of nodes and the plurality of connections defining the graph.
8 Citations
18 Claims
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1. A method for generating a graph, the method comprising:
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receiving GPS data points for a plurality of GPS tracks; simplifying the GPS tracks to provide GPS data for simplified GPS tracks, wherein simplifying the GPS tracks includes reducing the GPS data points for each of the GPS tracks by applying an iterative end-point fit algorithm to said GPS track, the iterative end-point fit algorithm using an error parameter that is dependent upon a sport associated with said GPS track, and then identifying points of greater curvature in the GPS tracks; clustering the identified points of greater curvature; determining a plurality of nodes for the graph based on the clustered identified points of greater curvature in the GPS tracks; and determining a plurality of connections between the nodes, the nodes and the connections defining the graph. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer readable medium containing instructions for generating a graph by:
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receiving GPS data points for a plurality of GPS tracks; simplifying the GPS tracks by reducing the GPS data points for each of the GPS tracks by applying an iterative end-point fit algorithm to said GPS track, the iterative end-point fit algorithm using an error parameter that is dependent upon a sport associated with said GPS track; identifying points of greater curvature in the GPS tracks; clustering the identified points of greater curvature; determining a plurality of nodes for the graph based on the clustered identified points of greater curvature in the GPS tracks; and determining a plurality of connections between the nodes, the nodes and the connections defining the graph. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method for generating a graph, the method comprising:
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receiving GPS data points for a plurality of GPS tracks, the GPS data points for the GPS tracks received from a plurality of GPS-enabled devices during fitness activities; identifying points of greater curvature for each of the plurality of GPS tracks and simplifying the GPS tracks to the identified points of greater curvature, wherein simplifying the GPS tracks to the identified points of greater curvature comprises (i) defining an error parameter, (ii) defining a line extending between two points of a subset of GPS data points for each of the GPS tracks, (iii) determining a point of the subset that is a farthest distance from the defined line, and (iv) maintaining the determined point as a point of greater curvature if the farthest distance is greater than the defined error parameter; clustering the GPS data points identified as the points of greater curvature; determining a plurality of nodes for the graph based on the clustered GPS data points; determining a sequence of closest nodes for a plurality of GPS data points for each of the GPS tracks; reducing the sequence of closest nodes for each of the GPS tracks by removing redundant nodes from the sequence; and determining a plurality of connections between the nodes based on the reduced sequence of closest nodes for each of the GPS tracks. - View Dependent Claims (18)
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Specification