Exercise Machine Rankings With Normalized Live Performance Projection
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Solution Overview
Problem
Existing exercise machines lack the ability to effectively compare and present real-time and historical performance data to competitors in a way that accurately predicts their relative rankings during and at the end of an exercise activity, especially in on-demand modes where historical performance data is used to create virtual competitions.
Innovation Solution
A processor computes a normalized performance metric for a subject competitor using data from a lesser scope of an exercise activity and combines it with historical performance data to predict a hypothetical performance over a predefined scope, generating a rank listing that includes both live and virtual competitors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If performance data from a lesser scope is used to compute performance metrics during an exercise activity, then real-time feedback is provided to competitors, but the accuracy of predicting final performance relative to historical competitors deteriorates
Solution Approach 1:
The system performs preliminary normalization of performance metrics during the exercise activity, adjusting lesser scope performance data to predict hypothetical full scope performance. This preliminary action enables real-time accuracy by pre-computing normalized values that account for the proportion of completion, rather than waiting until the activity ends to make comparisons.
Solution Approach 2:
The system changes the parameter representation by normalizing performance metrics based on the proportion of the predefined scope completed. Instead of using raw performance data from a lesser scope, the system transforms it into a normalized metric that reflects what the performance would be if the full scope were completed at the current rate, thereby enabling accurate real-time comparisons with historical full-scope performances.
2Ease of operation
If historical performance data is used to create virtual competitions in on-demand mode, then competitors can engage at convenient times, but the ability to provide accurate real-time ranking comparison deteriorates
Solution Approach 1:
The system applies parameter transformation by normalizing both historical and current performance metrics to a common basis (full predefined scope). Historical performance data is adjusted to the same scope as the current activity, and real-time performance is normalized to predict full-scope outcomes. This parameter alignment enables accurate ranking comparisons despite the time flexibility of on-demand participation.
Solution Approach 2:
The system creates a normalized copy of historical performance data that matches the scope and parameters of the current exercise activity. By copying and adapting historical full-scope performances to the current predefined scope, the system enables fair comparison between live and virtual competitors while maintaining the ease of on-demand participation.
3Productivity
If performance metrics are computed continuously during exercise activity, then dynamic rank listings are provided to enhance competitive experience, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential elements needed for ranking comparison: performance metrics and scope proportion. Instead of processing all raw sensor data continuously, it extracts the key performance parameter and the proportion of scope completed, then computes the normalized metric. This extraction approach reduces computational complexity while maintaining the ability to provide dynamic real-time rankings.
Solution Approach 2:
The system simplifies continuous data processing by transforming multiple performance parameters into a single normalized performance metric that directly enables ranking. By changing the parameter representation to a normalized value based on scope proportion, the system reduces the complexity of continuous computation while providing accurate dynamic rankings throughout the exercise activity.
Data Source
AI summary
Among other things, a processor executes instructions to: update, in real time during a current instance of an exercise activity by a subject competitor, a graphical user interface displayed on a display device, the display device being included in a first exercise machine operated by the subject competitor or included in a mobile electronic device. The graphical user interface includes: a ranking of the subject competitor and a second competitor based on (i) a projected performance metric of the subject competitor over a predefined scope of the exercise activity compared to (ii) a historical performance metric of the second competitor over the predefined scope of the exercise activity in a previous instance of the exercise activity, an illustration of a margin between the historical performance metric of the second competitor and the projected performance metric for the subject competitor, and an illustration of the projected performance metric.