Exercise Activity Pattern Prediction for Scheduling
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Solution Overview
Problem
Individuals often find it difficult to schedule time for exercise due to hectic schedules, and existing technologies lack effective methods to predict and recommend suitable exercise opportunities based on historical activity patterns.
Innovation Solution
A system that uses wearable monitoring devices to analyze historical exercise and non-exercise events, generating exercise activity patterns and recommending future exercise events by correlating with scheduled non-exercise activities, thereby suggesting optimal times for exercise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a person schedules exercise activities manually, then they can control their exercise routine, but they struggle to find suitable time slots due to hectic schedules filled with non-exercise activities
Solution Approach 1:
The system performs preliminary analysis of historical exercise events and non-exercise events to pre-identify suitable time slots for future exercise activities. By analyzing patterns in advance, the system proactively suggests exercise opportunities before the user needs to make scheduling decisions, effectively resolving the time allocation problem.
Solution Approach 2:
The system automatically analyzes the user's own historical data (exercise events and non-exercise events from calendar) to generate exercise suggestions without requiring manual input or intervention. The system serves itself by using its own stored data to improve future exercise scheduling recommendations.
2Measurement precision
If the system analyzes detailed historical data to improve prediction accuracy, then exercise recommendations become more precise, but the system complexity increases
Solution Approach 1:
The system extracts only the essential features from historical data that are relevant to exercise scheduling patterns, such as temporal relationships between non-exercise events and exercise events. By focusing on key extracted features rather than processing all raw data, the system achieves accurate predictions while maintaining manageable complexity.
Solution Approach 2:
The analysis process is segmented into distinct functional components: retrieving non-exercise events, retrieving exercise events, generating activity patterns, and suggesting future exercise events. This modular segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining prediction accuracy.
Data Source
AI summary
Methods, apparatuses, and computer readable mediums for exercise behavior prediction are provided. In a particular embodiment, the prediction evaluation controller is configured to generate an exercise activity pattern based on correlations between scheduling of a user's historical non-exercise events and the user's historical exercise events. In the particular embodiment, the prediction evaluation controller is also configured to generate, based on the generated exercise activity pattern, by the prediction evaluation controller, a future exercise event to correspond with a future non-exercise event scheduled on the user's calendar. In the particular embodiment, the prediction evaluation controller is also configured to provide an indication of the generated future exercise event.