Adaptive Exercise Model for Fair Virtual Competition
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Solution Overview
Problem
Existing exercise information measurement systems do not account for users starting exercise modes at different times, leading to unfair competition as virtual runners move regardless of start time, failing to consider daily activities and varying user schedules.
Innovation Solution
An exercise information measurement apparatus that generates exercise information based on bodily motion detection, stores exercise model data with time segment increments, calculates integration values, and displays user progress against a virtual agent's progress, allowing users to compete while performing daily activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the virtual runner moves according to time elapsed from exercise start, then the exercise model can be displayed, but the competition becomes unfair when users start at different times
Solution Approach 1:
The virtual runner's movement pattern is made dynamic by adjusting it according to the actual start time. Instead of using a fixed time-based progression, the system selects or modifies exercise model data to match the user's actual daily activity patterns, making the virtual runner's behavior adaptable to different starting conditions while maintaining competition fairness.
Solution Approach 2:
The system changes the parameters of the virtual runner's movement by selecting exercise model data that corresponds to different time of day patterns. When a user starts late at night, the system adjusts the virtual runner's expected movement pattern to reflect typical nighttime inactivity, thereby maintaining fair competition conditions across different start times.
2Adaptability or versatility
If the virtual runner follows fixed exercise model data, then the system is simple to operate, but it does not reflect user's actual daily activities
Solution Approach 1:
The exercise model data is segmented into time segments (e.g., morning, afternoon, evening, nighttime) with different activity patterns. This segmentation allows the system to select appropriate segments based on the user's start time, making the system adaptable to daily activities while keeping the data structure organized and manageable.
Solution Approach 2:
The system pre-prepares multiple exercise model data sets corresponding to different time segments and daily activity patterns. By having these models ready in advance, the system can quickly select the appropriate model when the user starts exercise, avoiding complex real-time calculations while still reflecting actual daily activities.
3Ease of operation
If exercise mode starts late at night, then users can exercise anytime, but the virtual runner continues moving creating unfair conditions
Solution Approach 1:
When the user starts exercise late at night, the system changes the parameters of the virtual runner's movement pattern to reflect nighttime conditions. The virtual runner's expected activity level is reduced or paused during nighttime segments, creating equal competition conditions regardless of when the user starts, while still allowing flexible exercise timing.
Data Source
AI summary
An exercise information measurement apparatus including: an exercise information generation unit that generates exercise information based on an output of a bodily motion detection unit that detects a bodily motion; a storage unit that stores exercise model data in which an increment of the exercise information is associated with each time segment obtained by dividing a pre-set time period (24 hours); an integration value calculation unit that calculates an integration value of the increment of the exercise information corresponding to a period of time from a particular time to a current time in the exercise model data; and a display control unit that causes a display unit to display the exercise information generated by the exercise information generation unit during the period of time from the particular time to the current time and the integration value.