Exercise Animation Parameter Estimation via Correlated Sensor Data
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Solution Overview
Problem
Existing wearable devices and motion sensors struggle to accurately represent the exercise state of users through animations, as they lack the ability to effectively estimate changes in exercise parameters based on correlated data, leading to incomplete and unrealistic motion representations.
Innovation Solution
An information processing device that acquires and processes exercise data from wearable sensors to generate animations by deriving reference values from a model based on sets of exercise parameters, allowing for the estimation of parameter changes and the creation of realistic motion representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices and motion sensors are used to measure exercise indexes, then exercise state data can be obtained, but the ability to accurately represent exercise state through animations is insufficient
Solution Approach 1:
The patent introduces a model as an intermediary between the measured exercise indexes and the animation representation. The model derives reference values for exercise parameters by processing the raw sensor data, enabling accurate animation generation. This intermediary layer transforms incomplete sensor measurements into reliable parameter estimates that accurately represent the user's exercise state.
Solution Approach 2:
The system uses feedback from multiple correlated exercise indexes to continuously refine the estimation of exercise parameters. By monitoring changes in one parameter and using the model to predict corresponding changes in other parameters, the system maintains accurate animation representation even when direct measurement is unavailable or incomplete.
2Ease of operation
If manual input of parameter values is required, then animation can be changed, but the system lacks the ability to automatically estimate parameter changes
Solution Approach 1:
The system performs self-service by automatically estimating exercise parameters using the model and correlated sensor data. Instead of requiring manual input, the system autonomously derives parameter values and detects changes, then uses these estimated values to generate and update animations. This eliminates the need for manual parameter input while maintaining ease of operation through automated processes.
3Device complexity
If only single parameter data is used, then data acquisition is simple, but the correlation between exercise parameters cannot be utilized for better estimation
Solution Approach 1:
The patent merges multiple exercise parameter data from different sensors into a unified model. By combining correlated parameters (such as combining acceleration data with heart rate data to estimate exercise intensity), the system achieves higher estimation precision. The model integrates these multiple data sources to derive reference values that accurately represent the exercise state, utilizing the correlations between different parameters.
Data Source
AI summary
An information processing device including a memory that stores a program; and a processor that executes the program. The processor is configured to acquire, from exercise data representing an exercise state of a subject, exercise parameter information including a plurality of parameters that represent the exercise state of the subject and have a correlation with each other. When an animation representing a motion of the subject based on the acquired exercise parameter information is displayed and then an operation for changing a value of a first parameter of the plurality of parameters is received, the processor generates an animation reflecting at least the first parameter for which the value is changed and a second parameter of the plurality of parameters, a value of the second parameter being changed in conjunction with the value of the first parameter.