Fitness Simulator Using Feature Point Clustering for Motion Prediction
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Solution Overview
Problem
Conventional virtual reality simulators using fitness devices, such as treadmills, fail to provide a stable and immersive experience due to their reliance on only current exercise speed, neglecting the continuity of user motion, which limits effective motion recognition and immersion.
Innovation Solution
A fitness device-based simulator that extracts feature points from camera sensors to generate clusters, predicts subsequent exercise states using Markov models, and controls the fitness device operation based on these predictions, ensuring stable and immersive virtual reality experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the treadmill control system uses only current exercise speed information, then the control system is simple, but the user motion recognition is inaccurate and the virtual reality experience is not immersive
Solution Approach 1:
The system performs preliminary actions by extracting feature points from user motion in advance, clustering them into exercise patterns, and storing these patterns before prediction is needed. This preparation enables accurate real-time prediction without increasing real-time processing complexity
Solution Approach 2:
The system implements feedback by using predicted exercise patterns to continuously adjust and control treadmill operation. The prediction results feed back into the control system, creating a closed-loop control that improves motion recognition accuracy through iterative optimization
Solution Approach 3:
The system applies dynamics by transitioning from static speed measurement to dynamic exercise pattern prediction. It captures the temporal evolution of user motion through feature point sequences and uses this dynamic information to predict future exercise states, improving recognition accuracy
2Reliability
If the treadmill control system only analyzes current exercise speed, then the system response is fast, but the user's continuous motion is not effectively recognized and the operation is unstable
Solution Approach 1:
The system performs preliminary clustering of feature points into exercise patterns during the user's exercise session, storing these patterns for future prediction. This advance preparation ensures reliable operation without requiring extensive real-time processing
Solution Approach 2:
The system maintains continuity by tracking feature point sequences over time and using exercise pattern transitions to predict continuous user motion. This continuous tracking approach improves operational stability by considering the temporal evolution of exercise behavior rather than isolated speed measurements
3Adaptability or versatility
If the system uses feature point clustering and exercise pattern prediction, then the virtual reality immersion is improved, but the device complexity increases
Solution Approach 1:
The system segments the complex task of motion recognition into distinct modules: feature point extraction, clustering into exercise patterns, state transition prediction, and control signal generation. This segmentation manages complexity by breaking down the overall system into manageable, specialized components
Solution Approach 2:
The system introduces exercise patterns as an intermediary representation between raw feature points and control decisions. These patterns serve as a simplified intermediate model that captures essential exercise behavior without requiring direct complex processing of raw sensor data, thereby improving VR immersion while managing system complexity
Data Source
AI summary
A fitness device-based simulator and a simulation method using the simulator. The fitness device-based simulator includes a feature point extraction unit for acquiring action-sensing information of a user who is located on a fitness device, and extracting feature points for a body skeletal structure of the user based on the action-sensing information, a feature point cluster generation unit for generating multiple feature point clusters by clustering two or more of the feature points, and setting respective cluster symbols for multiple feature point clusters, an exercise pattern information accumulation unit for generating and storing information about a state transition between the multiple feature point clusters of the user, and an exercise state prediction unit for predicting a subsequent exercise state of the user by predicting a feature point cluster subsequent to a feature point cluster currently being generated for the user, based on state transition information.