Predictive Motion Rendering for Real-Time Feedback Latency
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Solution Overview
Problem
Current systems for real-time human movement analysis and training lack accuracy and flexibility, particularly in providing detailed visual feedback, which is essential for efficient movement technique development and improvement.
Innovation Solution
The implementation of computational systems and networking with display hardware to predict and render human movement, utilizing depth sensors and statistical inference to generate accurate motion models, allowing for real-time synchronization with expert models and enhanced training exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational prediction systems are used to generate real-time visual feedback, then measurement precision and reliability of movement tracking improve, but device complexity increases
Solution Approach 1:
The system pre-captures expert movement data and builds predictive models before real-time training sessions. This preliminary preparation allows the complex computational work to be done in advance, reducing real-time processing requirements while maintaining high measurement precision through pre-established reference models
Solution Approach 2:
The patent introduces a computational prediction system as an intermediary between depth sensors and visual feedback displays. This mediator processes sensor data through statistical inference and predictive modeling to generate accurate movement representations, separating the complexity of analysis from the simplicity of data collection and display
2Loss of time
If real-time predictive rendering is implemented, then loss of time in feedback delivery is reduced, but use of energy increases
Solution Approach 1:
The system performs preliminary capture and processing of expert movement data to create predictive models before real-time sessions. This advance preparation enables rapid real-time prediction with reduced computational energy requirements, as the heavy lifting of model creation occurs beforehand
Solution Approach 2:
The system uses partial action by leveraging pre-captured expert data for specific movement patterns rather than continuously capturing and processing all movement data in real-time. This selective approach reduces real-time energy consumption while maintaining low latency through intelligent use of pre-processed information
3Manufacturing precision
If detailed visual feedback is provided through computational rendering, then manufacturing precision of movement technique development improves, but device complexity increases
Solution Approach 1:
The system creates accurate visual copies of expert movement patterns through computational rendering. By capturing expert movements and generating precise visual replicas that users can compare against their own movements, the system achieves high movement technique accuracy without requiring complex physical analysis equipment
Solution Approach 2:
The computational rendering system acts as an intermediary that transforms depth sensor data into detailed visual representations. This mediator layer handles the complexity of converting raw sensor data into precise movement visualizations, isolating the complexity from the core tracking function
Data Source
AI summary
A system and method may be used for reference model predictive tracking and rendering. Systems and methods may use computational systems, networking, or display hardware to seamlessly allow users to see their motion in real-time. The systems and methods may provide the brain the extra visual information to help a user converge to highly efficient high-quality technique (e.g., movement control) more rapidly than other processes. An approach to generating accurate models in delayed processing may include treating depth-sensor-derived skeletal inference of body position as statistical data about the underlying motion rather than a representation of that motion itself. In an example, a process for slicing up a time-series of body constructions in a motion model may use a full time series of positions for individual body segments, creating trajectories.