Movement Skill Augmentation via Phase Segmentation
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Solution Overview
Problem
Current systems for analyzing and improving human movement skills lack real-time feedback and are ineffective in providing actionable information for training, as they primarily focus on outcome variables rather than the complex, dynamic nature of movement, which is highly variable and influenced by individual differences in skill level, body type, and physical conditions.
Innovation Solution
A human movement augmentation system that decomposes movement into elements following the natural structure of the human nervous system, using wearable sensors and vision-based tracking to provide real-time and post-performance feedback through visual, haptic, and audio cues, enabling systematic improvement of movement skills and injury prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If movement analysis systems focus on outcome variables, then measurement is simplified, but actionable feedback for training is lost
Solution Approach 1:
The patent segments complex movement into discrete phases (e.g., wind-up, strike, follow-through) and identifies key events within each phase. This segmentation allows the system to provide actionable feedback about specific movement components rather than just overall outcome variables, resolving the contradiction by making complex measurement manageable through structured breakdown.
Solution Approach 2:
The system uses computer vision technology and machine learning models as intermediaries to bridge raw sensor data and actionable feedback. These intermediaries process complex movement data and translate it into meaningful training insights, maintaining measurement simplicity while preserving actionable information through intelligent data transformation.
2Productivity
If real-time feedback is provided during fast movements, then training effectiveness improves, but measurement and processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining movement phases and key events based on domain knowledge before actual measurement. This preparation allows real-time feedback during fast movements without increasing processing complexity, as the framework for analysis is already established and ready to capture relevant data points.
Solution Approach 2:
The patent replaces complex mechanical measurement systems with computer vision and machine learning-based approaches. This substitution enables real-time analysis of fast movements through software-based processing rather than complex hardware, reducing physical system complexity while maintaining training effectiveness.
3Measurement precision
If comprehensive movement data is collected, then analysis accuracy improves, but feedback delivery timeliness decreases
Solution Approach 1:
The system extracts only the most relevant features and key events from comprehensive movement data rather than processing all available information. This extraction approach maintains analysis accuracy for critical aspects while reducing processing time, enabling timely feedback delivery without sacrificing essential measurement precision.
Data Source
AI summary
A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.