Real-Time Motion Profile Analysis for Feedback Delay
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Solution Overview
Problem
Existing data acquisition systems lack the capability to provide real-time feedback and track changes over time, failing to account for multiple variables and trends in user motion data, which limits their effectiveness in providing immediate and clinically relevant feedback for improving physical movements.
Innovation Solution
The system employs real-time sensor data from motion sensors to calculate and compare multi-dimensional motion profiles against templates, providing immediate feedback through visual, tactile, or auditory signals, and allowing for the aggregation and analysis of data over time to adapt templates based on user progress and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time sensor data is collected and processed to provide immediate feedback, then feedback timeliness is improved, but system complexity increases
Solution Approach 1:
The system divides feedback delivery into two segments: immediate visual feedback displayed on a screen during the motion, and tactile feedback delivered later through a haptic device. This segmentation allows the system to provide timely feedback without requiring complex real-time haptic control, thus reducing overall system complexity while maintaining feedback timeliness.
Solution Approach 2:
The system performs preliminary data collection and analysis during the user's motion, preparing the feedback information in advance. The visual feedback is generated and displayed immediately, while tactile feedback is prepared and delivered right after the motion completes. This preliminary action ensures minimal delay without requiring complex real-time processing during the motion itself.
2Measurement precision
If multiple sensor data streams are integrated and analyzed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments data processing into distinct phases: data collection during motion, preliminary analysis to generate visual feedback, and detailed analysis after motion for tactile feedback. This segmentation allows multiple sensor streams to be integrated without overwhelming real-time processing requirements, maintaining measurement precision while managing complexity.
Solution Approach 2:
The system uses an intermediary computational layer that aggregates and correlates data from multiple sensors (accelerometers, gyroscopes, magnetometers) before generating feedback. This intermediary processing layer simplifies the integration of multiple data streams by normalizing and correlating them, improving measurement precision without proportionally increasing device complexity.
Data Source
AI summary
This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Disclosed embodiments may receive real-time sensor data from a motion sensor or sensors mounted on a user and/or equipment while a user performs a test motion. Disclosed embodiments may also calculate a test motion profile based on the real-time sensor data, the test motion profile describing a multi-dimensional representation of the test motion performed by the user or computed motion profiles. Disclosed embodiments may include comparing the test motion profile to a template motion profile to determine a deviation amount for the test motion profile indicating how the test motion deviated from the template motion profile. Still further embodiments may correlate test motion profiles over time with health indicators.