AR Eyewear Motion Evaluation With IMU-Based Posture Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack effective methods for analyzing and providing real-time feedback on physical exercises in augmented reality environments, particularly for wearable devices like eyewear, which are limited in their ability to accurately track user movements and provide immersive, interactive experiences.
Innovation Solution
The implementation of advanced AR technologies, including computer vision and object tracking, combined with inertial measurement units (IMUs) and multiple cameras, allows for precise localization and tracking of user movements, enabling the presentation of virtual targets and real-time feedback through graphical user interfaces on wearable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced AR technologies including computer vision and object tracking are implemented, then measurement precision of user movements is improved, but device complexity increases
Solution Approach 1:
The system divides movement analysis into multiple components: computer vision for visual tracking, IMUs for inertial measurement, and separate processing modules for each. This segmentation allows each component to specialize in specific tracking aspects, improving overall measurement precision while distributing computational complexity across modular units rather than a monolithic system.
Solution Approach 2:
The wearable device integrates multiple functions into a single platform: camera systems for visual tracking, IMUs for motion sensing, processors for real-time analysis, and display components for feedback. This multi-functionality consolidates what would otherwise require separate devices, improving measurement capabilities without proportionally increasing system complexity.
2Reliability
If multiple cameras and IMUs are integrated for precise tracking, then reliability of exercise analysis is improved, but device complexity increases
Solution Approach 1:
The system merges multiple sensing modalities (camera vision and IMU inertial measurement) into a unified tracking framework. By combining data from cameras and IMUs through sensor fusion algorithms, the system achieves higher reliability in exercise analysis than either modality could provide alone, while presenting a unified interface that masks the underlying hardware complexity.
Solution Approach 2:
The system introduces intermediate processing layers including sensor fusion algorithms and machine learning models that mediate between raw sensor data from multiple cameras and IMUs and the final exercise analysis. These intermediaries integrate data from multiple sources, improving reliability by cross-validating measurements while abstracting the complexity of coordinating multiple sensors.
3Ease of operation
If real-time feedback is provided through graphical user interfaces, then user engagement is improved, but use of energy increases
Solution Approach 1:
The system provides feedback through periodic updates rather than continuous display refreshes. The graphical user interface updates at optimized intervals based on exercise phase and user performance milestones, maintaining user engagement through timely feedback while reducing display power consumption compared to continuous high-rate updates.
Solution Approach 2:
The system applies feedback selectively based on exercise context: providing detailed graphical feedback during critical exercise phases where user correction is most beneficial, and reducing feedback intensity or frequency during stable performance periods. This partial application of feedback maintains engagement where needed while conserving energy during less critical moments.
Data Source
AI summary
Example systems, devices, media, and methods are described for evaluating movements and physical exercises in augmented reality using the display of an eyewear device. A motion evaluation application implements and controls the capturing of frames of motion data using an inertial measurement unit (IMU) on the eyewear device. The method includes presenting virtual targets on the display, localizing the current eyewear device location based on the captured motion data, and presenting virtual indicators on the display. The virtual targets represent goals or benchmarks for the user to achieve using body postures. The method includes detecting determining whether the eyewear device location represents an intersecting posture relative to the virtual targets, based on the IMU data. The virtual indicators display real-time feedback about user posture or performance relative to the virtual targets.