AR Motion Tracking via Sensor Calibration and Shadow Object Mirroring
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Solution Overview
Problem
Current augmented reality (AR) systems lack effective methods for accurately tracking and monitoring object motion in real-time, particularly in environments requiring precise physical therapy or exercise guidance, where user movement needs to be calibrated and compared to predefined sequences.
Innovation Solution
A sensor-based system is deployed to capture and analyze user motion data, integrating image data with a shadow object and tutor guide to provide real-time feedback, using sensors like accelerometers and gyroscopes to monitor position, angle, and acceleration, and adjust movement sequences based on user performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based tracking is implemented in AR environments, then motion tracking precision is improved, but system complexity increases
Solution Approach 1:
The system divides the tracking task into multiple independent sensor components (accelerometers, gyroscopes, magnetometers) distributed across multiple devices. Each sensor captures specific motion parameters, and the results are aggregated to form comprehensive motion tracking, reducing the complexity of any single component while improving overall precision.
Solution Approach 2:
The AR glasses and mobile devices serve multiple functions: they display augmented reality content, capture motion data through integrated sensors, process tracking information, and provide feedback to users. This multi-functionality reduces the need for separate dedicated tracking equipment, thereby managing system complexity while maintaining high measurement precision.
2Measurement precision
If real-time motion monitoring is implemented, then feedback accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary calibration and setup procedures before actual motion monitoring begins. Sensors are calibrated to establish baseline parameters, and motion sequences are pre-defined and stored in memory. During real-time operation, the system only needs to compare current sensor data against these pre-established parameters, significantly reducing processing time while maintaining high feedback accuracy.
Solution Approach 2:
The system continuously processes sensor data streams without interruption, maintaining constant tracking and feedback loops. By keeping the processing pipeline continuously active rather than batch-processing data in discrete intervals, the system minimizes latency and ensures real-time responsiveness, balancing processing time with feedback accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system enables precise tracking and feedback on user movement, ensuring accurate alignment and performance of exercises, enhancing the effectiveness of physical therapy and exercise guidance in AR environments.
Implementation Method 1
using sensors like accelerometers and gyroscopes to monitor position, angle, and acceleration
Implementation Method 2
using sensors like accelerometers and gyroscopes to monitor position, angle, and acceleration
Implementation Method 3
sensors deployed to at least one target can be calibrated. Sensor data may be received from the one or more sensors
Implementation Method 4
sensors deployed to at least one target can be calibrated. Sensor data may be received from the one or more sensors
Data Source
AI summary
Techniques are disclosed for capturing and monitoring object motion in an AR environment. One or more sensors deployed to at least one target can be calibrated. Sensor data may be received from the one or more sensors, and the image data can be augmented with a shadow object and a training object to generate augmented image data, the shadow object mirroring the motion of the at least one target. The training object can be caused to perform at least one movement sequence. The motion of the target can be monitored based on the sensor data, and compared to the at least one movement sequence. Based on the comparison, at least one sensor can be caused to provide feedback to the target through the at least one sensor.