Orientation Tracking Prediction Using Motion-Adaptive Rotation Models
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Solution Overview
Problem
Existing orientation tracking methods for audio processing in devices like smartphones and wireless earphones suffer from delays due to the time required for audio processing and data transfer, leading to distracting delays as the user's orientation changes, and current prediction methods are inaccurate and computationally cumbersome.
Innovation Solution
A method for predicting future orientation using a first-order predictor during acceleration and a second-order predictor during deceleration, with a computationally efficient approach that switches between these predictors based on angular velocity thresholds, ensuring accurate orientation tracking without introducing audio artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If future orientation prediction is implemented to reduce delay, then user immersion is improved, but computational complexity increases and may introduce audio artifacts
Solution Approach 1:
The system performs preliminary action by predicting future orientation before the actual orientation change completes, using angular velocity and acceleration data to calculate anticipated head position. This allows audio rendering to be prepared in advance for the predicted orientation, reducing perceptible delay and improving user immersion without requiring complex real-time processing during orientation transitions.
2Measurement precision
If accurate prediction methods are used to reduce delay, then orientation tracking precision is improved, but computational burden increases making it unsuitable for portable devices
Solution Approach 1:
The system applies parameter changes by dynamically adjusting the prediction model based on detected motion states. When linear acceleration exceeds a threshold indicating non-linear motion, the system switches from a simple linear prediction model to a more complex model that accounts for angular velocity and acceleration. This adaptive approach maintains high orientation tracking precision during complex movements while minimizing computational energy consumption during steady-state conditions.
Data Source
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AI summary
The present disclosure relates to a method and system for predicting a future orientation of an orientation tracker (100). The method comprising obtaining a sequence of angular velocity samples, each angular velocity sample indicating an angular velocity at a point in time and obtaining a sequence of angular acceleration samples, each angular acceleration sample indicating an acceleration or deceleration of the angular velocity at each point in time. Wherein said method further comprises determining (S5a), for each point in time where the angular velocity is accelerating, a predicted orientation of the orientation tracker (100) based on a first order prediction of an accumulated rotation of the orientation tracker (100) and determining (S5c), for each point in time where the angular velocity is decelerating, a predicted orientation of the orientation tracker (100) based on a second order prediction of the accumulated rotation of the orientation tracker (100).