Head Pose Tracking Error Reset via Posture Change Detection
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Solution Overview
Problem
Existing spatial audio platforms face challenges in maintaining accurate head pose tracking when the source device and headset move relative to each other, leading to impaired user experience due to persistent tracking errors.
Innovation Solution
A method that involves obtaining motion data from both the source device and headset, estimating a gravity vector, splitting the data into vertical and horizontal planes, calculating similarity measures, detecting user posture change events, and resetting head tracking errors to maintain accurate spatial audio alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the source device and headset are free to move relative to each other, then user mobility and comfort are improved, but head pose tracking accuracy deteriorates
Solution Approach 1:
The system continuously monitors the relative motion between source device and headset, detects when posture changes occur, and feeds back correction signals to reset tracking errors. This closed-loop feedback mechanism maintains tracking accuracy despite device movement, resolving the contradiction between mobility and precision.
Solution Approach 2:
The system proactively detects user posture changes before they significantly degrade tracking accuracy by monitoring motion data continuously. When posture changes are detected, the system preemptively resets tracking errors, preventing accuracy deterioration before it occurs.
2Measurement precision
If head pose tracking is continuously monitored, then tracking accuracy is maintained, but system complexity increases
Solution Approach 1:
The system dynamically adjusts its operation mode based on detected user behavior. During normal use, it operates in a low-power monitoring mode, and only activates full tracking correction when posture changes are detected. This dynamic adaptation maintains accuracy while reducing average system complexity and power consumption.
Solution Approach 2:
The system uses the motion data already being collected for other purposes (spatial audio rendering) to detect posture changes, rather than requiring separate dedicated sensors or systems. This self-service approach maintains tracking accuracy using existing infrastructure, minimizing additional system complexity.
3Measurement precision
If similarity measures are calculated over longer time windows, then posture change detection accuracy is improved, but response time to posture changes deteriorates
Solution Approach 1:
The system calculates similarity measures over multiple time windows with different durations simultaneously. Shorter windows provide rapid response to posture changes, while longer windows provide more accurate detection. By using partial information from multiple windows, the system achieves both fast response and high accuracy.
Data Source
AI summary
Embodiments are disclosed for user posture change detection for triggering re-centering of spatial audio. In an embodiment, a method comprises: obtaining source device motion data from a source device and headset motion data from a headset worn by a user; estimating a gravity vector from one of the source device or headset motion data; splitting the source device and headset motion data into vertical and horizontal planes, the vertical plane in the direction of the estimated gravity vector and the horizontal plane perpendicular to the estimated gravity vector; calculating similarity measures based on the source device motion data and headset motion data in the vertical and horizontal planes over a time window; detecting a posture change event based on the calculated similarity measures; and resetting a head tracker error after the detected user posture change event.