Head Tracking via ML-Processed Seat Sensors for 3D Audio
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Solution Overview
Problem
Conventional head tracking methods for three-dimensional audio rendering are expensive and suffer from high latency, which impede the performance of immersive audio systems, especially when accounting for the position and orientation of a user's head in a seated environment.
Innovation Solution
A system utilizing a plurality of inexpensive sensors distributed around a seat, whose outputs are fed into a machine learning model, allowing the model to predict head position and orientation parameters without the need for a motion tracking device, thereby reducing costs and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video-based or camera-based head tracking is used, then head position and orientation information can be obtained, but the system becomes expensive and experiences high latency
Solution Approach 1:
The patent replaces conventional video-based or camera-based head tracking systems with a machine learning model that processes data from inexpensive sensors (accelerometers, gyroscopes, magnetometers). This substitution eliminates the need for complex optical tracking infrastructure while achieving comparable or superior tracking performance with reduced latency and lower cost.
Solution Approach 2:
The patent transforms the approach by changing from direct optical measurement to indirect sensing through motion sensors combined with machine learning. The system collects raw sensor data from multiple axes, processes it through a trained ML model, and derives head position and orientation parameters, thereby achieving accurate tracking through parameter transformation rather than direct measurement.
2Measurement precision
If conventional video-based or camera-based head tracking is used, then head position and orientation information can be obtained, but the system cost increases significantly
Solution Approach 1:
The patent replaces expensive video-based or camera-based head tracking systems with a machine learning model that processes data from inexpensive sensors (accelerometers, gyroscopes, magnetometers). This substitution eliminates the need for complex optical tracking infrastructure while achieving comparable or superior tracking performance with reduced latency and lower cost.
Solution Approach 2:
The patent employs inexpensive sensor modules (accelerometers, gyroscopes, magnetometers) that can be easily manufactured and deployed compared to expensive camera-based systems. These low-cost sensors provide sufficient data for accurate head tracking when processed through the machine learning model, making the overall system more cost-effective.
3Measurement precision
If motion tracking device is used for training, then accurate position and orientation parameters can be obtained, but the expense and latency increase
Solution Approach 1:
The patent implements a two-phase approach: during the training phase, expensive motion tracking devices are used to collect ground truth data for training the machine learning model. Once trained, the model can operate independently using inexpensive sensors, eliminating the need for continuous use of expensive tracking devices. This preliminary action allows the system to achieve high accuracy without ongoing high costs.
Solution Approach 2:
The patent creates a virtual copy of the expensive motion tracking device's functionality through a machine learning model trained on its data. The model learns to replicate the tracking accuracy of the expensive device while using inexpensive sensors during operation, effectively copying the performance characteristics without the associated cost and latency.
Data Source
AI summary
Mechanisms and methods are provided for improved head tracking for three-dimensional audio rendering. In some embodiments, methods may comprise obtaining sensor outputs from a plurality of sensors at fixed positions on a portion of a seat (e.g., a headrest of the seat). The sensor outputs may be provided to a machine learning model, which may be trained to predict parameters related to a position and/or an orientation of a head of a user of the seat based on those sensor outputs as well as corresponding position and/or orientation parameters from a motion tracking device used during training. The machine learning model may in turn provide a set of translation and quaternion parameter predictions to an audio system for improved rendering of three-dimensional audio signaling for the headrest.