Neuromuscular Sensors for Precise Arm and Wrist Tracking
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Solution Overview
Problem
Existing artificial-reality systems inadequately track and respond to user movements, leading to inaccuracies in the representation of user interactions within the artificial-reality environment.
Innovation Solution
Employing neuromuscular-signal sensors in conjunction with inertial measurement units (IMUs) to accurately track arm, wrist, and hand movements, enabling precise detection and representation of user interactions in the artificial-reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cameras are used to track user movements, then the system can detect user movements, but the tracking accuracy and precision are insufficient
Solution Approach 1:
The patent combines multiple sensing modalities (cameras, IMUs, neuromuscular sensors) into an integrated tracking system. The camera provides visual tracking while IMUs provide inertial data, and neuromuscular sensors detect muscle activation patterns. This multi-sensor fusion approach resolves the contradiction by merging complementary measurement methods to achieve both high precision and reliability in movement tracking.
Solution Approach 2:
The patent introduces neuromuscular sensors as an intermediary between the user's intent and the virtual object interaction. These sensors detect muscle signals before the actual movement occurs, providing early indication of user intent. This intermediary measurement layer enhances both tracking precision and the accuracy of representing user interactions by capturing physiological signals that precede and inform voluntary movements.
2Measurement precision
If multiple sensors are used to improve tracking precision, then movement detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the sensing system into distinct functional modules: camera subsystem for visual tracking, IMU subsystem for inertial measurement, and neuromuscular sensor subsystem for muscle signal detection. Each module operates independently with specialized processing, allowing the complex multi-sensor system to be managed through modular architecture. This segmentation resolves the contradiction by organizing complexity into manageable, independent components that can be processed separately before integration.
Solution Approach 2:
The patent implements a universal processing framework that handles data from multiple sensor types through a common architecture. The system uses unified coordinate systems, standardized data fusion algorithms, and integrated processing pipelines that can accommodate different sensor modalities. This multi-functional approach reduces overall system complexity by providing a universal interface and processing method rather than requiring separate specialized systems for each sensor type.
Data Source
AI summary
The various implementations described herein include methods and systems for tracking user movements. In one aspect, a method includes, while a user is interacting with a virtual object, obtaining tracking information by tracking, via a sensor, a position of an arm of a user. The method also includes, in conjunction with tracking the position of the arm, obtaining wrist information by detecting, via a neuromuscular-signal sensor, a movement of a wrist of the arm. The method further includes assigning one or more motion characteristics to the virtual object in accordance with the tracking information and the wrist information.