Multimodal Gesture Control via Eye Gaze and Head Tracking
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Solution Overview
Problem
Existing methods for controlling electronic devices often struggle to accurately and efficiently communicate and confirm user intent, particularly in hands-free and voice-free scenarios, across various applications such as accessibility, gaming, and augmented reality, due to limitations in gesture recognition and interpretation.
Innovation Solution
The development of systems and algorithms that utilize a combination of eye gaze steadiness, head motion, facial expressions, and hand gestures to define and interpret user gestures, enabling precise control of electronic devices through the use of Object of Interest (OOI) warping, Post Warp Period (PWP), and Object Modification Drivers (OMDs), with features like steady eye gaze confirmation and multiple OMD actions to enhance user intent confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture recognition systems are used for hands-free control, then ease of operation is improved, but reliability of user intent confirmation deteriorates
Solution Approach 1:
The patent combines multiple gesture recognition methods (eye tracking, head tracking, facial expressions, hand gestures) into a unified control system. By merging these different input modalities, the system maintains hands-free operation capability while improving reliability through cross-validation of user intent across multiple sensors and gesture types.
Solution Approach 2:
The system implements feedback mechanisms where the electronic device provides visual or auditory confirmation of detected gestures and interpreted user intent. This allows users to verify that their gestures were correctly recognized, enabling them to correct misinterpretations and thereby improving overall reliability of intent confirmation.
2Measurement precision
If multiple gesture parameters are combined for control, then measurement precision of user intent is improved, but device complexity increases
Solution Approach 1:
The patent segments the gesture recognition system into distinct functional modules: eye tracking module, head tracking module, facial expression analysis module, and hand gesture recognition module. Each module processes specific gesture parameters independently, then results are integrated to determine overall user intent. This segmentation improves measurement precision while managing complexity through modular design.
Solution Approach 2:
The control system is designed with multi-functional capabilities to handle various types of gestures (eye movements, head movements, facial expressions, hand gestures) through a unified processing framework. This universal approach allows the system to accurately interpret diverse user intents using the same underlying technology platform, improving precision without proportionally increasing complexity.
3Reliability
If steady eye gaze confirmation is required, then reliability of user intent is improved, but loss of time in gesture execution increases
Solution Approach 1:
The system performs preliminary analysis of eye gaze stability and other gesture parameters before finalizing user intent recognition. By pre-assessing whether eye gaze meets stability thresholds and combining this with other gesture data, the system can quickly confirm reliable intent without requiring prolonged steady gaze periods, thus reducing time loss while maintaining high reliability.
Data Source
AI summary
Methods of interpreting user actions for controlling electronic devices, as well as apparatuses and systems implementing the methods. User actions can involve eyes, head, face, fingers, hands, arms, other body parts, as well as facial, verbal and/or mental actions. Hand and/or voice free control of devices. Methods for large as well as fine motion and placement of objects. Use of actions before and during other actions to confirm the intent of the other actions. Objects can be moved or warped based on combinations of actions. Measurement of eye gaze, and iterations and helper signals for improved accuracy of control. Triggers to start and end recognition of user actions and generation of device commands.