18211712. GAZE BEHAVIOR DETECTION simplified abstract (Apple Inc.)
Contents
GAZE BEHAVIOR DETECTION
Organization Name
Inventor(s)
Mehmet N. Agaoglu of Dublin CA (US)
Andrew B. Watson of Los Gatos CA (US)
Tim H. Cornelissen of Mountain View CA (US)
Alexander G. Berardino of San Francisco CA (US)
GAZE BEHAVIOR DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18211712 titled 'GAZE BEHAVIOR DETECTION
Simplified Explanation
The patent application describes devices, systems, and methods for determining a user's gaze behavior state based on physiological data. This includes identifying gaze shifting events, gaze holding events, and loss events.
- The process involves obtaining eye data and head data associated with a user's gaze during a specific period of time.
- Various characteristics of the user's eye movement and head pose are determined and aggregated.
- Machine learning techniques are used to classify the user's eye movement state based on the collected data.
Potential Applications
This technology has potential applications in various fields, including:
- Assistive technology: It can be used to develop devices that assist individuals with limited mobility or communication abilities by tracking their gaze behavior.
- Human-computer interaction: It can improve the interaction between users and computers by allowing for more intuitive control and navigation based on gaze behavior.
- Virtual reality and gaming: It can enhance the immersive experience in virtual reality environments and gaming by accurately tracking and responding to the user's gaze behavior.
Problems Solved
The technology addresses several problems related to understanding and analyzing a user's gaze behavior:
- Accurate identification: It provides a method to accurately identify different gaze behavior states, such as gaze shifting, gaze holding, and loss events.
- Non-invasive monitoring: It allows for the collection of physiological data without the need for invasive methods, such as attaching sensors directly to the user's eyes.
- Real-time analysis: The use of machine learning techniques enables real-time analysis of the user's gaze behavior, allowing for immediate feedback and response.
Benefits
The use of this technology offers several benefits:
- Improved accessibility: It enables individuals with disabilities to interact with technology more effectively and independently.
- Enhanced user experience: It provides a more natural and intuitive way of interacting with computers, virtual reality environments, and gaming systems.
- Personalized applications: The analysis of gaze behavior can be used to personalize user experiences and adapt interfaces based on individual preferences and needs.
Original Abstract Submitted
Various implementations disclosed herein include devices, systems, and methods that determine a gaze behavior state to identify gaze shifting events, gaze holding events, and loss events of a user based on physiological data. For example, an example process may include obtaining eye data associated with a gaze during a first period of time (e.g., eye position and velocity, interpupillary distance, pupil diameters, etc.). The process may further include obtaining head data associated with the gaze during the first period of time (e.g., head position and velocity). The process may further include determining a first gaze behavior state during the first period of time to identify gaze shifting events, gaze holding events, and loss events (e.g., one or more gaze and head pose characteristics may be determined, aggregated, and used to classify the user's eye movement state using machine learning techniques).
