Pupillary Response Detection for Gesture-Free Interaction Events
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately determine user intent during interaction with electronic content without requiring physical gestures, limiting the ability to provide tailored and engaging user experiences.
Innovation Solution
The system assesses physiological data, such as gaze characteristics and illumination responses, to predict interaction events by analyzing pupil dilation, constriction, stable gaze direction, and scene-induced pupil response variations, using machine learning techniques to improve interaction event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physiological data collection is implemented to determine user intent, then user interaction accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the physiological data collection process into multiple independent sensor components (eye tracker, pupil response sensor, illumination sensor) that can be individually activated and processed. Each sensor type captures specific aspects of user physiological state, allowing the system to achieve comprehensive intent detection while managing complexity through modular architecture.
Solution Approach 2:
The device integrates multiple sensor types into a unified physiological data collection system that serves various functions: eye tracking for gaze detection, pupil response monitoring for attention assessment, and illumination sensing for environmental context. This multi-functional approach consolidates complexity into a single system while improving overall measurement precision for user intent determination.
2Measurement precision
If multiple sensor types are used to collect physiological data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (eye tracker, pupil sensor, illumination sensor) into a unified physiological data stream that is processed together to determine user intent. By combining these sensors rather than treating them separately, the system achieves more precise gaze characteristics while managing complexity through integrated processing architecture.
Solution Approach 2:
The system introduces an intermediary processing layer that receives raw data from multiple sensor types and transforms it into meaningful physiological measurements. This intermediary layer consolidates the complexity by providing a standardized interface between sensors and the intent detection algorithm, allowing precise measurement without proportionally increasing system complexity.
3Speed
If physiological data is processed in real-time, then interaction response speed is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic sampling of physiological data rather than continuous processing, where sensors collect data at optimized intervals based on user interaction patterns. This periodic action enables real-time response to significant events while reducing overall energy consumption by avoiding constant processing of all sensor data streams.
Solution Approach 2:
The system performs preliminary processing of physiological data locally at the sensor level to filter and pre-process information before it reaches the main processing unit. This preliminary action reduces the amount of data that requires intensive real-time processing, thereby maintaining fast interaction response while lowering overall energy consumption through reduced processing workload.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of user interactions without physical gestures, enhancing user experience through personalized content adjustments and improving accessibility for users with disabilities.
Implementation Method 1
analyzing pupil dilation, constriction, stable gaze direction, and scene-induced pupil response variations
Data Source
Figure 1~2
Figure 3A~3B
Figure 4A~4C
AI summary
Various implementations disclosed herein include devices, systems, and methods that determine an interaction event during presentation of an interaction element. For example, an example process may include obtaining physiological data associated with a pupil during presentation of an interaction element, determining, based on the obtained physiological data, a pupillary response during the presentation of the interaction element, determining that the pupillary response corresponds to attention response characteristics associated with attention of a region of the regions of the interaction element based on the different illumination characteristics of the regions, and determining an interaction event during the presentation of the interaction element based on determining that the pupillary response corresponds to directing attention to the region during the presentation of the interaction element.