Wearable Bio-sensing System with Eye-tracking and Multi-modal Sensors
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Solution Overview
Problem
Traditional bio-sensing systems are cumbersome, costly, and not designed for real-world applications, lacking the ability to accurately pinpoint events in mobile settings, often requiring users to manually mark events, which compromises accuracy.
Innovation Solution
A wearable, wireless, multi-modal bio-sensing system incorporating cameras for eye-tracking and sensors for EEG, ECG, and other bio-signals, allowing for comfortable, real-time data collection and synchronization of bio-signals with environmental events, using cameras to overlay eye-gaze on the user's view and process data for emotion recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bio-sensing systems are used, then measurement capability is provided, but device complexity and bulkiness increase, making them unsuitable for real-world mobile settings
Solution Approach 1:
The system divides the bio-sensing functionality into separate modular components: physiological sensors (EEG, ECG, GSR), ocular sensors (eye-tracking cameras), and environmental sensors. Each sensor type can be independently selected and configured based on specific experimental needs, reducing overall system complexity while maintaining comprehensive measurement capability.
Solution Approach 2:
The wearable device integrates multiple sensing modalities into a single platform that can simultaneously capture physiological signals, eye movements, and environmental context. This multi-functional approach eliminates the need for separate specialized devices, reducing bulkiness while preserving comprehensive measurement capabilities.
2Reliability
If traditional bio-sensing systems are assembled together, then complete sensing capability is achieved, but synchronization and calibration time increases
Solution Approach 1:
The system performs automated calibration procedures that occur during normal operation rather than requiring separate setup sessions. The synchronization of multiple sensor streams is handled through hardware-level time-stamping and software algorithms that automatically align data streams, eliminating manual calibration steps.
Solution Approach 2:
The bio-sensing system automatically synchronizes and calibrates its multiple sensors during operation without requiring external intervention. The system self-adjusts timing offsets and calibration parameters based on real-time data correlation, reducing setup time while maintaining sensing completeness.
3Ease of operation
If manual event marking is required, then user control is provided, but accuracy and reliability of event correlation deteriorates
Solution Approach 1:
The system replaces manual button-pressing with automated computer vision algorithms that detect and identify objects in the user's field of view. The eye-tracking camera captures visual attention data, and machine learning models automatically correlate this with physiological responses, eliminating manual intervention while improving precision.
Solution Approach 2:
The system introduces an intermediary computational layer that processes raw sensor data and automatically identifies meaningful events. Rather than relying on direct user input, the system uses algorithms to detect patterns in physiological signals and eye movements that indicate cognitive or emotional events, then correlates these with environmental context.
4Adaptability or versatility
If customized bio-sensing configurations are implemented, then user-specific needs are met, but system complexity and setup effort increase
Solution Approach 1:
The system provides dynamic configuration where users can enable or disable specific sensor modalities based on their needs. The software automatically adjusts data processing pipelines and analysis algorithms based on which sensors are active, maintaining simplicity while allowing extensive customization of the sensing suite.
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, comfortable, and cost-effective bio-signal monitoring in real-world environments, improving data correlation with user experiences by directly tagging emotions with observed objects and events, enhancing the reliability of bio-sensing systems.
Implementation Method 1
a first camera coupled to the frame and facing towards an eye of the user to capture a first set of images of the eye
Implementation Method 2
a second camera coupled to the frame and facing away from the user and configured to capture a second set of images of an environment from the user's perspective
Implementation Method 3
one or more sensors configured to measure biological functions of the user and to generate sensor data
Data Source
AI summary
Methods, systems, and devices are disclosed for implementing a low-cost wearable multi-modal bio-sensing system capable of recording bio-markers and eye-gaze overlaid on world view in real-world settings. In an exemplary embodiment, a bio-sensing system uses at least two cameras and one or more bio-sensors to record a variety of events and bio-marker data. In another exemplary embodiment, the recorded information is used to track the eye position, calculate the pupil dimensions, and calculate the gaze of a human being. The eye position and dimensions of the pupil can be used for emotion recognition.