Wearable Computer Eye Tracking Proactive Assistance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional personal computers and digital assistants lack the ability to see what a user is looking at, limiting their proactive assistance capabilities, as they rely on user input rather than real-time visual feedback, and existing attempts at natural user interfaces have not effectively addressed this requirement.
Innovation Solution
A wearable computer system with an eyeglass frame-based setup that includes eye tracking cameras, scene recording cameras, microphones, and processors, allowing users to interact via eye and hand gestures, voice, and providing visual feedback, enabling the device to anticipate user needs and offer assistance proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wearable computer uses cameras to capture user's visual field and eye movements, then the system can provide proactive assistance by understanding user attention, but the device complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The system divides the wearable computer into separate functional modules: eyewear unit with eye tracking camera, scene recording camera, and portable computing device. This segmentation allows each component to be optimized independently while working together to provide proactive assistance through integrated processing.
Solution Approach 2:
The wearable computer integrates multiple functions into a single system: eye tracking for attention detection, scene recording for visual capture, voice recognition for hands-free interaction, and display output. This multi-functionality enables the system to provide comprehensive proactive assistance while managing complexity through unified architecture.
2Productivity
If the system processes and displays information about user attention and visual field, then the personal assistant becomes more intelligent and responsive, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of eye tracking data and scene images to determine user attention and visual field before triggering specific assistant functions. This advance preparation allows the personal assistant to respond more quickly and intelligently to user needs by having contextual information ready in advance.
Solution Approach 2:
The system continuously monitors eye movements and visual field data, processing this feedback to dynamically adjust assistant responses. This real-time feedback loop enables the personal assistant to adapt to changing user attention and provide timely, context-aware assistance while optimizing processing efficiency through intelligent algorithms.
3Productivity
If the wearable computer captures and processes visual information continuously, then the system can provide timely assistance, but the energy consumption increases
Solution Approach 1:
The system uses periodic sampling of eye tracking data and scene images rather than continuous processing. By analyzing visual information at strategically determined intervals based on user behavior patterns and attention changes, the system maintains timely assistance capability while significantly reducing energy consumption compared to continuous processing.
Solution Approach 2:
The system automatically adjusts processing frequency and intensity based on detected user attention states and environmental conditions. When user engagement is low or visual information is stable, the system reduces processing activity to conserve energy. When attention changes or new visual stimuli are detected, processing intensity increases to provide timely assistance, creating a self-regulating energy consumption pattern.
4Ease of operation
If the system provides hands-free and attention-free operation through eye and voice tracking, then ease of operation improves, but the device complexity and sensor requirements increase
Solution Approach 1:
The system merges multiple input modalities including eye tracking, voice recognition, and scene analysis into a unified interaction framework. By combining these sensors and processing channels, the system achieves hands-free and attention-free operation while managing overall system complexity through integrated architecture and coordinated processing.
Data Source
AI summary
An embodiment of a Wearable Computer apparatus includes a first portable unit for data gathering and communicating feedback and a second portable unit for processing the at least gathered data from the first unit. The first portable unit includes an eyeglass frame, at least one first optical unit disposed on the eyeglass frame for capturing at least one scene image corresponding to a field of view of a user, at least one second optical unit disposed on the eyeglass frame for capturing at least one eye image corresponding to at least a portion of at least one eye of the user, at least one microphone to allow the user to communicate via voice, at least one speaker to allow the user to receive feedback via voice, at least one visible light source to allow the user to receive feedback via light signals, at least one motion sensor to monitor the head movements of the user, and at least one first processor to at least receive data from the data gathering units in the first portable unit and at least manage the communication with the second portable unit. The second portable unit is in communication with the first portable unit and includes at least one second processor configured for receiving the at least data from the first processor and decoding a pre-defined command from the user and executing at least one command in response to the received command. At least one of the processors will determine a direction within the field of view to which the at least one eye is directed based upon the at least a history of one eye image, and generates a command or a subset of the at least one scene image based on the determined direction. At least one of the processors will provide a feedback to the user to acknowledge the user command received. In one embodiment, the Wearable Computer will function as a driver assistant and in another embodiment as a cameraman.