Description:
This invention introduces lightweight, glasses-style wearable computing devices that integrate dry electrooculography (EOG) sensors into the temples to enable real-time, camera-free eye-movement control. Using onboard wavelet-based signal processing, the system accurately detects directional and magnitude-based saccades, supporting intuitive human–computer interaction, assistive communication, and continuous neuro-ocular health monitoring in natural environments.
Background:
Current eye-based human–computer interfaces typically depend on camera systems or wet-electrode EEG/EOG headsets, both of which carry significant drawbacks. Camera-based tracking requires controlled lighting, substantial computational resources, and costly optical components, while wet-electrode systems are uncomfortable, intrusive, and impractical for long-term wear. Dry-electrode EOG offers a promising alternative but faces challenges such as low-amplitude signals, motion artifacts, and drift instability, which hinder consistent saccade detection. A lightweight, low-cost, and accurate wearable interface is needed to reliably decode eye movements for real-time interaction and neuro-health monitoring.
Technology Overview:
This invention presents a glasses-style wearable device equipped with dry electrodes embedded into each temple and an onboard processor. The system captures electrooculographic (EOG) signals and performs fully local, wavelet-based signal processing: baseline drift is corrected using wavelet transforms, noise is reduced through median filtering, and saccades are detected using Continuous Wavelet Transform (CWT) with Haar mother wavelets at a fixed scale (e.g., scale 20). These methods enable real-time classification of saccade direction, magnitude, and size (small, medium, large). Eye movements are then encoded into a multi-level radix-7 symbol language, supporting intuitive gesture-based human–computer interaction without cameras or external computation.
Advantages:
• Seamless integration of dry EOG electrodes into eyeglass frames for all-day comfort and natural wearability.
• On-device wavelet-based signal processing provides robust artifact rejection and accurate saccade detection.
• Camera-free, low-power architecture eliminates the need for bulky optics or intensive computation.
• Multi-level eye-movement encoding expands the control vocabulary far beyond binary left/right triggers.
• High accuracy in distinguishing saccades and blinks under dynamic, real-world conditions.
• Cost-effective and compatible with consumer smart-glasses platforms (e.g., Google Glass, Vuzix).
• Potential for continuous, unobtrusive neuro-ocular health monitoring.
Applications:
• Hands-free assistive communication for individuals with severe motor impairments.
• Industrial and surgical augmented-reality interfaces for hands-busy, voice-restricted environments.
• Eye-driven control of consumer electronics and smart-home systems.
• Mobile neuro-health monitoring and saccadography for concussion and fatigue detection.
• Human–machine interfaces for AR/VR and robotics requiring natural, intuitive eye-based input.
• Driver and pilot alertness monitoring through real-time saccade and blink analysis.
Intellectual Property Summary:
• United States, 61/900,397, Provisional, 11/5/2013, Converted 9/23/2019
• United States, 14/533,617, Utility, 11/5/2014, Patented 5/1/2018, US 9,955,895
Stage of Development:
Prototype
Licensing Status:
This technology is available for licensing.
Licensing Potential:
Attractive to developers of wearable electronics, assistive communication technologies, AR/VR interfaces, and neuro-monitoring systems seeking a low-power, camera-free method for accurate, real-time eye-movement control.
Additional Information:
Information available upon request.
Inventors:
Zhanpeng Jin, Sarah Laszlo