Posture-Based Dry Eye Alert System
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Solution Overview
Problem
Existing methods for preventing dry eye syndrome, such as artificial tear solutions and eye tracker technology, are cumbersome or inconvenient, and there is a need for a system that can alert users to blink more frequently while performing tasks to prevent decreased eye blink rates.
Innovation Solution
A system comprising a data collecting unit, an eye blink frequency calculating unit, and a diagnosis and alert output unit that detects user posture and task type to alert users to blink more frequently, using sensors on a chair and software on smart devices to estimate eye blink frequency and output alerts when it falls below a preset reference value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If artificial tear solutions are used to treat dry eye syndrome, then the symptoms of dry eye are relieved, but the treatment becomes cumbersome and requires repeated administration
Solution Approach 1:
The system enables self-monitoring of eye blink frequency through sensors that detect blinking automatically without requiring user intervention. The system serves itself by autonomously calculating blink rates and generating alerts when thresholds are exceeded, eliminating the need for manual treatment administration and monitoring
Solution Approach 2:
The system implements a feedback loop where eye blink frequency is continuously monitored, compared against reference values, and alerts are generated when abnormal patterns are detected. This closed-loop feedback mechanism provides real-time information about eye health status, enabling proactive prevention rather than reactive treatment
2Measurement precision
If eye tracker technology is used to monitor eye movement, then eye behavior can be tracked, but the device becomes inconvenient due to the need for wearing or attaching separate equipment
Solution Approach 1:
The system merges the eye monitoring function with the existing chair or workstation environment by integrating sensors into these familiar objects. This combination eliminates the need for separate wearable eye trackers while maintaining the capability to detect eye blink frequency and patterns through the integrated sensor system
Solution Approach 2:
The system uses an intermediary approach by detecting eye blink frequency indirectly through sensors that measure physical movements associated with blinking, rather than directly tracking eye position. This intermediary measurement method achieves sufficient monitoring precision without requiring complex optical eye tracking equipment
3Productivity
If users focus attention on tasks for long periods, then productivity increases, but eye blink frequency decreases leading to dry eye syndrome
Solution Approach 1:
The system takes preliminary action by continuously monitoring eye blink frequency and generating alerts before dry eye syndrome develops. By detecting reduced blink rates during focused tasks and providing early warning, the system enables users to take preventive measures before actual eye damage occurs, maintaining both productivity and eye health
Solution Approach 2:
The system applies preliminary anti-action by counteracting the harmful effect of reduced eye blinking during focused work through automated alerts. These alerts prompt users to consciously blink more frequently, providing a counterbalancing action that prevents the accumulation of harmful effects while allowing sustained task engagement
Data Source
AI summary
A dry eye syndrome alert system through posture and work detection, includes: a data collecting unit configured to detect a posture of a user to collect posture data of the user and preprocess the posture data; an eye blink frequency calculating unit configured to identify a posture change of the user on the basis of the posture data, calculate a motion variability on the basis of the posture change, and estimate an eye blink frequency of the user on the basis of the motion variability; and a diagnosis and alert output unit configured to store data regarding the estimated eye blink frequency, compare the estimated eye blink frequency with a preset reference value, and output an alert to the user when the estimated eye blink frequency is less than or equal to the preset reference value.