Predictive Health Device Configuration for Migraine Symptom Reduction
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Solution Overview
Problem
Migraines and other health afflictions are exacerbated by mobile device features like blue light and noise, negatively impacting quality of life and productivity, with existing technologies failing to effectively predict and mitigate these symptoms.
Innovation Solution
A computer-implemented method that predicts the onset of health afflictions by monitoring environmental and internal conditions, then configures devices to reduce symptom-exacerbating factors, such as adjusting display brightness and sound levels, using wearable sensors and logistic regression classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile devices are used to monitor and predict health conditions, then health prediction accuracy is improved, but device emissions (blue light, noise) exacerbate symptoms of health afflictions
Solution Approach 1:
The system performs preliminary actions by predicting health afflictions before they occur using sensor data and machine learning models. When a migraine attack is predicted, the system proactively configures devices to reduce harmful emissions (blue light, noise) before the affliction begins, thereby preventing symptom exacerbation while maintaining prediction accuracy
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data from wearables and mobile devices, comparing actual health conditions against predictions, and adjusting device configurations accordingly. This closed-loop feedback ensures that device emissions are minimized during predicted health events while maintaining accurate health monitoring capabilities
2Object-affected harmful factors
If devices are configured to reduce harmful emissions during health afflictions, then symptom exacerbation is reduced, but device functionality and user convenience are limited
Solution Approach 1:
The system dynamically adjusts device configurations based on real-time health predictions and current conditions. Instead of permanently limiting device functionality, the system temporarily modifies settings (display brightness, blue light filtering, noise levels) only when and where needed during predicted health events, thereby reducing symptom exacerbation while preserving normal device operation during healthy states
Solution Approach 2:
The system changes operational parameters of devices (display brightness, color temperature, volume levels, notification settings) based on predicted health conditions. These parameter adjustments are automatically reversed when the health event passes, ensuring that symptom exacerbation is reduced during afflictions while full device functionality remains available during normal times
3Measurement precision
If comprehensive sensor data is collected to improve prediction accuracy, then measurement precision is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system uses multi-functional sensor data collection where the same sensors serve multiple purposes: monitoring health metrics for prediction, tracking environmental conditions, and providing context for symptom management. This universal approach improves prediction accuracy without requiring separate dedicated sensors for each function, thereby limiting the increase in system complexity
Data Source
AI summary
Device configuration based on predicting a health affliction. A process acquires measurements of conditions that a user is experiencing. The process predicts, based on the measurements, whether the user will experience a particular health affliction. Based on predicting that the user will experience the particular health affliction, the process configures devices of an environment in which the user is present to reduce effects of the devices on symptoms of the particular health affliction. The configuring includes adjusting a respective at least one state of each device of the devices.