IoT Device Self-Adaptive Notification Customization
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Solution Overview
Problem
Existing electronic devices in IoT environments often have default notification settings that can cause unintended physiological changes in users, such as increased heart rate, blood pressure, or stress, due to inconsistent user experiences.
Innovation Solution
A method and system for customizing electronic device characteristics, such as vibration intensity, display settings, and sound levels, based on a user's current activity, environmental context, and health parameters, using a learning module to adjust settings when significant physiological changes are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default notification settings are used, then device complexity is reduced and ease of operation is improved, but user physiological health deteriorates due to unintended stress and heart rate changes
Solution Approach 1:
The system continuously monitors user physiological parameters (heart rate, stress levels, blood pressure) and uses this feedback to dynamically adjust notification characteristics. The feedback loop enables the device to adapt notification intensity, timing, and modality based on real-time physiological state, preventing harmful effects while maintaining ease of operation.
Solution Approach 2:
The notification system transitions from static default settings to dynamic adaptive settings that change based on user physiological state. The device characteristics (vibration intensity, sound volume, display brightness) are no longer fixed but are continuously adjusted according to real-time physiological data, resolving the contradiction between simplicity and health protection.
2Reliability
If notification intensity is increased to ensure user awareness, then information delivery effectiveness is improved, but physiological stress on the user increases
Solution Approach 1:
The system changes notification parameters (intensity, duration, modality) based on physiological state. When stress levels are high, the system adjusts parameters to reduce stress impact while maintaining information delivery. When attention is low, parameters are adjusted to enhance awareness without causing excessive stress, thus resolving the contradiction between reliable information delivery and stress reduction.
Solution Approach 2:
Physiological monitoring data acts as an intermediary that mediates between the notification system and the user. The system uses physiological state as an intermediate variable to determine appropriate notification intensity, ensuring information delivery effectiveness while preventing harmful stress responses.
3Adaptability or versatility
If personalized notification settings are implemented for each user, then user experience is improved, but device complexity and configuration time increase
Solution Approach 1:
The system performs self-configuration by automatically monitoring physiological parameters and deriving personalized notification preferences without requiring explicit user input. The device learns optimal notification settings through continuous physiological feedback, eliminating the need for complex manual configuration while achieving high personalization.
Solution Approach 2:
The system performs preliminary physiological monitoring and analysis to pre-determine optimal notification settings before actual notifications are sent. By proactively establishing personalized parameters based on baseline physiological data, the system reduces configuration complexity while maintaining adaptability.
4Measurement precision
If continuous physiological monitoring is performed, then notification customization accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic physiological measurements at strategically timed intervals. Physiological parameters are measured before, during, and after notifications to capture state changes, rather than maintaining constant monitoring. This periodic approach maintains measurement precision for notification customization while significantly reducing energy consumption.
Solution Approach 2:
The system applies partial monitoring by focusing measurement resources on critical physiological parameters and critical time windows around notification events. Rather than exhaustive continuous monitoring of all parameters, the system monitors only the necessary parameters at necessary times, achieving sufficient precision with reduced energy expenditure.
Data Source
AI summary
Methods and systems for customizing the characteristic of an electronic device (in the Internet of Things (IoT) environment based on at least one user's physiological state are provided. The method includes identifying context of the electronic device in response to receiving at least one event by the electronic device, wherein the at least one context includes at least one current user activity and an environmental context of a user. The method includes determining the change in a health parameter of the user and re-calibrates the characteristics of an electronic device through the magnitude of change in health parameter from the learning module. The method includes identifying current user activity and an environment context of the user on receiving the event from the electronic device).