Vital Data Sensor System with Predictive Evaluation Unit
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Solution Overview
Problem
Current systems for monitoring vital parameters in living beings lack the ability to predict changes in condition, such as wake-up times, with sufficient accuracy and automation, limiting their interaction with other devices and user convenience.
Innovation Solution
A system comprising a sensor that measures vital parameters and an evaluation unit that processes the measurement signals to predict changes in condition, using machine learning algorithms and notification devices to automate actions based on predicted times, such as waking up, to enhance user experience and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current monitoring systems only record and display vital data without prediction capability, then the system complexity remains low, but the usefulness and user convenience are limited
Solution Approach 1:
The system performs prediction of future states (wake-up times, state changes) before they actually occur by analyzing current and historical vital parameter data. This allows the system to provide advance information and trigger preliminary actions, transforming a simple monitoring system into a predictive system that adds versatility without proportionally increasing complexity
Solution Approach 2:
The evaluation unit acts as an intermediary between the sensor data and the user/external systems. It processes raw measurement signals, applies evaluation criteria and machine learning models, and generates predicted information. This intermediary layer enables complex predictive functionality while keeping the overall system architecture manageable and modular
2Measurement precision
If the system uses machine learning algorithms for prediction, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies machine learning algorithms selectively rather than continuously. Prediction operations are triggered based on detected patterns in vital parameter data, such as when specific sleep phases are detected or when prediction confidence thresholds are met. This partial application of complex processing reduces overall computational burden while maintaining prediction accuracy when needed
Solution Approach 2:
The machine learning models are trained offline using historical data, allowing the system to learn prediction patterns during periods when real-time processing is less critical. The trained models then perform rapid inference during actual monitoring, separating the computationally intensive training phase from the real-time prediction phase to minimize processing time impact
3Ease of operation
If the system triggers actions based on predicted times, then user convenience and productivity improve, but the risk of incorrect predictions and unwanted disruptions increases
Solution Approach 1:
The system incorporates feedback mechanisms where prediction results are monitored and compared against actual outcomes. When predictions are made about state changes or wake-up times, the system tracks whether these predictions materialize and uses this information to refine prediction algorithms and adjust future predictions. This feedback loop continuously improves reliability while maintaining the convenience of automated action triggering
Solution Approach 2:
The system applies cushioning by setting confidence thresholds and validation criteria before triggering automated actions. Multiple evaluation criteria are checked, and predictions are only acted upon when they meet predefined reliability standards. This beforehand cushioning prevents premature or incorrect actions while preserving the benefits of predictive automation for high-confidence predictions
Data Source
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AI summary
The invention relates to a system comprising a sensor 1, 2, 3, 4, which can measure a vital parameter of a living being 5 and output a corresponding measurement signal 6, and an evaluation unit 7, which can evaluate the measurement signal 6 from the sensor 1, 2, 3, 4. Based on the evaluated measurement signal, the evaluation unit 7 can predict a time t1, t2 for the occurrence of a change in the state of the living being 5. The invention further relates to a use, a method, and a computer program product. Household appliances can thus perform actions or enter operating states depending on the predicted time.