Multi-Sensor Vital Sign Monitoring With Sensor-Fallback Prediction
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Solution Overview
Problem
Existing vital sign monitors face challenges in accurately predicting vital signs, such as heart rate and respiratory rate, especially when one or more sensors become unavailable or non-functional, leading to reduced precision and reliability.
Innovation Solution
A vital sign monitor equipped with multiple sensors and machine-learning based encoders and decoders that can extract feature vectors from datasets, allowing for predictive capabilities even when some sensors are not operational, and a training method that maintains the decoder's ability to predict vital signs from a single dataset while also utilizing additional sensors when available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to improve prediction precision, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The decoder is designed to universally handle feature vectors from one or multiple sensors. It can process feature vectors from the first sensor alone, or from both first and second sensors, making the system adaptable to different operational scenarios without requiring separate processing paths for each sensor configuration.
Solution Approach 2:
The system dynamically changes its operational parameters based on sensor availability. When the second sensor is unavailable, the system operates in single-sensor mode using only the first sensor's feature vector. When both sensors are available, it switches to multi-sensor mode, combining feature vectors from both sensors to improve prediction precision.
2Reliability
If the system is designed to handle sensor unavailability, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary checks to determine the availability of the second sensor before processing. Based on this preliminary information, it pre-determines which encoder to use (first encoder for single-sensor mode, second encoder for multi-sensor mode), avoiding runtime complexity and ensuring smooth operation regardless of sensor availability.
Solution Approach 2:
The system architecture is designed to be dynamic and adaptive. The decoder can dynamically accept feature vectors from different encoders based on sensor availability. This dynamic design allows the system to maintain reliability across varying operational conditions without requiring a fixed, complex architecture that handles all possible sensor combinations.
3Adaptability or versatility
If optional sensor usage is implemented, then adaptability is improved, but ease of operation worsens
Solution Approach 1:
The system automatically determines sensor availability and configures its processing pipeline without user intervention. It self-adapts by selecting the appropriate encoder based on whether the second sensor is available, eliminating the need for users to manually configure the system and maintaining operational simplicity despite the underlying complexity of supporting multiple sensor configurations.
Data Source
AI summary
In an embodiment a vital sign monitor includes a first sensor for obtaining a time series of a first sensor signal as a first dataset, a second sensor for obtaining a time series of a second sensor signal as a second dataset, a machine-learning based first encoder for extracting a first feature vector from the first dataset, a machine-learning based second encoder for extracting a second feature vector from the first dataset and the second dataset, and a machine-learning based decoder for predicting a vital sign of a person from the first feature vector or the second feature vector.