Multi-Output Neural Network for Wearable Physiological Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional wearable devices utilize separate neural networks for different physiological metrics, leading to inefficiencies in processing time, resource consumption, and reduced accuracy due to independent algorithm operation without knowledge transfer.
Innovation Solution
Implementing a multi-output neural network that processes multiple physiological metrics, such as sleep staging and SpO2, using a single architecture to reduce redundancy and enable knowledge transfer between tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural networks are used for different physiological metrics, then each metric can be processed independently, but processing time increases and power consumption increases
Solution Approach 1:
The patent combines multiple separate neural networks into a single multi-output neural network that processes multiple physiological metrics simultaneously. The network architecture includes shared layers that process common features from input data and separate output heads that generate predictions for different metrics (e.g., sleep staging, SpO2, respiratory rate), enabling parallel processing of multiple metrics in a single forward pass through the network.
Solution Approach 2:
The multi-output neural network is designed with universal functionality to handle multiple physiological metric predictions through a single architecture. The shared computational layers serve multiple purposes by extracting features that are relevant to different metrics, while the specialized output layers provide metric-specific predictions, allowing the system to perform multiple functions efficiently without requiring separate dedicated networks for each metric.
2Reliability
If separate neural networks are used for different physiological metrics, then each algorithm operates independently, but knowledge transfer between tasks is reduced and accuracy decreases
Solution Approach 1:
The patent merges multiple independent algorithms into a unified multi-output neural network where the shared layers enable knowledge transfer between different physiological metric predictions. The shared feature extraction layers learn representations that are beneficial for multiple metrics simultaneously, allowing the model to leverage correlations between different physiological signals and improve overall prediction accuracy compared to independent models.
Solution Approach 2:
The shared computational layers in the multi-output neural network act as intermediaries that facilitate knowledge transfer between different metric prediction tasks. These shared layers process input data and generate intermediate representations that contain information useful for multiple output metrics, enabling the model to learn common patterns and transfer knowledge across different physiological metric predictions.
3Reliability
If separate neural networks are used for different physiological metrics, then each metric has dedicated processing, but resource consumption increases and power consumption increases
Solution Approach 1:
The patent merges multiple separate neural network processing pipelines into a single multi-output neural network that processes multiple physiological metrics simultaneously in one forward pass. By sharing computational layers and consolidating the processing architecture, the system reduces redundant computations and resource consumption while maintaining dedicated processing capabilities for each metric through specialized output layers.
Data Source
AI summary
Methods, systems, and devices for utilizing a multi-output neural network are described. The system may receive physiological data and input the physiological data into the multi-output neural network. The physiological data may include a first input stream corresponding to the heartbeat data and a second input stream corresponding to the motion data. The multi-output neural network is trained to simultaneously compute one or more values of a first physiological metric and one or more values of a second physiological metric. In some cases, the first physiological metric and the second physiological metric each include an input stream from at least one of the first input stream, the second input stream, or both. The system may generate, via a single pass of the multi-output neural network, the one or more values of the first physiological metric and the one or more values of the second physiological metric.