Wearable Arrhythmia Detection via Cloud-Edge ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wearable medical devices are not effectively utilizing long-term data to improve detection rates of intermittent conditions, as the data is treated similarly to limited data from prior art devices, lacking advanced analysis techniques.
Innovation Solution
Implementing machine learning methods, such as neural nets and support vector machines, in wearable devices with sensor packages and computing resources to analyze biometric signals in real time, creating individualized models from population models, and enabling local and cloud-based data repositories for trend analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used on long-term wearable device data, then device simplicity is maintained, but detection precision for intermittent conditions deteriorates
Solution Approach 1:
The patent introduces cloud-based computing resources as an intermediary to perform complex machine learning analysis on wearable device data. The wearable device collects and transmits data to the cloud, where population models and individualized models are applied for arrhythmia detection. This mediator approach enables advanced detection precision while keeping the wearable device itself relatively simple.
Solution Approach 2:
The patent replaces traditional mechanical/statistical data analysis methods with machine learning algorithms including neural networks, support vector machines, and deep networks. This substitution enables more precise detection of intermittent arrhythmias by automatically learning patterns from long-term data without requiring complex manual analysis systems.
2Measurement precision
If machine learning techniques are implemented in wearable devices, then detection precision improves, but device complexity and computational requirements worsen
Solution Approach 1:
The patent segments the computational workload between the wearable device and cloud infrastructure. The wearable device performs preliminary signal processing and feature extraction, while more computationally intensive machine learning model training and complex analysis are performed in the cloud. This segmentation enables advanced detection capabilities while managing device complexity.
Solution Approach 2:
The patent develops population models that can be applied across multiple individuals and device types. These universal models capture general arrhythmia patterns that can be efficiently deployed across different wearable devices, reducing the need for each device to independently handle all computational complexity while maintaining high detection precision.
3Measurement precision
If population models are used for arrhythmia detection, then analysis speed is maintained, but measurement precision for individual cases deteriorates
Solution Approach 1:
The patent performs preliminary action by training population models on large datasets in advance, storing these pre-trained models for efficient deployment. When analyzing individual patient data, the system quickly adapts these pre-trained population models to individual characteristics, significantly reducing the time required for individualized analysis while maintaining high precision.
Solution Approach 2:
The patent implements feedback mechanisms where individual patient data continuously refines and personalizes the population models over time. As more individual data becomes available, the models adapt and improve their precision for that specific patient, gradually reducing the gap between population-level and individualized detection accuracy without requiring complete retraining.
Data Source
AI summary
Systems and methods of arrhythmia detection and associated apparatus that utilize machine learning techniques that allow for the consideration individual characteristics and the tailoring/personalization of biometric data allow for early detection and treatment, especially of cardiac arrhythmias and other abnormalities.