Wearable Heartbeat Risk Scoring for Early Metabolic Syndrome Detection
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Solution Overview
Problem
Individuals often fail to recognize and report mild symptoms of negative health conditions like metabolic syndrome, leading to worsening health outcomes due to unreported symptoms.
Innovation Solution
A method and system using a wearable device to collect heartbeat data, process it through machine-learning models, and generate risk scores for metabolic syndrome by analyzing heart rate variability and other data types, with down-sampling to ensure consistent data quantity across different time intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If symptom reporting is relied upon for health condition detection, then individual awareness of symptoms is required, but mild or unrecognized symptoms are missed leading to delayed diagnosis
Solution Approach 1:
The system automatically collects heartbeat data and performs risk assessment without requiring user action. The wearable device continuously monitors cardiac activity and the machine learning model autonomously generates risk scores, eliminating the need for users to manually report symptoms while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the manual symptom reporting mechanism with an automated physiological monitoring system. Instead of relying on user-subjective reports, the system uses objective heartbeat data collected by sensors and processed through machine learning algorithms to detect metabolic syndrome risk
2Adaptability or versatility
If multiple heartbeat data types are collected with different sampling rates, then comprehensive analysis is achieved, but data quantity inconsistency affects model processing
Solution Approach 1:
The system changes the parameter of data quantity by applying down-sampling to reduce the number of data points for high-frequency heartbeat metrics while up-sampling or holding values for low-frequency metrics. This parameter transformation ensures all heartbeat data types have consistent quantity suitable for machine learning model input while preserving the original multi-type data structure
Solution Approach 2:
The patent implements dynamic data processing where the sampling rate adjustment is not fixed but adapts based on the specific heartbeat data type and time interval requirements. The system dynamically determines optimal down-sampling factors for each data type to achieve quantity consistency while maintaining the distinctive characteristics of each heartbeat metric
3Reliability
If continuous heartbeat monitoring is performed, then early detection capability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the continuous heartbeat data into distinct time intervals and processes each interval separately through the machine learning model. This segmentation transforms the complex continuous monitoring task into manageable discrete units, reducing processing complexity while maintaining early detection capability across the entire monitoring period
Solution Approach 2:
The system performs preliminary processing of heartbeat data by calculating time interval statistics and down-sampling before feeding data to the machine learning model. This preliminary action reduces the dimensionality and complexity of the input data, making the overall system more manageable while preserving the essential information needed for early detection
Data Source
AI summary
Aspects of the present disclosure include methods for assessing metabolic risk. In one example, a device may receive a set of heartbeats over a time interval. A set of time interval statistics for the time interval may be generated by generating values for each of a set of heartbeat data types based on a subset of the set of heartbeats and corresponding to a portion of the time interval and generating a time interval statistic based on values of a heartbeat data type. The set of time interval statistics may be processed using a machine-learning model to generate a risk score for the time interval. The device may determine that the risk score is greater than a predetermined threshold, and in response, output an indication of a health risk that is associated with a user of the device.