Multimodal Biomarker Collection with Timestamp Alignment
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Solution Overview
Problem
Current methods for collecting physiological biomarker data are time-consuming and cumbersome, making it difficult to utilize these biometrics for real-time or near real-time detection of health conditions.
Innovation Solution
A method involving the collection of biomarker-related data from multiple sensors, with timestamping for alignment, to generate physiological biomarkers that can be used to determine a subject's health status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to collect physiological biomarker data, then measurement precision can be achieved, but the collection process becomes time-consuming and cumbersome, preventing real-time or near real-time detection
Solution Approach 1:
The patent combines multiple sensor types (optical sensors, acoustic sensors, thermal sensors, etc.) into a single integrated multimodal data collection system. This merging allows simultaneous collection of various physiological biomarkers (gait metrics, vocal metrics, skin temperature, heart rate, etc.) without requiring separate collection processes, thereby maintaining measurement precision while eliminating time-consuming sequential collection procedures
Solution Approach 2:
The system employs a universal multimodal data collection apparatus that can detect multiple types of physiological biomarkers through different sensor modalities. This multi-functional system can measure gait-related metrics, vocal metrics, skin temperature, heart rate, and other physiological parameters simultaneously, enabling real-time health monitoring without requiring multiple specialized collection devices
2Reliability
If multiple types of biomarker data are collected from multiple sensors, then the robustness and predictive value of physiological biomarkers improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex data collection system into distinct sensor modules (optical sensors for gait detection, acoustic sensors for vocal analysis, thermal sensors for temperature measurement, etc.), each dedicated to collecting specific types of biomarker data. This segmentation allows the system to handle multiple data types through modular components while maintaining manageable complexity and enabling specialized processing of each biomarker type
Solution Approach 2:
The system introduces a data processing intermediary that receives raw data from multiple sensor types, performs time-alignment using timestamps, and generates integrated physiological biomarkers. This intermediary layer simplifies the complexity by providing a standardized interface between diverse sensors and the analysis system, managing the complexity through structured data transformation and correlation
3Measurement precision
If biomarker data from multiple sensors is collected and time-aligned, then the accuracy of health status determination improves, but the data processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary time-alignment of sensor data using embedded timestamps before generating physiological biomarkers. By pre-processing the time-synchronization in the data collection phase rather than during analysis, the system reduces the computational burden during health status determination while maintaining accurate temporal correlation between different biomarker measurements
Solution Approach 2:
The multimodal data collection system operates continuously, simultaneously gathering data from multiple sensor types without interruption. This continuous collection with built-in time-stamping eliminates the need for post-collection time-alignment processing, as the temporal relationships are already captured during the continuous measurement process, thereby reducing processing time while maintaining precision
Data Source
AI summary
A method is disclosed that includes collecting first biomarker-related data of a subject from a first sensor and second biomarker-related of the subject from a second sensor, the first biomarker-related data being tagged with a first time of collection of the first biomarker-related data from the first sensor, the second biomarker-related data being tagged with a second time of collection of the second biomarker-related data from the second sensor; generating a first physiological biomarker using the first biomarker-related data and a second physiological biomarker using the second biomarker-related data; and determining a health status of the subject based on the first physiological biomarker and the second physiological biomarker.