Sensor Clock Synchronization via Heart Rate Signal Modeling
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Solution Overview
Problem
Existing methods for synchronizing output signals from wearable sensors measuring heartbeat-derived physiological signals are unreliable, robust, and accurate due to high power requirements, device complexity, and subject-dependent synchronization methods, which increase costs and security vulnerabilities.
Innovation Solution
A method that synchronizes output signals from multiple sensors by determining heart rate sequence signals, identifying heart rate events, and applying a model-based time domain transformation to align the clocks of the sensors, using naturally occurring heart rate variability to match patterns and fit a model for clock drift, allowing for posteriori synchronization of recorded physiological signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time information is sent wirelessly during measurement for synchronization, then synchronization accuracy is improved, but power consumption increases and device complexity increases
Solution Approach 1:
The system uses the sensors' own physiological signals (heart rate) to achieve synchronization without requiring external time information transmission. The heart rate signals serve dual purposes: clinical measurement and clock synchronization, eliminating the need for separate synchronization communication infrastructure.
Solution Approach 2:
The heart rate signal serves multiple functions simultaneously: it is both the primary physiological measurement for clinical analysis and the synchronization reference signal for aligning time domains across multiple sensors. This multi-functionality eliminates the need for dedicated synchronization hardware.
2Measurement precision
If time information is sent wirelessly during measurement for synchronization, then synchronization accuracy is improved, but device complexity increases
Solution Approach 1:
The sensors utilize their existing signal processing capabilities to extract synchronization information from their primary measurements. No additional hardware modules for synchronization communication are required, as the heart rate signal processing infrastructure is already in place for clinical analysis.
Solution Approach 2:
The same signal processing pipeline that analyzes heart rate for clinical purposes is also used to derive synchronization timing information. This universal approach eliminates the need for separate synchronization hardware and reduces overall system complexity.
3Device complexity
If a posteriori signal analysis methods are used for synchronization, then device complexity is reduced, but reliability decreases due to subject-dependent requirements
Solution Approach 1:
The system continuously monitors heart rate variability patterns and uses this feedback to dynamically adjust and refine the synchronization model. The feedback loop ensures that synchronization adapts to individual subject characteristics and maintains accuracy without requiring complex manual configuration or subject cooperation.
Solution Approach 2:
The synchronization model automatically adjusts timing parameters based on observed heart rate patterns and variability. By dynamically changing synchronization parameters to match individual physiological characteristics, the system achieves high reliability across different subjects without requiring subject-specific manual calibration.
Data Source
AI summary
The present invention relates to a method (100) and system (1) of synchronizing output signals (S1, S2) from a plurality of sensors (11, 12) each measuring a heart rate derivable physiological signal of a subject. The method comprises providing a first output signal (S1) of a first sensor (11) configured to measure a heartbeat derivable physiological signal of a subject: providing a second output signal (S2) of a second sensor (12) configured to measure a heart rate derivable physiological signal of the subject, wherein each of the first and second sensor comprises an independent clock (11a, 12a) for associating the first and second output signals with a respective time domain. The method further comprises the steps of: determining first and second heart rate sequence signal (r1, r2) from the first and second output signals: identifying timings of at least one heart rate event in the respective first and second heart rate sequence signals; and determining a first time domain transformation for the time domain of the second output signal to the time domain of the first output signal by means of a model fitted to the timings of the heart rate event in the respective first and second heart rate sequence signals.