Respiration Rate Measurement from Noisy Irregular Sensor Signals

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Solution Overview

Problem

Existing methods for long-distance contactless respiration rate monitoring are unreliable due to noisy and irregularly sampled sensor signals, particularly in uncontrolled environments, leading to poor estimation accuracy.

Innovation Solution

A system utilizing Discrete Prolate Spheroid window functions and Lomb-Scargle periodograms to process signals from sensors like RGB cameras and laser rangefinders, normalizing and computing weighted averages to estimate respiration rate from irregularly sampled data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional sensor signal processing methods are used for long-distance respiration rate monitoring, then the system is simple to implement, but the measurement precision deteriorates due to noisy and irregularly sampled sensor signals

Engineering Contradiction:

Improverespiration rate estimation accuracyVSAvoidsignal processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The signal processing is divided into distinct segments: normalization stage, windowing stage (with four different Discrete Prolate Spheroid window functions), and spectral analysis stage (Lomb-Scargle periodogram). Each segment handles specific aspects of the noisy, irregularly sampled data, improving overall measurement precision without requiring a complete redesign of the entire system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The Discrete Prolate Spheroid window functions serve as intermediaries between the raw sensor signals and the spectral analysis. These window functions specifically address the irregular sampling issue by providing appropriate weighting, enabling accurate frequency extraction even from non-uniformly sampled data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If Lomb-Scargle periodogram with multiple window functions is used to improve respiration rate estimation from irregularly sampled data, then the measurement precision improves, but the computational time increases

Engineering Contradiction:

Improverespiration rate estimation accuracyVSAvoidcomputational processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The sensor output signal is normalized before being processed by the window functions and periodogram analysis. This preliminary normalization step standardizes the data format and reduces computational complexity in subsequent steps, preventing the need for more complex processing later while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system computes four different Discrete Prolate Spheroid window functions and their corresponding Lomb-Scargle periodograms, then combines them. This excessive action (using more window functions than the minimum one) provides robustness against noise and irregular sampling, ensuring accurate respiration rate detection even in challenging environments.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If contactless long-distance monitoring is implemented, then the adaptability improves for uncontrolled environments, but the reliability deteriorates due to noisy sensor signals

Engineering Contradiction:

Improveenvironmental adaptabilityVSAvoidsignal quality

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system changes the temporal parameters of signal processing by using multiple Discrete Prolate Spheroid window functions with different bandwidths. This allows the system to adapt to varying environmental conditions and noise levels while maintaining reliable respiration rate measurement through appropriate parameter selection and combination.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12569161B2Methods for long-distance respiration rate measurement using noisy and irregularly sampled sensor signals

Publication Date: 2026.03.10 KONINKLIJKE PHILIPS NV

AI summary

A system for measuring long-distance respiration rate is provided. The system includes a processing unit configured to receive and normalized a sensor output signal. The processing unit is further configured to compute a series of four Discrete Prolate Spheroid window functions. For each of the series of four Discrete Prolate Spheroid window functions, the processing unit is configured to (1) multiply the normalized sensor output signal by the Discrete Prolate Spheroid window function to produce a windowed output signal, and (2) compute a Lomb-Scargle periodogram based on the windowed output signal. The processing unit is further configured to compute a weighted average of the Lomb-Scargle periodograms to produce an averaged periodogram. The processing unit is further configured to compute a frequency corresponding to a peak estimated spectral density on the averaged periodogram. The processing unit is further configured to output breaths per minute data based on the frequency.