JP7729529B2 - Biological information detection method and biological information detection system - Google Patents

Description

The present invention relates to a biological information detection method and biological information detection system for detecting biological movements and vital signs in daily life.

Today, there are many systems available that collect various vital signs and movements while a person is at rest, such as sleeping, or while working or otherwise active. However, one of the biggest obstacles to achieving satisfactory accuracy is environmental noise surrounding the object being measured, such as the presence or movement of objects that are not the object being measured.
Since objects cannot exist independently, it is common for a large number of displacements and vibrations (hereafter referred to as "waveforms") to be detected simultaneously from the human living environment. The detected waveforms can only be processed as information once the target waveform is extracted from among them.

Against this background, various waveform analysis methods have been provided, including a method for detecting vital signs through FFT analysis (see Patent Document 1 or Patent Document 2 below), a method for deriving feature points extracted from time-series data using up to positive and negative third-order differentials (see Patent Document 3 below), a method for extracting heartbeats using a harmonic bandpass filter (see Patent Document 4 below), and a method for extracting heartbeats using pattern matching from fundamental frequencies and harmonic components (see Patent Document 5 below).

Japanese Patent Application Laid-Open No. 2019-13479 JP 2013-116216 A JP 2016-214876 A Japanese Patent Application Laid-Open No. 2017-169867 Japanese Patent Application Laid-Open No. 2007-167545

Displacements on the surface of the human body are generally determined for each of breathing, heart rate, and other bodily movements. However, when applied to in-vehicle systems, for example, road vibrations and vibrations generated by the vehicle itself, such as the engine, are superimposed, making it extremely difficult to accurately measure pulse. Accurate non-contact detection of vital signs in various high-noise environments, whether at rest or during activity, has not yet been successfully achieved.

Furthermore, the above analysis method requires a highly capable computing device to perform it precisely, which increases the cost of the entire system, making it unsuitable for widespread use in society as a system for everyday personal use.

The present invention was made in consideration of the above-mentioned circumstances, and aims to provide a low-cost biometric information detection method and system that can derive accurate biometric information from mixed waveforms, even when any movement or vibration is superimposed on the source of human body movement or vital signs in a living environment.

The biometric information detection method of the present invention, developed to solve the above problems, is a biometric information detection method that derives information on a target pulsation from information detected by a sensor, and is characterized by going through the following steps: a mapping process that saves the displacement and detection level collected by the sensor in chronological order; a filtering process that detects frequency components in a specific harmonic band around an n-th (a natural number greater than or equal to 2) multiple frequency of the target pulsation from the displacement collected by the sensor; a specific component extraction process that detects a specific frequency component with a predetermined level from the frequency components in the specific harmonic band and derives that frequency; and a target information identification process that multiplies the frequency of the specific frequency component by 1/n to derive the detection frequency of the target pulsation.

The specific component extraction process can employ a method of outputting the average frequency value of the specific frequency component per unit time.

The biological information detection system of the present invention, which has been developed to solve the above problems, is an information detection system that derives information on a target pulsation from information detected by a sensor, and is characterized by comprising: a level recording means that stores the detection level of the displacement collected by the sensor, linking it to the time of detection; a frequency calculation means that derives frequency components from the progression of the displacement collected by the sensor; a frequency recording means that stores the frequency components, linking them to the time of detection; a filtering means that detects frequency components in a specific harmonic band around an n-th multiple (a natural number greater than or equal to 2) of the target pulsation frequency from the frequency components; a specific component extraction means that detects a specific frequency component with a predetermined level from the frequency components in the specific harmonic band and derives that frequency; and a target information identification means that multiplies the frequency of the specific frequency component by 1/n to derive the detection frequency of the target pulsation.

The specific component extraction means may be configured to output an average value of the frequency of the specific frequency component per unit time.
Further, the present invention may be configured to include a specific band selection means for setting the specific harmonic band, or a specific level setting means for setting the range or order of detection levels to be adopted as the specific frequency components in the specific component extraction means.

The biometric information detection method and biometric information detection system of the present invention employ a technique for detecting the frequency of the target pulsation by first extracting specific frequency components from a specific harmonic band. This suppresses the effects of discontinuous or abnormally sized displacements, as well as distant pulsations with extremely low detection levels. This allows the target frequency component to be accurately detected while easily separating it from other frequency components, and enables inexpensive and accurate verification of the characteristics of the pulsation's periodic and amplitude fluctuations over time.

1 is a block diagram showing an example of a biological information detection method and a biological information detection system according to the present invention. 1 is a graph showing an example of visualization of an ECG measurement signal in the time frequency domain as a reference using the biological information detection method and biological information detection system according to the present invention. 1 is a graph showing an example of a Doppler signal visualized in the time frequency domain by the biological information detection method and biological information detection system according to the present invention. 10 is a graph showing an example of a Doppler signal visualized in the time-frequency domain and frequency components of n harmonics extracted by the biological information detection method and biological information detection system according to the present invention. 10 is a graph showing an example in which a Doppler signal is visualized in the time-frequency domain by the biological information detection method and biological information detection system according to the present invention, and the frequency component of the nth harmonic is extracted and displayed in a 1/nth frequency band. 1A and 1B are block diagrams showing an example of a hardware configuration and a module configuration of a biological information detection method and a biological information detection system according to the present invention. 1 is a block diagram showing an example of a functional configuration of a biological information detection method and a biological information detection system according to the present invention.

The following describes in detail an embodiment of a biological information detection method and biological information detection system according to the present invention, with reference to the accompanying drawings.

The figure shows an example of a biometric information detection system (hereinafter referred to as the "system") that analyzes detection information collected by a sensor from the body of a monitored subject, and is composed of a sensor unit A (non-contact vital sensor) that detects the ecology and environment of the monitored subject, and a control unit B that stores and analyzes the movement data detected by the sensor unit A.
The sensor unit A is configured to include a Doppler sensor (see, for example, JP 2011-34938 A) that detects motion data consisting of displacement (detection phase difference) detected using the body of the monitored object as a reflective surface and the distance to the monitored object (detection level) in a non-contact manner, and a communication device that transmits the motion data obtained from the output of the Doppler sensor to the control unit B.

The sensor unit A is placed in a location where it can overlook or monitor the life of the monitored person, such as in front of the driver's seat, a private or shared room, toilet, washroom, kitchen (or kitchen), workshop, bathroom, or hallway, and is installed by camouflaging it so as not to be conspicuous, or by embedding it in a wall or ceiling (see, for example, JP 2002-117466 A).

The control unit B is a unit that analyzes the motion data detected by the sensor unit A and detects the heart rate, breathing, body movement, and other displacements of the clothing-wearing monitoring subject.
The control unit B is configured as, for example, a computer system, and as shown in FIG. 6, it comprises a CPU (Central Processing Unit) 1 that controls each part, a timer 2 that functions as a calendar and clock, a ROM (Read Only Memory) 3 that stores various information including various programs, a RAM (Random Access Memory) 4 that functions as a work area, a storage unit 5 that stores various information in a readable and/or writable manner, a user interface or other input interface 6 that receives input from the outside, and an output interface 7 that outputs various information, and also comprises an analysis unit 8 that derives information on the target pulsation (hereinafter referred to as "target") from the motion data, an input control unit 9 that processes and controls input via various operating devices, and an output control unit 10 that processes and controls output of various information, such as a display unit that displays various input/output information (see FIG. 6).

Specific means of the analysis unit 8, which are formed by cooperation between the hardware resources and the biometric information detection program embedded therein, include: level recording means for storing level data linking the detection level of motion data collected by sensor unit A equipped with a Doppler sensor with the detection date and time; frequency calculation means for deriving the frequency components and frequencies of the pulsation contained in the displacement data of the motion data collected by sensor unit A from temporal fluctuations in the data; frequency recording means for storing frequency data linking the frequency components and the detection date and time; filtering means for extracting frequency components present in a peripheral band (specific harmonic band) of n (a natural number greater than or equal to 2) times the frequency suitable for detecting the target's harmonic (specific frequency component) from the frequency components; specific component extraction means for detecting specific frequency components with a predetermined level from the frequency components in the specific harmonic band and deriving their frequency (multiplied frequency); and target information identification means for multiplying the multiplied frequency by 1/n to derive the target's detection frequency (see Figure 7).

Furthermore, the control unit B in this example is equipped with a specific level setting means for setting the range or order of level data of frequency components to be adopted as the specific frequency components by the specific component extraction means (the order of adoption of frequency components based on the value of the level data), and a specific band selection means for selecting the specific harmonic band in which the specific frequency component can be detected most accurately from a plurality of harmonic bands each divided into frequency bands including any of the second harmonic through the Nth (max)th harmonic, and setting this as the processing target of the filtering means.

The level recording means controls the sensor unit A via the input control unit 9, and stores the detection level of the movement data collected by the sensor unit A as level data linked to the detection date and time in the memory unit 5, which may be a memory IC, hard disk, removable media, or a recording medium accessible via an information network.

The frequency calculation means extracts one or more pulsations that occur at a stable period (preferably occurring at a stable detection level) from the level data and displacement data recorded in the level recording means, and calculates the occurrence period and frequency of the pulsations (hereinafter referred to as "pulsation frequency").
The frequency recording means regards each of the pulsation frequencies obtained by the frequency calculation means as an actual frequency component, and stores it in the memory unit 5 as frequency data linked to the detection date and time when the frequency component existed.

This example includes means (hereinafter referred to as "visualization means") for outputting the level data stored in the level recording means and the frequency data derived by the frequency calculation means to a display device or printer for visualization, including waveform generation means for generating a displacement distribution chart by plotting displacement data derived from the dynamic data in time series, distribution display means for plotting the frequency data on the time axis and generating a frequency distribution chart showing the distribution and fluctuation status of each frequency component in time series, and three-dimensional distribution display means for generating a level/frequency distribution chart (see Figure 3) in which the dots constituting the frequency distribution are further color-coded according to the level data.

The existence of such visualization means allows for visual examination and verification when considering which harmonic band is most suitable for detecting specific frequency components, such as whether to select the second harmonic band or another harmonic band as the specific harmonic band using the specific band selection means, or when considering what detection level of pulsation to select using the specific level setting means (for example, when considering which level, in descending order, is the specific frequency component), allowing for more accurate selection.

The filtering means, based on the level/frequency distribution map visualized by the three-dimensional distribution display means or the data thereof, performs so-called digital filter processing or the like to select a band (specific harmonic band) from each harmonic surrounding band including any of the target harmonics, which band satisfies the following requirements: (1) it contains a frequency component (estimated to be a specific frequency component) with a relatively stable period (frequency), and (2) there is no other frequency component in the vicinity of the specific frequency component whose level data and frequency data are similar to each other; and selectively outputs only the frequency component of the specific harmonic band to the specific component extraction means (see FIG. 4).
The width of the specific harmonic band is set as an appropriate bandwidth, for example, a width including the fluctuation band of the target reference frequency, or a width higher than the n-1th harmonic and lower than the n+1th harmonic that sandwiches the nth harmonic.

The target is selected as a fundamental frequency component by the specific level setting means in accordance with the frequency and detection level predicted to be detected from breathing, heartbeat, etc. among stable pulsations with little frequency fluctuation, and the harmonic (specific frequency component) of the fundamental frequency component is selected by the specific band selection means in accordance with the requirements (1) and (2) above.
The narrower the specific harmonic band, the easier the filtering process by the filtering means becomes, the shorter the delay time of the process becomes, and the easier it becomes to detect the multiplied frequency of the specific frequency component in the specific component extraction means.

The specific component extraction means detects a multiple frequency of a specific frequency component in a specific harmonic band specified as a processing target of the filtering means (see FIG. 4).
The specific component extraction means in this example takes into consideration frequency fluctuations of the specific frequency component and outputs the average value of the multiplied frequency in a unit time as the multiplied frequency at the date and time.
The target information specifying means multiplies the multiplied frequency derived by the specific component extracting means by 1/n to derive the target detection frequency (see FIG. 5).

The biometric information detection system configured as described above continuously collects dynamic data from the monitored subject and its surrounding environment using the sensor unit A from the start of operation, and the analysis unit B sequentially detects one or more vital signs from the dynamic data collected by the sensor unit A and tracks their progression.
In this case, the level recording means extracts level data and displacement data from the dynamic data collected by the sensor unit A and records them in chronological order, the frequency calculation means calculates the frequencies of the frequency components present in the dynamic data from the level data and displacement data, and the frequency recording means records them in chronological order, and the visualization means compiles the level data and the frequency data calculated from the displacement data in chronological order and outputs them on a display screen, paper, etc. as time-level/frequency component time-series characteristics (see Figure 3).

The filtering means removes frequency components other than a specific harmonic band including a specific frequency component previously determined by the specific band selection means, and limits the frequency components to be subjected to subsequent processing.
The harmonics adopted as the specific frequency components are selected and set in advance as described above depending on the environment in which the subject is placed.
For example, when respiration and heart rate are set as targets (vital signs), the filtering means sets reference values of 0.30 Hz to 0.40 Hz for respiration, 1.2 Hz for heart rate, 0.8 mm to 50.0 mm for body movement due to respiration, and 0.1 mm to 5.0 mm for displacement due to heart rate, and sets as the specific harmonic band a band in which frequency components having a frequency that is a multiple of the reference values, a displacement that falls within the range of the reference values, and having level data of the detection level that would be obtained at the distance to the monitored object are suitably manifested.

Next, the specific component extraction means constantly tracks a specific frequency component having a detection level preset by the specific level setting means from the level data of the specific harmonic band, and periodically calculates the average of the frequency data of the specific frequency component, thereby being able to detect fluctuations in the occurrence period of the target via the specific frequency component.
In this case, if an appropriate specific harmonic band is selected by the specific band selection means, the specific frequency component of the target will be detected without being confused with neighboring frequency components, contributing to accurate detection of vital signs without delay for the reasons described below.

That is, even if other frequency components (hereinafter referred to as "similar frequency components") having frequencies close to those of the target coexist in the band surrounding the fundamental frequency including the fundamental frequency of the vital sign (target), in an appropriate specific harmonic band, the harmonic frequencies of both the specific frequency component and the similar frequency component will be relatively separated, making it easy to distinguish between the specific frequency component and the similar frequency component.
Furthermore, in the bands surrounding higher harmonics, the frequency components of movements with unstable frequencies and pulsations located far away with low detection levels are attenuated more as the harmonic band becomes higher, so the S/N ratio when detecting specific frequency components present in the specific harmonic band is significantly improved compared to the S/N ratio when detecting fundamental frequency components present in the bands surrounding the fundamental frequency.

Finally, the target information identification means multiplies the multiplied frequency by 1/n to derive the target detection frequency, and stores this as the subject's vital sign in a storage means, linking it to the individual's ID, and outputting it to a display screen, paper, or another device via an information and communications network.

1 CPU, 2 timer, 3 ROM, 4 RAM, 5 storage unit,
6 input interface, 7 output interface,
8 analysis unit, 9 input control unit, 10 output control unit,

Claims (3)

A biological information detection system for deriving target pulsation information from detected information,
a Doppler sensor that detects motion data in a non-contact manner, the motion data being composed of a displacement detected by using the body of the monitoring target as a reflective surface and a detection level indicating the distance to the monitoring target;
a level recording means for storing the detection level of the motion data in association with the time of detection;
a frequency calculation means for deriving frequency components from the transition of displacement of the dynamic data ;
a frequency recording means for storing the frequency components in association with the time of detection;
a filtering means for detecting frequency components in a specific harmonic band around n (a natural number equal to or greater than 2) multiples of the target pulsation frequency from the frequency components;
a specific component extraction means for detecting a specific frequency component having a predetermined detection level from the frequency components in the specific harmonic band and deriving the frequency;
a target information specifying means for multiplying the frequency of the specific frequency component by 1/n to derive a target pulsation detection frequency;
The biological information detection system further comprises a specific level setting means for setting a range or order of detection levels to be adopted as the specific frequency components in the specific component extraction means. The biological information detection system described in claim 1, characterized in that the specific component extraction means outputs the average value of the frequency of the specific frequency component per unit time. The biological information detection system of either claim 1 or claim 2, characterized in that it comprises a specific band selection means for setting the specific harmonic band.