Biological information detection method and biological information detection system

Abstract

To provide low cost biological information detection method and system capable of drawing accurate biological information from mixed wave forms even when sources of human body movements and vital signs are superposed with any movement and vibration in a life environment.SOLUTION: A biological information detection method and a system thereof include: mapping processing for storing a displacement and a detection level collected by a sensor in a time-series manner; filtering processing for detecting a frequency component in a specific harmonic band in the neighborhood of the frequency n-times (n being a natural number of 2 or more) of a target pulse from the displacement collected by the sensor; specific component extraction processing for detecting a specific frequency component with a predetermined level from the frequency component in the specific harmonic band and drawing the frequency; and target information specification processing for multiplying the frequency of the specific frequency component by 1 / n and drawing a detection frequency of the target pulse.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a biological information detection method and a biological information detection system for detecting the movements and vital signs of a living body in daily life. [Background technology]

[0002] 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 many displacements and vibrations (hereafter referred to as "waveforms") to be detected simultaneously in the human living environment. The detected waveforms can only be processed as information once the target waveform is extracted from among them.

[0003] Given 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 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). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-13479 [Patent Document 2] Japanese Patent Application Laid-Open No. 2013-116216 [Patent Document 3] Japanese Patent Application Laid-Open No. 2016-214876 [Patent Document 4] Japanese Patent Application Laid-Open No. 2017-169867 [Patent Document 5] Japanese Patent Application Laid-Open No. 2007-167545 Summary of the Invention [Problem to be solved by the invention]

[0005] 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 an in-vehicle system, for example, road vibrations and vibrations generated by the vehicle itself, such as the engine, are further superimposed, making it extremely difficult to accurately measure the pulse. Therefore, it has not yet been possible to successfully detect accurate vital signs in a non-contact manner in various high-noise environments, whether at rest or during activity.

[0006] Furthermore, the above analysis method requires a highly capable computing device to perform the analysis precisely, which increases the cost of the entire system, making it unsuitable for widespread use in society as a system for everyday use by individuals.

[0007] The present invention has been made in consideration of the above-mentioned situation, and aims to provide a low-cost biometric information detection method and biometric information detection system that can derive accurate biometric information from mixed waveforms, regardless of any movements or vibrations that are superimposed on the movement of the human body or the source of vital signs in a living environment. [Means for solving the problem]

[0008] The biometric information detection method according to the present invention, which has been developed to solve the above-mentioned problems, is a biometric information detection method that derives information on a target pulsation from detection information from 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 n (a natural number greater than or equal to 2) times the frequency of the target pulsation from the displacement collected by the sensor; a specific component extraction process that detects a specific frequency component having 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.

[0009] The specific component extraction process may employ a method of outputting an average value of the frequency of the specific frequency component per unit time.

[0010] The biological information detection system of the present invention, which has been made to solve the above-mentioned problems, is an information detection system that derives information on a target pulsation from detection information from a sensor, and is characterized by comprising: a level recording means that stores the detection level of the displacement collected by the sensor in association with the detection time; 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 in association with the detection time; a filtering means that detects frequency components in a specific harmonic band around n (a natural number greater than or equal to 2) times the frequency of the target pulsation from the frequency components; a specific component extraction means that detects specific frequency components having 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.

[0011] 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. [Effects of the Invention]

[0012] According to the biometric information detection method and biometric information detection system of the present invention, by adopting a technique of detecting the frequency of the target pulsation after extracting a specific frequency component from a specific harmonic band, the influence of discontinuous or abnormal displacements and distant pulsations with extremely low detection levels is suppressed, and the frequency component of the target pulsation can be accurately detected in a state where it is easy to separate the target frequency component from other frequency components, and the characteristics of the periodic fluctuations and amplitude fluctuations of the pulsation over time can be verified inexpensively and accurately. [Brief explanation of the drawings]

[0013] [Figure 1] 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. [Figure 2] 10 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. [Figure 3] 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. [Figure 4] 10 is a graph showing an example of a Doppler signal visualized in the time-frequency domain and frequency components of n th harmonic extracted by the biological information detection method and biological information detection system according to the present invention. [Figure 5] 10 is a graph showing an example in which a Doppler signal is visualized in the time frequency domain using 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 the 1 / nth band. [Figure 6] 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. [Figure 7] 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. DETAILED DESCRIPTION OF THE INVENTION

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of a biological information detection method and a biological information detection system according to the present invention will be described in detail with reference to the accompanying drawings.

[0015] 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. It 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 non-contact 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), and a communication device that transmits the motion data obtained from the output of the Doppler sensor to the control unit B.

[0016] The sensor unit A is placed, for example, in front of the driver's seat of the vehicle, in a private or shared room, toilet, washroom, kitchen (or kitchen), workshop, bathroom, hallway, etc., so that the life of the monitored person can be seen or grasped, 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).

[0017] 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 Figure 6, is equipped with 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 memory unit 5 that stores various information in a readable and / or writable manner, a user interface or other input interface 6 that accepts input from outside, and an output interface 7 that outputs various information, and is also configured with 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 the output of various information, including a display unit that displays various input and output information (see Figure 6).

[0018] The control unit B includes, as specific means of the analysis unit 8 in which the hardware resources and the bioinformation detection program incorporated therein cooperate, a level recording means for saving level data linking the detection level of motion data collected by a sensor unit A equipped with a Doppler sensor with the detection date and time, a frequency calculation means for deriving the frequency components of the pulsation contained in the displacement data of the motion data collected by the sensor unit A and their frequencies from the time-varying fluctuations in the data, a frequency recording means for saving the frequency data linking the frequency components with the detection date and time, a filtering means for extracting frequency components present in a surrounding band (specific harmonic band) of n (a natural number greater than or equal to 2) times the frequency suitable for detecting the harmonic (specific frequency component) of the target from the frequency components, a specific component extraction means for detecting a specific frequency component with a predetermined level from the frequency components of the specific harmonic band and deriving its frequency (multiplied frequency), and a target information identification means for multiplying the multiplied frequency by 1 / n to derive the detection frequency of the target (see Figure 7).

[0019] 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 in the specific component extraction means (the adoption order 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 with the highest accuracy from a plurality of harmonic bands each divided into frequency bands including any of the second harmonic to the N(max)th harmonic, and setting the selected specific harmonic band as the processing target of the filtering means.

[0020] The level recording means controls the sensor unit A via the input control unit 9, and stores the detection level of the motion data collected by the sensor unit A as level data linked to the detection date and time in the memory unit 5, which consists of a memory IC, a hard disk or removable media, or a recording medium accessible via an information network.

[0021] 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 storage unit 5 as frequency data linked to the detection date and time when the frequency component existed.

[0022] 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, which comprises 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.

[0023] The existence of such a visualization means allows visual examination and verification when considering a harmonic band that is favorable for detecting a specific frequency component, such as whether to select the second harmonic band as the specific harmonic band or another harmonic band under the specific band selection means, or when considering what detection level of pulsation to select in the specific level setting means (for example, when considering which level, from highest to lowest, is the specific frequency component), thereby allowing for more accurate selection.

[0024] Based on the level-frequency distribution map visualized by the three-dimensional distribution display means or its data, the filtering means 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 Figure 4). The width of the specific harmonic band is set as an appropriate bandwidth, for example, a width that includes the fluctuation band of the target reference frequency, or a width that is higher than the n-1th harmonic and lower than the n+1th harmonic that sandwiches the nth harmonic.

[0025] 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.

[0026] The specific component extraction means detects a multiple frequency of a specific frequency component in a specific harmonic band that has been specified as a processing target for the filtering means (see FIG. 4). The specific component extraction means in this example takes into consideration the frequency fluctuation 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).

[0027] 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 time series, 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 time series, and the visualization means compiles the level data and the frequency data calculated from the displacement data in time series and outputs them on a display screen, paper, etc. as time-level-frequency component time series characteristics (see Figure 3).

[0028] 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 the reference values ​​as 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 the specific harmonic band as 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.

[0029] 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.

[0030] 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 are 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.

[0031] Finally, the target information identification means multiplies the multiplied frequency by 1 / n to derive the target detection frequency, and stores it in a memory means as the vital sign of the person being measured, linked to the individual's ID, and outputs it to a display screen, paper, or other device via an information and communication network. [Explanation of symbols]

[0032] 1 CPU, 2 timer, 3 ROM, 4 RAM, 5 memory, 6 input interface, 7 output interface, 8 analysis unit, 9 input control unit, 10 output control unit,

Claims

1. A biological information detection system that derives 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.

2. 2. The biological information detection system according to claim 1, wherein the specific component extraction means outputs an average value of the frequency of the specific frequency component per unit time.

3. 3. The biological information detection system according to claim 1, further comprising a specific band selection means for setting the specific harmonic band.

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