WO2021147901A1 - Bracelet de reconnaissance de pression artérielle - Google Patents

Bracelet de reconnaissance de pression artérielle Download PDF

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Publication number
WO2021147901A1
WO2021147901A1 PCT/CN2021/072876 CN2021072876W WO2021147901A1 WO 2021147901 A1 WO2021147901 A1 WO 2021147901A1 CN 2021072876 W CN2021072876 W CN 2021072876W WO 2021147901 A1 WO2021147901 A1 WO 2021147901A1
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WIPO (PCT)
Prior art keywords
pressure
subject
data
module
signal
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PCT/CN2021/072876
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English (en)
Chinese (zh)
Inventor
赵起超
杨苒
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北京津发科技股份有限公司
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Priority claimed from CN202010066014.9A external-priority patent/CN111184521B/zh
Priority claimed from CN202010066037.XA external-priority patent/CN111248928A/zh
Application filed by 北京津发科技股份有限公司 filed Critical 北京津发科技股份有限公司
Publication of WO2021147901A1 publication Critical patent/WO2021147901A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state

Definitions

  • the invention relates to the technical field of physical sign monitoring, in particular to a pressure recognition bracelet.
  • the vital sign information collected is generally only used to monitor and manage the physiological state of the subject, such as real-time detection of heart rate signals to feed back the heartbeat status.
  • the subject's evaluation of their own physiological state is generally only used to monitor and manage the physiological state of the subject, such as real-time detection of heart rate signals to feed back the heartbeat status.
  • the user's needs for perception of their own physiological state are not limited to the surface content reflected by vital signs data. Emotions, as a unique ideology of human beings, can also be reflected to a certain extent through vital signs data. Especially with regard to the stress state of people, more and more people hope to understand the stress state they are in in the social and work process, so as to make adjustments, but there is no equipment in the prior art that can effectively perform the stress state of the subject evaluation of.
  • the embodiment of the present invention provides a stress recognition bracelet, which is used to solve the defect that the user's emotional stress state cannot be monitored and analyzed in real time in the prior art, and fills the gap in the prior art.
  • a pressure identification bracelet which includes a main casing and a strap connected to the main casing, and further includes:
  • the physiological index collection component is used to detect the vital sign signal of the subject, the vital sign signal includes at least: skin temperature data signal, skin electrical data signal, cortisol data signal, and pulse data signal;
  • the processor module is used to sequentially collect each vital sign signal at a specified interval within a set period to form a discrete-period time-domain signal; and perform short-time Fourier transform and continuous wavelet transform on the time-domain signal to obtain the corresponding
  • the feature sequence to be tested is obtained through similarity matching to obtain the set number of sample feature sequences that are closest to the feature sequence to be tested in the standard pressure database, and the stress levels to which most of them belong are determined as the subject to which the subject belongs Pressure level; wherein the standard pressure database includes at least a plurality of pressure levels and their corresponding sample characteristic sequences;
  • the power supply component is used to supply power to the pressure recognition bracelet.
  • the pressure recognition bracelet further includes an auxiliary recognition module for obtaining the subject’s pulse data signal to be measured when the vital sign signal is damaged or the data is incomplete; obtaining the pulse data signal through the KNN proximity algorithm
  • the second set number of sample pulse data signals closest to the pulse data signal to be measured in the standard pressure database are determined, and the pressure level to which most of them belong is determined as the pressure level to which the subject belongs.
  • the physiological index collection component includes a reflective blood volume and pulse measurement device
  • the reflective blood volume and pulse measurement device includes a primary reflective collection sensor and an auxiliary reflective collection sensor; the primary collection reflective sensor A main emitting light source module and a main receiving light window are provided on the main emitting light source module.
  • the main emitting light source module emits visible infrared light, and the main receiving light window receives the reflected light of the visible infrared light;
  • the auxiliary collecting reflective sensor is provided with an auxiliary emitting A light source module and an auxiliary light receiving window, the auxiliary emitting light source module emits invisible infrared light, and the auxiliary light receiving window receives the reflected light of the invisible infrared light;
  • the collection device is used for detecting the invisible infrared light;
  • the subject taking the center of the acquisition device as the center point, collects the reflected blood volume pulse data on the center point and the points uniformly surrounding the center point, and exercises the collected data Filter removal of violations.
  • the distance between the center point and a point uniformly ringed around the center point is at least 2 cm.
  • it further includes an acceleration sensor for collecting wrist acceleration data signals, and the processor module is further used for judging that the subject is in a resting state or an exercise state according to the acceleration data signals.
  • the processor module is specifically configured to determine whether there is a sudden change in the acceleration data signal in a preset direction, if there is a sudden change, determine that the subject is in motion, and if not, determine that the subject is in motion.
  • the subject is in a resting state; when there is a sudden change, if the sudden change is a spike burr, it is judged to be noise, and the program will not process it, and filtering is used to remove the noise.
  • the stress recognition bracelet further includes a wireless network transmission module for wirelessly connecting to a mobile terminal device and transmitting to the mobile terminal device data containing vital signs signals and the pressure level of the subject Bag.
  • the pressure recognition bracelet further includes a standard pressure database update module, which is connected to the mobile terminal device through the wireless network transmission module, and is used to obtain update data from the mobile terminal device every set time interval.
  • Package updates the standard pressure database.
  • the wireless network transmission module is a mobile data transmission module, a Bluetooth module, a WiFi module and/or a ZigBee wireless transmission module.
  • the standard pressure database further includes heart rate low-frequency power and/or heart rate low-frequency energy density corresponding to the characteristic sequence of each sample, for synchronously outputting the heart rate when determining the stress level to which the subject belongs
  • the low-frequency power and/or the low-frequency energy density of the heart rate are used to evaluate the degree of stress, and the low-frequency power and/or the low-frequency energy density of the heart rate is positively correlated with the degree of stress.
  • the pressure recognition bracelet further includes a display module for displaying the pressure level to which the subject belongs.
  • the pressure recognition bracelet further includes an alarm module, which is used to generate an alarm prompt message when the pressure level of the subject is higher than a set level to prompt the subject to adjust the pressure state.
  • the alarm module is configured to send an alarm reminder to a designated database or network object when the pressure level of the subject is higher than a set level.
  • the power supply assembly includes:
  • DC battery used to store and release DC power
  • the charging component includes a charging interface for wired charging and/or a charging coil for wireless charging.
  • the pressure recognition bracelet of the present invention sequentially collects discrete-period time-domain signals formed by each vital sign signal at a specified interval within a set period, and extracts the subject through short-time Fourier transform and continuous wavelet transform
  • the feature sequence of the vital signs signal to be tested retains the characteristics of multiple vital signs signals, and the similarity matching method is used to obtain the set number of sample feature sequences that are closest to the feature sequence to be tested stored in the standard pressure database.
  • the stress level to which most of them belong is determined as the stress level to which the subject belongs. The effective assessment of the pressure state of the subject is realized, the reliability is high, and the detection speed is fast.
  • FIG. 1 is a schematic diagram of the structure of the pressure recognition bracelet in an embodiment of the present invention
  • FIG. 2 is a structural block diagram of the pressure recognition bracelet in an embodiment of the present invention.
  • FIG. 3 is a structural block diagram of the pressure recognition bracelet in another embodiment of the present invention.
  • FIG. 4 is a structural block diagram of the pressure recognition bracelet in another embodiment of the present invention.
  • FIG. 5 is a structural block diagram of the pressure recognition bracelet in another embodiment of the present invention.
  • FIG. 6 is a structural block diagram of the pressure recognition bracelet in another embodiment of the present invention.
  • FIG. 7 is a schematic diagram of the structure of the main housing part of the pressure recognition bracelet in another embodiment of the present invention.
  • Fig. 8 is a view from the direction A of Fig. 1;
  • FIG. 9 is a schematic diagram of the structure of the physiological index collection component of the pressure recognition bracelet in an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of time-domain signals collected by the processor module in the pressure recognition bracelet in an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of the structure of the reflective blood volume and pulse measuring device in real time according to the present invention.
  • FIG. 12 is a schematic diagram of the time delay of the signals collected by each sensor in the reflective blood volume pulse measurement device in an embodiment of the present invention.
  • connection herein can not only refer to a direct connection, but also an indirect connection in which an intermediate exists.
  • the present invention provides a pressure recognition wristband, which obtains the characteristic sequence to be measured by extracting the characteristics of the collected vital signs signals, and further obtains the set number of the most similar to the characteristic sequence to be measured from the standard pressure database. Sample characteristic sequence, and determine the stress level to which most of them belong as the stress level of the subject. Realize the rapid and accurate judgment of the subject's pressure state.
  • the pressure recognition bracelet includes a main housing 110 and a strap 120 connected to the main housing 110, and further includes:
  • the physiological index collection component 130 is used to detect the vital sign signal of the subject, the vital sign signal includes at least: skin temperature data signal, skin electrical data signal, cortisol data signal, and pulse data signal;
  • the processor module 140 is used to sequentially collect each vital sign signal at a specified interval within a set period to form a discrete-period time-domain signal; and perform short-time Fourier transform and continuous wavelet transform on the time-domain signal to obtain the corresponding
  • the set number of sample characteristic sequences that are closest to the characteristic sequence to be tested in the standard pressure database are obtained through similarity matching, and the stress level to which most of them belong is determined as the stress level of the subject; among them,
  • the standard pressure database includes at least multiple pressure levels and their corresponding sample characteristic sequences;
  • the power supply component 150 is used to supply power to the pressure recognition bracelet.
  • the physiological index collection component 130 mainly includes a variety of physiological sensors, including at least a temperature sensor for detecting skin temperature data signals, a detection electrode for detecting skin electrical data signals, and cortisol data. Cortisol sensor for signal, red light emitting lamp and photoelectric sensor for detecting pulse data signal. In some embodiments, it further includes an acceleration sensor for detecting wrist acceleration data signals.
  • the processor module 140 may be a single-chip microcomputer or other computer storage media that can store and run programs.
  • the processor module 140 may integrate a signal acquisition circuit, and the signal acquisition circuit may include: an amplification circuit and an analog-to-digital conversion circuit.
  • the signal acquisition circuit can also be set separately. It is used to collect the vital sign data signal detected by the physiological index collecting component 130. Specifically, in order to retain the signal characteristics of various physiological data and realize accurate judgment of the emotional stress state of the test subject, it is necessary to comprehensively evaluate and analyze multiple physiological indicators to reduce the impact of specific data on the evaluation of the emotional stress of the test subject. The impact on the judgment result.
  • the subject to be tested when the subject to be tested suffers from a physiological disease or abnormal physical signs, it may induce data such as heart rate, respiration, or skin temperature to be different from the general state. If only a few types of physiological indicators are used for judgment, the evaluation will be inaccurate.
  • the processor module 140 sequentially collects various vital sign signals at specified intervals within a set period to form discrete-period time-domain signals.
  • the object to be measured is sequentially collected the signal voltage value corresponding to each physiological index to obtain a set of discrete periodic signals in the time domain, in a signal cycle
  • Use x(n) to represent the physiological signal sequence to be measured, n 1, 2, 3...n, respectively represent the voltage value of a specific vital sign signal, for example, x(1) can represent the pulse signal voltage value, x(2) Can represent the cortisol signal voltage value.
  • indicators that are highly correlated with emotional changes can be used.
  • they can include: pulse (heart rate), cortisol, skin temperature, skin electricity, blood pressure, acceleration, etc., and can also be selected in the actual selection process
  • Other types of indicators are calibrated and measured to obtain more generalized data.
  • Short-time Fourier transform and continuous wavelet transform are performed on the acquired time-domain signal to obtain the characteristic sequence to be measured.
  • the specific conversion method is as follows:
  • x(n) is the physiological signal sequence to be measured
  • ⁇ (k-n) is the window function
  • k can be set according to the actual application scenario to adjust the quantity issued during the window period
  • e is the natural base.
  • the window function With the change of k, the window function will be shifted on the time axis, and the intercepted part of the window data is left for fast Fourier transform.
  • the window function may adopt a Gaussian function, that is, a Gabor transformation is performed on the physiological signal sequence to be measured, so that the properties of the time axis and the frequency axis after the transformation are symmetrical to each other, so as to obtain a better comparison effect, which is conducive to obtaining More representative feature sequence to be tested.
  • a is the scale factor
  • b is the time shift factor
  • the similarity matching method can be realized by artificial intelligence algorithm, or the uniform approximation function theorem, that is, the weierstrass theorem, can be used for processing.
  • the sample feature sequence that is closest to the feature sequence to be tested the first set number can be an odd number within 20, and the stress level to which most of them belong is determined as the stress level to which the subject belongs.
  • the power supply component 150 can be a 5V DC battery.
  • the pressure recognition bracelet further includes an auxiliary recognition module 160, which is used to obtain the subject’s pulse data signal to be measured when the vital sign signal is damaged or the data is incomplete; through the KNN proximity algorithm Obtain the second set number of sample pulse data signals closest to the pulse data signal to be measured in the standard pressure database, and determine the pressure level to which most of them belong as the pressure level to which the subject belongs.
  • the core idea of the KNN proximity algorithm is that if most of the k nearest samples in the feature space of a sample belong to a certain category, the sample also belongs to this category and has the characteristics of the samples in this category.
  • the auxiliary identification module 160 may adopt a single-chip microcomputer, a CPU, or other storage media capable of storing and executing computer programs, or may be integrated in the processor module 140.
  • the vital signs signal is damaged or the data is incomplete, the effective feature sequence to be measured cannot be calculated.
  • the KNN proximity algorithm is used to obtain and The second set number of sample pulse data signals closest to the pulse data signal to be measured, the second set number can be set to an odd number less than 20, and most of the pressure levels are determined as the pressure levels to which the subject belongs.
  • the collected pulse signal may have noise pollution, which affects the judgment of the subject's stress level.
  • the collected pulse wave will be interfered by the movement. Therefore, it is necessary to filter the collected pulse wave. Since the subject is at rest, it will not be disturbed by the movement.
  • the acquisition of the pulse signal of the subject in the exercise state will be described in detail.
  • the acceleration sensor in the physiological index collection component 130 collects the acceleration data signal of the subject's wrist, and transmits the acceleration data signal to the processor module 140, and the processor module 140 determines the subject according to the acceleration data signal.
  • the participant is in a resting state or in a state of exercise. Specifically, if there is a sudden change in the acceleration data signal in the preset direction, it is determined that the subject is in a motion state; if there is no sudden change in the acceleration data signal in the predetermined direction, it is determined that the subject is in a resting state.
  • the acceleration sensor can be used as the coordinate origin to establish a three-dimensional space coordinate system. It can be determined whether there is a sudden change in the components of the acceleration signal data on the x-axis, y-axis, or z-axis. Determined to belong to noise, the program does not process, use filtering to remove the noise, (for example, the component of acceleration signal data on a certain coordinate axis suddenly increases or decreases), through the nature of acceleration component data increase or decrease Determine whether the subject is in motion.
  • the physiological index collection component 130 includes a reflection-type blood volume and pulse measurement device, which is connected to the processor module 140 for collecting blood volume and pulse information and sending it to the processor module 140.
  • the reflection-type blood volume pulse measurement device may include a main reflection-type acquisition sensor and four auxiliary reflection-type acquisition sensors.
  • the main reflection-type acquisition sensor 1301 and the four auxiliary reflection-type acquisition sensors 1302 can be set up.
  • Adopt the main reflection type acquisition sensor is set in the center position, and the four auxiliary reflection type acquisition sensors are uniformly distributed around the main reflection type acquisition sensor in a ring shape.
  • auxiliary reflective collection sensors are only an example and cannot be used to limit this embodiment.
  • the number of auxiliary reflective collection sensors may also be other numbers, such as six, seven, or eight.
  • the distance between the main reflective collection sensor and any other auxiliary collection reflective sensor is equal and at least 2 cm, so that mutual interference between them can be effectively avoided, which is more conducive to data analysis.
  • the main collection reflective sensor is provided with a main emitting light source module and a main receiving light window.
  • the main emitting light source module is used to emit visible infrared light
  • the main receiving light window is used to receive reflected light of visible infrared light.
  • the auxiliary collection reflective sensor is provided with an auxiliary emitting light source module and an auxiliary receiving light window.
  • the auxiliary emitting light source module is used for emitting invisible infrared light
  • the auxiliary receiving light window is used for receiving reflected light of invisible infrared light.
  • the main collection reflective sensor is easily interfered by external light sources.
  • invisible infrared light can be used.
  • the visible red light of the main collection reflective sensor uses visible red light with a wavelength of 850 nm for data collection, while the main collection reflective sensor uses invisible infrared light with a wavelength of 770 nm for data collection.
  • the auxiliary collection reflective sensor has less influence on the measurement of the main collection reflective sensor.
  • both the main receiving light window and the auxiliary receiving light window can receive the light reflected from the blood vessel, and output the corresponding analog signal according to the intensity of the reflected light, so as to reflect the magnitude of the analog signal The intensity of the reflected light signal.
  • the processor module 140 after the processor module 140 receives the subject's pulse data measured by the reflective blood volume pulse measurement device, it also needs to perform noise reduction processing on the pulse data.
  • the pulse data can be filtered to remove motion violations to achieve the effect of noise reduction.
  • the pulse data can be amplified by the amplifier circuit, and the data below 0.16 Hz can be filtered out, and then the remaining data can be quantified.
  • a high-precision ADC 24BIT
  • Analog-to-digital conversion to quantify the data.
  • variable step size LMS adaptive filtering algorithm and the data shifting and superimposing algorithm are used to remove the motion violation.
  • the LMS algorithm refers to the minimum mean square error adaptive algorithm, and its expression is as follows:
  • the filter output function is y(k), and the expected signal function is d(k);
  • the minimum mean square value f(e(k)) of the error signal is the smallest, then the output y(x) of the filter must be closer to the expected signal function as d(x); then the selection of the step size factor u is a key Parameters.
  • the mean square error performance surface has only one unique minimum value, if the main step length u is selected appropriately, it can converge to the small point of the error surface.
  • This method is called the objective function gradient inversion method, and the expression is as follows:
  • the maximum value of u calculated is 1/(2
  • the step size at this time can take into account both the convergence speed and the steady-state offset error.
  • X(k) is the acceleration data signal input
  • the collected acceleration signal value is X+Y+Z
  • the signal output by the filter is y(k);
  • the filtering process of each reflection module is to remove motion violations according to the above method
  • the following is an algorithm for superimposing and shifting the data of the five sensors (the main reflective acquisition sensor and the four auxiliary reflective acquisition sensors).
  • the pressure recognition bracelet further includes a wireless network transmission module 170, which is used to wirelessly connect to a mobile terminal device (not shown in the figure), and transmit to the mobile terminal device signals containing vital signs and The data package of the stress level to which the subject belongs.
  • a wireless network transmission module 170 which is used to wirelessly connect to a mobile terminal device (not shown in the figure), and transmit to the mobile terminal device signals containing vital signs and The data package of the stress level to which the subject belongs.
  • the pressure identification bracelet is wirelessly connected to the mobile terminal device.
  • the mobile terminal device may include a mobile phone, a tablet computer, or a PC computer.
  • the wireless network transmission module 170 transmits data packets containing vital signs signals and the pressure level of the subject to the mobile terminal device for saving and recording data, and at the same time completing status feedback.
  • the vital sign signals in the data packet may be further processed to obtain evaluation information of the corresponding physiological state.
  • the pressure recognition bracelet further includes a standard pressure database update module 180, which is connected to the mobile terminal device through the wireless network transmission module 170, and is used to obtain update data from the mobile terminal device every set interval.
  • Package updates the standard pressure database.
  • the data packet containing the new sample characteristic sequence is obtained from the mobile terminal device every 24 hours, and the new sample characteristic sequence is added to the standard pressure database stored in the pressure recognition bracelet.
  • the wireless network transmission module 170 may adopt a mobile data transmission module, a Bluetooth module, a WiFi module, and/or a ZigBee wireless transmission module.
  • the standard pressure database further includes heart rate low-frequency power and/or heart rate low-frequency energy density corresponding to each sample feature sequence, which is used to synchronously output the heart rate low-frequency power and/or heart rate when determining the stress level to which the subject belongs Low-frequency energy density to evaluate the degree of stress, low-frequency power and/or heart rate low-frequency energy density is positively correlated with the degree of stress.
  • the pulse PPG signal its frequency characteristics are: low frequency range 0-0.04 Hz, middle frequency range 0.04-0.15 Hz, high frequency range 0.15-0.4 Hz; heart rate low frequency power LF means that the pulse data signal is in 0-0.04Hz low frequency power, heart rate high frequency power HF refers to the power of pulse data signal in the high frequency range of 0.15-0.4Hz, heart rate low frequency energy density refers to LF/HF, heart rate low frequency power LF and heart rate low frequency energy density LF/ HF can reflect the pressure state, where the greater the value of LF and LF/HF, the greater the pressure.
  • the pulse PPG signal in the time domain can be converted into a frequency domain signal by Fourier transform, combined with the frequency characteristics of the pulse PPG signal, the low frequency band 0-0.04 Hz and the high frequency band at 0.15-0.4 Hz are integrated and summed, Obtain the corresponding heart rate low-frequency power LF and heart rate high-frequency power HF.
  • the pressure recognition bracelet further includes a display module (not shown in the figure) for displaying the pressure level to which the feedback subject belongs.
  • a display module is set to display the evaluated stress level of the subject in real time, so as to achieve the effect of timely feedback.
  • the display module may adopt an LCD display or other types of display devices, and may also adopt an indicator light assembly.
  • one or more specific vital signs can be displayed simultaneously while displaying the stress level of the subject.
  • the pressure recognition bracelet further includes an alarm module 190, which is used to generate an alarm prompt message when the pressure level of the subject is higher than the set level to prompt the subject to adjust State of stress.
  • an alarm module 190 which is used to generate an alarm prompt message when the pressure level of the subject is higher than the set level to prompt the subject to adjust State of stress.
  • the alarm prompt information is directly responded to at the end of the pressure recognition bracelet, including generating sound prompts and displaying prompts.
  • the wireless network transmission module 170 may also send alarm prompt information to the mobile terminal device for alarm prompt.
  • the alarm module 190 sends an alarm reminder to a designated database or network object when the pressure level of the subject is higher than the set level.
  • the designated database can be a cloud server platform or an information receiving and storing platform independently set by the subject.
  • the network object can be a designated account under various social platforms, such as WeChat and QQ users; it can also be a designated phone number under the platform of a mobile network operator.
  • the database or network object can be associated with the subject or a third-party counterpart.
  • the power supply component 150 includes:
  • DC battery (not shown in the figure), used to store and release DC power
  • the charging assembly (not shown in the figure) is used to charge the DC battery, and includes a charging interface for wired charging and/or a charging coil for wireless charging.
  • the voltage and capacity of the DC battery can be set according to actual application requirements, and the charging component is equipped with hardware devices in two forms of wired charging and wireless charging to meet the needs of multiple application scenarios in modern society.
  • the pressure recognition watch includes a main casing 110 and a strap 120 connected to the main casing 110; as shown in FIG. 7, a physiological index collection component 210 is provided at the bottom of the main casing 110, A power switch 220 is provided on the side of the main housing 110; as shown in Figures 8 and 9, the physiological index collection component 130 is provided with at least a skin temperature collection window 211, a cortisol collection window 212, and a blood volume pulse collection window 213;
  • the pulse collection window 213 also includes a light emission window (not marked in the figure) and a light receiving window (not marked in the figure), and blood pressure and respiration values can be indirectly calculated through the collection of blood volume pulse signals.
  • the skin temperature collection window 211 is provided with an infrared thermocouple sensor (not shown in the figure)
  • the cortisol collection window 212 is provided with a cortisol sensor (not shown in the figure)
  • the blood volume pulse collection window 213 is provided with an infrared pulse Sensor (not shown in the figure)
  • the strap 120 is provided with a skin electrode 310.
  • the main casing is provided with a power supply assembly, a wireless network transmission module, a processor module, and an acceleration sensor.
  • the infrared thermocouple sensor, the cortisol sensor, the infrared pulse sensor, the skin electrode 310 and the acceleration sensor are respectively connected to the power supply component and the processor module.
  • the processor module is also connected to the wireless network transmission module.
  • the infrared thermocouple sensor adopts a digital signal sensor, and uses infrared thermocouple technology, which can measure a very accurate temperature without touching human skin.
  • the infrared thermocouple sensor adopts I2C (Inter-Integrated Circuit) bus communication mode, and sends the measured data to the processor module for data storage for use;
  • the electrical skin electrode 140 starts to collect electrical skin data after being energized.
  • the electric skin electrode 140 is connected to the input port of the signal acquisition circuit, after the follower removes the interference of the previous stage, the signal is amplified by 10 times through the amplifier circuit, and then the 50Hz power frequency signal is filtered through the power frequency notch filter to enter
  • the amplifying circuit performs 5 times amplifying, the amplified signal enters the small signal processor, the Bessel filter performs data processing, and the processed data enters the analog-to-digital converter for data conversion and sends it to the processor module, such as a single-chip microcomputer, for storage. use.
  • the infrared pulse sensor uses the principle of reflecting light flux to detect the pulse wave.
  • the principle is that every time the heart contracts and relaxes, it will bring about the contraction and expansion of blood vessels.
  • the light flux signal received by the receiving device is the smallest, and vice versa, the light flux signal is the largest.
  • the original data of the pulse waveform is also analog data.
  • the follower filters the interference of the previous stage, it is amplified 20 times by the amplifier circuit, and then the power frequency interference of 50Hz is filtered out, and then the second stage amplification is performed, and the amplification is 5 times.
  • the amplified signal passes through the analog-to-digital converter, the data is sent to a processor module, such as a single-chip microcomputer, for storage to be used.
  • the cortisol sensor adopts non-invasive measurement, analyzes the chemical composition of sweat secreted by the human body, and then sends the data of the analysis result to the single-chip microcomputer for storage and standby;
  • the acceleration sensor is a digital sensor
  • the measured signal is a data signal, which is read and stored for use through a SPI (Serial Peripheral Interface) communication method.
  • SPI Serial Peripheral Interface
  • the processor module collects the discrete-period time-domain signals formed by each vital sign signal in a set period at a specified interval time, and performs short-time Fourier transform on the time-domain signals And continuous wavelet transform to get the corresponding feature sequence to be measured. Obtain the sample characteristic sequence stored in the local standard pressure database that is closest to the characteristic sequence to be tested by means of similarity comparison, and determine the subject as the pressure level corresponding to the closest sample characteristic sequence.
  • the detected vital signs signals can also be sent to a mobile terminal device, such as a mobile phone or a tablet, and the mobile terminal device uploads the obtained data to a cloud server via a wireless network, such as GPRS, 4G, 3G , 2G, WIFI, the cloud server evaluates the emotional stress of the object to be tested by the same method as described above, and returns the evaluation result to the mobile device and stores it.
  • a mobile terminal device such as a mobile phone or a tablet
  • the mobile terminal device uploads the obtained data to a cloud server via a wireless network, such as GPRS, 4G, 3G , 2G, WIFI
  • the cloud server evaluates the emotional stress of the object to be tested by the same method as described above, and returns the evaluation result to the mobile device and stores it.
  • the pressure recognition watch of the present invention sequentially collects the discrete-period time-domain signals formed by each vital sign signal at a specified interval within a set period, and extracts them through short-time Fourier transform and continuous wavelet transform
  • the test feature sequence of the subject’s vital sign signal retains the characteristics of multiple vital sign signals, and the similarity matching method is used to obtain the set number of samples stored in the standard pressure database that are closest to the test feature sequence.
  • Feature sequence and determine the stress level to which most of them belong as the stress level to which the subject belongs.
  • the effective assessment of the pressure state of the subject is realized, the reliability is high, and the detection speed is fast.
  • the program or code segment may be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link through a data signal carried in a carrier wave.
  • Machine-readable medium may include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and so on.
  • the code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.
  • the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices.
  • the present invention is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be different from the order in the embodiments, or several steps may be performed at the same time.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Veterinary Medicine (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Public Health (AREA)
  • Animal Behavior & Ethology (AREA)
  • Physics & Mathematics (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Child & Adolescent Psychology (AREA)
  • Developmental Disabilities (AREA)
  • Educational Technology (AREA)
  • Hospice & Palliative Care (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Abstract

L'invention concerne un bracelet de reconnaissance de pression artérielle, comprenant un boîtier principal (110), un bracelet de montre (120) relié au boîtier principal (110), un ensemble de collecte d'indices physiologiques (130), un module processeur (140) et un ensemble d'alimentation (150), le module processeur (140) étant destiné à collecter séquentiellement, à intervalles déterminés et dans une période définie, des signaux de domaine temporel de périodes discrètes formées par divers signaux de signes vitaux. Des séquences de caractéristiques à tester des signaux de signes vitaux d'un individu sont extraites au moyen d'une transformée de Fourier à court terme et d'une transformée en ondelettes continue, de sorte que les caractéristiques de divers signes vitaux soient conservées. Un nombre défini de séquences de caractéristiques d'échantillon qui sont stockées dans une base de données de pression standard et qui sont les plus proches desdites séquences de caractéristiques sont acquises par mise en correspondance de similitudes, et le niveau de pression auquel se trouve la plupart des séquences de caractéristiques d'échantillon est déterminé comme étant le niveau de pression auquel se trouve l'individu. La présente invention permet ainsi de réaliser l'évaluation efficace de l'état de pression de l'individu, avec une crédibilité et une vitesse de détection élevées.
PCT/CN2021/072876 2020-01-20 2021-01-20 Bracelet de reconnaissance de pression artérielle WO2021147901A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN202010066014.9A CN111184521B (zh) 2020-01-20 2020-01-20 一种压力识别手环
CN202010066037.X 2020-01-20
CN202010066014.9 2020-01-20
CN202010066037.XA CN111248928A (zh) 2020-01-20 2020-01-20 压力识别方法及装置

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Publication number Priority date Publication date Assignee Title
CN103584872A (zh) * 2013-10-29 2014-02-19 燕山大学 一种基于多生理参数融合的心理压力评估方法
US20150245777A1 (en) * 2012-10-19 2015-09-03 Basis Science, Inc. Detection of emotional states
US9307908B2 (en) * 2012-10-30 2016-04-12 Vital Connect, Inc. Measuring psychological stress from cardiovascular and activity signals
CN107392124A (zh) * 2017-07-10 2017-11-24 珠海市魅族科技有限公司 情绪识别方法、装置、终端及存储介质
US20180184901A1 (en) * 2017-01-05 2018-07-05 The Trustees Of Princeton University Stress detection and alleviation system and method
CN111184521A (zh) * 2020-01-20 2020-05-22 北京津发科技股份有限公司 一种压力识别手环
CN111248928A (zh) * 2020-01-20 2020-06-09 北京津发科技股份有限公司 压力识别方法及装置

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150245777A1 (en) * 2012-10-19 2015-09-03 Basis Science, Inc. Detection of emotional states
US9307908B2 (en) * 2012-10-30 2016-04-12 Vital Connect, Inc. Measuring psychological stress from cardiovascular and activity signals
CN103584872A (zh) * 2013-10-29 2014-02-19 燕山大学 一种基于多生理参数融合的心理压力评估方法
US20180184901A1 (en) * 2017-01-05 2018-07-05 The Trustees Of Princeton University Stress detection and alleviation system and method
CN107392124A (zh) * 2017-07-10 2017-11-24 珠海市魅族科技有限公司 情绪识别方法、装置、终端及存储介质
CN111184521A (zh) * 2020-01-20 2020-05-22 北京津发科技股份有限公司 一种压力识别手环
CN111248928A (zh) * 2020-01-20 2020-06-09 北京津发科技股份有限公司 压力识别方法及装置

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