CN113693571A - Non-contact physiological signal monitor - Google Patents
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Abstract
The invention belongs to the technical field of physiological signal detection, and particularly relates to a non-contact physiological signal monitor. The invention comprises a physiological signal detection device, a human face detection device, a tracking and positioning mechanism and an upper computer; the physiological signal detection device comprises a laser emission module, an optical signal detection conversion module and a signal processing algorithm; the face detection device comprises an infrared camera and a face detection algorithm; the tracking and positioning structure comprises a slide rail and a cradle head built by a steering engine; the laser emission module, the optical signal detection conversion module and the infrared camera are arranged on the holder; the signal processing algorithm and the face detection algorithm are deployed in the microprocessor; the human face image is collected in real time through the infrared camera, the microprocessor controls the holder to adjust the laser emission angle to irradiate aiming at the forehead of the human brain, the optical signal detection processing module detects reflected light, and the microprocessor calculates physiological parameters according to the detected reflected light signal; data are transmitted to the upper computer through Bluetooth.
Description
Technical Field
The invention belongs to the technical field of physiological signal detection, and particularly relates to a non-contact physiological signal monitor.
Background
With the continuous development of economy, the problem of aging of the population becomes more and more prominent. In the climax of social development, the lifestyle of the young generation is also slowly changing. These factors lead to a continuous increase in the incidence of cerebrovascular disease and a trend toward younger life. Without effective detection and prevention measures, not only are life and health of patients seriously threatened, but also a heavy economic and social burden is brought to families and even society of the patients [1-2 ]. Therefore, the continuous exploration on the internal mechanism of the brain continuously overcomes the difficulties in brain science research, and seeks early discovery and early treatment of brain diseases on the basis, thereby having great medical significance and economic and social benefits. Approximately one third of a person's lifetime spends sleeping. The probability of a brain disease onset at night is very high. In order to improve the life quality of people, sleep monitoring at night is a very significant thing. Cerebral blood oxygen, heart rate, cerebral vascular tone and the onset of cerebral diseases are closely related. Monitoring these three physiological signals allows for assessment and early warning of disease.
In recent years, with the continuous development of sensor technology, monitoring technology and modern imaging technology, there are more and more brain function monitoring means, such as:
1. electroencephalogram
The electroencephalogram (EEG) can reflect the spontaneous and rhythmic electrical activities of brain cell populations, and has important application values in the aspects of diagnosis, disease monitoring, prognosis judgment, clinical treatment and the like of brain diseases. Electroencephalogram monitoring equipment commonly used clinically mainly includes conventional electroencephalography (RECG), video electroencephalography (VEEG), dynamic electroencephalography (AEEG), and intracranial electroencephalography (ieg).
2. Near infrared spectrometer (NIRS)
NIRS has the following principle: near infrared light is emitted to the head at a specific location and undergoes random scattering and absorption processes in the tissue, attenuating by 7-9 orders of magnitude. Where a portion of the light travels back through the banana-shaped path to the tissue surface and is then detected by a sensitive photodetector. The concentration of oxyhemoglobin and the concentration of deoxygenated hemoglobin are obtained by detecting the intensity of incident light and transmitted light and converting through Beer-Lanbert law, and the brain blood flow and the brain blood volume are calculated through the change of the blood oxygen saturation and the change of the concentration of oxyhemoglobin and deoxygenated hemoglobin. NIRS was introduced in 1985 into clinical practice for assessing brain oxygenation in premature infants, and has been applied with constant improvement to monitoring in various surgical anaesthesia. However, due to individual basic differences, NIRS monitoring cannot determine an absolute threshold of cerebral hypoxia, currently clinically used equipment can only monitor relative changes of cerebral blood oxygen, and cerebral blood oxygen saturation monitoring belongs to a trend index, cannot diagnose causes of diseases, and only has a warning effect.
In addition to the two categories mentioned above, there are also known CT (computed tomography), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI). At present, various methods and devices have been developed in the field of monitoring brain electricity, brain blood oxygen and brain blood flow, so as to greatly promote the development of brain science, but the existing devices still have certain limitations, and the summary is as follows:
1. the sensitivity of electroencephalogram monitoring is lower than the cerebral blood oxygen saturation, and professional doctors are required to analyze;
2. the CT equipment has large volume and ionizing radiation, and also needs professional doctor operation and cannot be monitored in real time;
3. the PET equipment is very expensive, large in size, needs to be operated by a special doctor and cannot be monitored in real time;
4. MRI equipment is expensive and large in size, is rarely used in general primary hospitals, needs to be operated by special doctors and cannot be monitored in real time;
due to the shortcomings of the above devices, a variety of portable brain function monitoring devices have been developed on the market. These devices have the following drawbacks:
1. most portable instruments are tightly attached to the head, and the comfort level and the usability are not high.
2. At present, a non-contact instrument based on a camera and a non-contact instrument based on near infrared light have no automatic tracking function. These instruments do not allow accurate, real-time monitoring of physiological signals due to the movement of the human head.
Aiming at the problems, the invention provides a non-contact physiological signal monitoring instrument capable of tracking and positioning a human face. The invention can track human face, accurately irradiate laser on the forehead of the brain of a tested person and realize accurate detection.
Reference documents:
[1]Edwards A.D et al.Cotside measurement of cerebral blood flow in ill newborn infants by near infrared spectroscopy[J].1988,332(8614):770-771.
[2]Wyatt J S et al.Quantitation of cerebral blood volume in human infants by near-infrared spectroscopy[J].Journal of applied physiology(Bethesda,Md.:1985),1990,68(3):1086-91.
[3] riqu, Song Yu, Deng Jianqi, Liumin, Chen Zhong Chen Ling, Zhou Ji Liu.
Disclosure of Invention
The invention aims to provide a non-contact physiological signal monitor which has a face tracking and positioning function and can accurately acquire signals.
The invention provides a non-contact physiological signal monitor, comprising: the system comprises five parts, namely a physiological signal detection device, a human face detection device, a microprocessor, a mechanical structure for tracking and positioning and an upper computer for data display; wherein:
the physiological signal detection device comprises: the device comprises a laser emission module, an optical signal detection and conversion module and a signal processing algorithm module; wherein:
the laser emitting module is a laser emitter and is used for emitting laser;
the optical signal detection and conversion module comprises a photoelectric conversion circuit, an automatic gain circuit, a phase-locked amplification circuit and a programmable filter circuit.
(1) The photoelectric conversion circuit is mainly used for generating 0.1V to 4.9V output voltage for the photodiode current of 0uA to 5.43 uA.
(2) The automatic gain circuit adopts a PGA281 chip and is used for providing the lowest gain of 0.125 times and the highest gain of 176 times;
(3) the phase-locked amplification circuit (also called a phase detector) is an amplifier which can separate a signal with a specific carrier frequency from an environment with extremely high interference (the signal-to-noise ratio can be as low as-60 dB or even lower).
(4) The programmable filter circuit adopts a filter constructed by MAX 262. Two independent program-controlled filters are arranged inside the MAX 262; wherein, the low-pass, the high-pass, the band-stop, the full-pass, the central frequency and the Q value can be adjusted by the processor
The signal processing algorithm module comprises an algorithm for calculating the blood oxygen saturation and a peak detection algorithm for calculating the heart rate. These algorithms are deployed in a microprocessor. After the microprocessor collects the original signals, the algorithms are called for calculation, and the calculated values are sent to an upper computer through Bluetooth.
(II) the face detection device comprises: infrared camera, human face detection algorithm; the infrared camera is used for capturing and collecting face information at night. The human face detection algorithm mainly collects human face information according to the infrared camera, calculates the projection distance of the human face image in the sub-face space, and judges that the image is a corresponding human face if the image meets the threshold comparison condition. Specifically, the face judgment is realized by calling opencv-python and numpy libraries through a microprocessor; the face detection algorithm is deployed in a microprocessor.
(III) the microprocessor adopts STM32F4, deploys a signal processing algorithm and a face detection algorithm; the microprocessor calls a face detection algorithm to judge the face, controls the slide rail and the holder, adjusts the laser emission angle and irradiates the forehead of the brain; after the microprocessor collects the original signal, a signal processing algorithm is called for calculation, and the calculated value is sent to an upper computer through Bluetooth;
(IV) the mechanical structure for tracking and positioning comprises: the device comprises a cradle head, an arc-shaped sliding rail and a motor which are built by a steering engine. Two ends of the arc-shaped slide rail are fixed on two sides of the bed corresponding to the position of the head of a person, the arc-shaped track surrounds the position of the head of the person, and the holder can move along the arc-shaped track; the holder is driven by a motor and is controlled to move on the arc-shaped slide rail. The laser emission module (laser emitter), the optical signal detection conversion module and the infrared camera are arranged on the holder;
after the infrared camera arranged on the holder acquires the face information, the microprocessor drives the motor to lock the irradiation position of the laser emitted by the laser emitter arranged on the holder on the forehead of the testee. Thus, even if the head of the testee moves, the laser can be accurately irradiated on the relevant part, and accurate measurement can be carried out.
And (V) the upper computer performs data interaction with the microprocessor through Bluetooth. The upper computer can perform initial analysis on the original signal, display the waveform of the physiological signal in real time and control the relevant actions of the instrument. The hardware system transmits the acquired and processed digital physiological electric signals back to an upper computer at the PC end through a serial port communication module, and the upper computer draws an original data oscillogram of the received data in real time and stores the data in a data buffer area for data transfer.
The working process of the non-contact physiological signal monitor comprises the following steps:
(1) the motor and the pan-tilt are driven by the microprocessor. And starting an infrared camera on the holder to capture the face. The microprocessor controls the motion of the steering engine in the x direction and the y direction respectively by two layers of circulation, and the two-dimensional motion of the camera is realized. Once the face is recognized, the face capturing mode is exited, the face following mode is entered, and the camera is made to move along with the face;
(2) because laser emitter and infrared camera all dispose on the cloud platform, when the camera followed the people's face and moved, laser emitter also can follow people's face and move.
(3) After the laser irradiates the forehead of the brain, the light is scattered and refracted on the cerebral cortex in a banana-shaped path. And the light signal detection and conversion module on the holder converts the reflected light into a voltage signal. The microprocessor calculates the cerebral blood oxygen, the heart rate and the cerebral vascular tension according to the voltage signals.
Compared with the prior art route, the invention has the beneficial effects that:
1. under the non-contact condition, the invention realizes the acquisition of three physiological signals of brain blood oxygen, heart rate and cerebral vascular tension.
2. In night sleep monitoring, the invention can automatically track and position the position of the human face, thereby adjusting the emission angle of the optical signal and accurately acquiring the signal in real time.
3. The invention has moderate price, is convenient to install and is suitable for common household monitoring.
Drawings
Fig. 1 is a system overall framework diagram.
Fig. 2 is a laser driving circuit.
Fig. 3 is a photoelectric conversion circuit.
Fig. 4 shows PGA281 gain control.
Fig. 5 is a specific flow inside the optical signal detection and conversion module.
Fig. 6 is a schematic structural view.
FIG. 7 is a host computer interface.
Fig. 8 is the original signal.
Detailed Description
Fig. 1 is a schematic diagram of an embodiment of the present invention, and the present invention will be further described with reference to the following embodiment and the accompanying drawings.
The invention provides a non-contact physiological signal monitor, comprising: the system comprises five parts, namely a physiological signal detection device, a human face detection device, a microprocessor, a mechanical structure for tracking and positioning and an upper computer for data display; wherein:
the physiological signal detection device comprises: the device comprises a laser emission module, an optical signal detection and conversion module and a signal processing algorithm module; wherein the content of the first and second substances,
the laser emitting module is a laser emitter and is used for emitting laser. As shown in fig. 2, the specific driving circuit includes two transistors Q1 and Q2, and six peripheral resistors R40, R41, R42, R43, R44, and R45. The two transistors Q1 are identical to the peripheral circuits of Q2, so that only one of the two circuits is described. For example: the circuit composed of Q1, R40, R41 and R42 is as follows, a resistor R40 is connected to the base of a triode Q1, a resistor R42 is connected to the collector of the triode Q1, and R41 is connected between the emitter and the base of the triode. When the base electrode of the triode Q1 is at a high level, the triode is conducted, and laser lights; when the base of the transistor Q1 is low, the transistor is turned off and the laser is extinguished. In order for the instrument to achieve high accuracy, the intensity of the laser should be kept as constant as possible and independent of variations in the supply voltage and temperature. In addition, a PWM (pulse width modulation) wave is employed as the laser drive signal. The design of the circuit is compatible with PWM driving.
The optical signal detection and conversion module comprises a photoelectric conversion circuit, an automatic gain circuit, a phase-locked amplification circuit and a programmable filter circuit.
(1) The design goal of the photoelectric conversion circuit is to generate an output voltage of 0.1V to 4.9V for a photodiode current of 0uA to 5.43 uA.
Specific circuitry as shown in fig. 3, this circuit includes a single power operational amplifier (AD8605) configured as a transimpedance amplifier having a bandwidth greater than 1MHz for amplifying the optically dependent current of a photodiode (VEMD 5060). The current model IPD and the capacitor CJ are connected in parallel to form a simulation model of the photodiode (VEMD 5060); the capacitor C1 and the resistor R1 are connected in parallel and bridged at the reverse input end and the output end of the operational amplifier to form a feedback network; the resistors R2 and R3 form a voltage divider circuit that divides the voltage of the voltage source V1 to provide a low bias voltage to the non-inverting input of the operational amplifier. This may prevent the output from saturating on the negative supply rail in the absence of input current; in the circuit, a resistor R4 and a capacitor C3 form a passive filter for filtering high-frequency noise.
(2) The automatic gain circuit is designed by adopting a PGA281 chip. Since PGA281 is a programmable chip whose key circuits are highly integrated by manufacturers, it is only necessary to care about the connection of external pins of PGA281 and then program MCU drivers. FIG. 4 shows the relationship between the PGA281 pin and the output gain, and the gain can be changed by the MCU changing the high and low levels of the G4-G0 pins. As can be seen from fig. 4: PGA281 can provide 0.125 times gain at the lowest, up to 176 times.
(3) The phase-locked amplifying circuit is realized by adopting an AD630 chip and a low-pass filter circuit. The AD630 is a high-precision balanced modulator, and the internal resistors are all high-stability thin film resistors, so that the working accuracy and stability of the AD630 are ensured. The lock-in amplifier is actually an analog fourier transformer, and the output signal passes through a low-pass filter circuit to obtain a dc voltage proportional to the amplitude of a signal at a specific frequency (the parameter input frequency) in the input signal. While other frequency components in the input signal do not contribute to the output voltage. The low-pass filter circuit is implemented using a programmable filter circuit as mentioned below.
(4) The programmable filter circuit adopts a filter constructed by MAX 262. Two independent program-controlled filters are arranged inside the MAX 262; wherein, the low-pass, the high-pass, the band-stop, the full-pass, the central frequency and the Q value can be adjusted by the processor.
The present invention relates to two programmable filter circuits. A programmable filter circuit is designed as a low-pass filter circuit with the cut-off frequency of 0.2HZ, and forms a phase-locked amplifier with an AD630 chip for extracting the cerebral blood oxygen signal; the other programmable filter circuit is designed as a band-pass filter with a frequency range of 0.2Hz-2Hz for extracting the heart rate signal and the cerebrovascular tension signal.
The signal processing algorithm module is deployed in a microprocessor and comprises brain blood oxygen saturation calculation, heart rate peak calculation and cerebral vascular tension calculation. The microprocessor converts the original signal into a digital signal through an ADC (analog signal to digital signal), and then calls a signal processing algorithm to calculate.
In performing the blood oxygenation calculation, the radiation transfer equation gives the spatial derivative of the absorption after obtaining the intensity of the reflected light at different distances, assuming that the scattering coefficient is constant and known. In combination with fick's diffusion law, the following analytical solution can be obtained:
where λ is the wavelength of light, μaIs the absorption coefficient of the medium, ρ is the distance between the light source and the detector, k is a constant, and h is the normalized slope between the scattering coefficient and the wavelength. A is the optical density, defined as:
wherein, IoAnd I are the light intensity of the origin and the distance ρ, respectively. Absorption coefficient muaCan be expressed as:
wherein epsilonHbAnd
as a specific extinction coefficient, cHbAnd
the molarity of the deoxygenated hemoglobin and deoxygenated hemoglobin, respectively. For two wavelengths, this equation can be rewritten as:
brain blood oxygen concentration may be expressed as:
when calculating the heart rate, a peak detection algorithm is adopted to calculate the period between two signal peaks, so as to obtain the heart rate.
When the cerebral vascular tension is calculated, the magnitude of the cerebral vascular tension is reflected by calculating the magnitude of the signal peak value.
Further, a specific flow inside the optical signal detection and conversion module is shown in fig. 5:
the photodiode converts the optical signal into corresponding current, and converts the current signal into a voltage signal by combining with a photoelectric conversion circuit;
the voltage signal enters an automatic gain circuit to be amplified. Because the photodiodes have different response degrees to optical signals with different wavelengths, the voltage signals converted from the optical signals with different wavelengths have different magnitudes, and in order to make the circuit have a relatively large dynamic range, the optical signals with different wavelengths need to be amplified by different multiples, so that an automatic gain circuit (PGA) is designed in the circuit;
after the signal is amplified by the automatic gain circuit, the signal is divided into two parts: one part of the signal enters a phase-locked amplifier for extracting the original cerebral blood oxygen signal; the other part of the signals passes through a band-pass filter to extract a heart rate original signal and a cerebral vascular tension original signal; the microprocessor performs digital-to-analog conversion on the original signal, and then performs calculation through an algorithm to obtain a result.
(II) the face detection device comprises: infrared camera, human face detection algorithm; the infrared camera is used for capturing and collecting face information at night. The face detection algorithm is deployed in the microprocessor, and face detection is realized by calling opencv-python and nump libraries through the microprocessor. The traditional characteristic sub-face discrimination method [3] is adopted in the invention. The main idea is as follows: from the statistical point of view, the basic element of the distribution of the face image, namely the characteristic vector of the covariance matrix of the face image sample set, is searched, so as to approximately represent the face image. These feature vectors are called eigenfaces. The characteristic face reflects the information hidden in the face sample set and the structural relationship of the face. The eigenvectors of the covariance matrix of the sample sets of eyes, cheeks and mandible are called characteristic eyes, characteristic jaws and characteristic lips, and are collectively called characteristic sub-faces. The feature sub-faces generate a sub-space in the corresponding image space, referred to as the sub-face space. And calculating the projection distance of the test image window in the sub-face space, and if the window image meets the threshold comparison condition, judging the window image to be a human face.
(III) the mechanical structure for tracking and positioning comprises: the device comprises a cradle head, an arc-shaped sliding rail and a motor which are built by a steering engine. Two ends of the arc-shaped slide rail are fixed on two sides of the bed corresponding to the position of the head of a person, the arc-shaped track surrounds the position of the head of the person, and the holder can move along the arc-shaped track; these mechanical structures are driven by a motor. The infrared camera arranged on the holder obtains the face, the microprocessor drives the motor, and the laser emitting module on the holder locks the laser irradiation position on the forehead of the testee. Thus, even if the head of the testee moves, the laser can be accurately irradiated on the relevant part, and accurate measurement can be carried out. The overall structural relationship is shown in fig. 6.
The motor driving circuit adopts a related circuit designed by a motor driving chip TB6612 FNG. The chip has small volume and large output current and supports double-circuit motor driving.
And (IV) the interface of the upper computer is shown in figure 7. The host computer contains: serial number search, baud rate setting, stop bit setting, data bit setting and check bit setting. Meanwhile, the upper computer is provided with a receiving area and a sending area. The upper computer is communicated with the microprocessor through the Bluetooth serial port, an 'open' button is clicked, the communication is started, and the receiving area draws the waveform of the received data, as shown in figure 8; clicking a 'close' key, finishing communication, and storing the received data into a txt file by the upper computer; clicking a 'clear' key, the waveform of the receiving area is cleared; by clicking the "send" button, the command from the send area is transmitted to the microprocessor.
Claims (7)
1. A non-contact physiological signal monitor, comprising: the system comprises a physiological signal detection device, a human face detection device, a microprocessor, a mechanical structure for tracking and positioning and an upper computer for data display; wherein:
the physiological signal detection device comprises: the device comprises a laser emission module, an optical signal detection and conversion module and a signal processing algorithm module; wherein:
the laser emitting module is a laser emitter and is used for emitting laser;
the optical signal detection and conversion module comprises a photoelectric conversion circuit, an automatic gain circuit, a phase-locked amplification circuit and a programmable filter circuit; wherein:
(1) the photoelectric conversion circuit mainly generates an output voltage of 0.1V to 4.9V for a photodiode current of 0uA to 5.43 uA;
(2) the automatic gain circuit adopts a PGA281 chip and is used for providing the lowest gain of 0.125 times and the highest gain of 176 times;
(3) the phase-locked amplifying circuit is an amplifier which can separate a specific carrier frequency signal from an environment with great interference;
(4) the programmable filter circuit adopts a filter constructed by MAX 262; two independent program-controlled filters are arranged inside the MAX 262; wherein, the low-pass, the high-pass, the band-stop, the full-pass, the central frequency and the Q value can be adjusted by the processor; one of the two programmable filter circuits is designed as a low-pass filter circuit with the cutoff frequency of 0.2HZ, and forms a phase-locked amplifier with an AD630 chip for extracting the cerebral blood oxygen signal; the other programmable filter circuit is designed as a band-pass filter with the frequency range of 0.2HZ-2HZ and is used for extracting a heart rate signal and a cerebral vascular tension signal;
the signal processing algorithm module comprises an algorithm for calculating the blood oxygen saturation and a peak detection algorithm for calculating the heart rate; these algorithms are deployed in a microprocessor;
(II) the face detection device comprises: infrared camera, human face detection algorithm; the infrared camera is used for capturing and collecting face information at night; the human face detection algorithm is mainly used for acquiring human face information according to the infrared camera, calculating the projection distance of a human face image in a sub-face space, and judging the image as a corresponding human face if the image meets a threshold comparison condition; the face detection algorithm is deployed in the microprocessor.
The microprocessor adopts STM32F4 to deploy a signal processing algorithm and a human face detection algorithm; the microprocessor calls a face detection algorithm to judge the face, controls the slide rail and the holder, adjusts the laser emission angle and irradiates the forehead of the brain; after the microprocessor collects the original signal, a signal processing algorithm is called for calculation, and the calculated value is sent to an upper computer through Bluetooth;
(IV) the mechanical structure for tracking and positioning comprises: the cradle head, the arc-shaped sliding rail and the motor are built by the steering engine; two ends of the arc-shaped slide rail are fixed on two sides of the bed corresponding to the position of the head of a person, the arc-shaped track surrounds the position of the head of the person, and the holder can move along the arc-shaped track; the holder is driven by a motor and is controlled to move on the arc-shaped slide rail; the laser emission module, the optical signal detection and conversion module, the microprocessor and the infrared camera are arranged on the holder;
after the infrared camera arranged on the holder acquires the face information, the microprocessor drives the motor to lock the irradiation position of the laser emitted by the laser emitter arranged on the holder on the forehead of the testee; therefore, even if the head of the testee moves, the laser can be accurately irradiated on the relevant part to accurately measure;
the upper computer performs data interaction with the microprocessor through Bluetooth; the upper computer performs initial analysis on the original signal, displays the waveform of the physiological signal in real time and controls the related actions of the instrument; the hardware system transmits the acquired and processed digital physiological electric signals back to an upper computer at the PC end through a serial port communication module, and the upper computer draws an original data oscillogram of the received data in real time and stores the data in a data buffer area for data transfer.
2. The monitor of claim 1, wherein the laser emitting module comprises two transistors Q1 and Q2, and six peripheral resistors R40, R41, R42, R43, R44, R45; two transistors Q1 are the same as the peripheral circuit of Q2; for the circuit composed of Q1, R40, R41 and R42, a resistor R40 is connected to the base of a triode Q1, a resistor R42 is connected to the collector of the triode Q1, and R41 is connected between the emitter and the base of the triode. When the base electrode of the triode Q1 is at a high level, the triode is conducted, and laser lights; when the base of the triode Q1 is at low level, the triode is cut off, and the laser is extinguished; the connection relationship and the function of the circuit composed of Q2, R43, R44 and R45 are the same as those of the circuit composed of Q1, R40, R41 and R42.
3. The non-contact physiological signal monitor of claim 1, wherein the photoelectric conversion circuit comprises a single power operational amplifier configured as a transimpedance amplifier having a bandwidth greater than 1MHz for amplifying the photo-dependent current of the photodiode; the current model IPD and the capacitor CJ are connected in parallel to form a simulation model of the photodiode; the capacitor C1 and the resistor R1 are connected in parallel and bridged at the reverse input end and the output end of the operational amplifier to form a feedback network; the resistors R2 and R3 form a voltage division circuit, and divide the voltage of the voltage source V1 to provide a low bias voltage for the non-inverting input end of the operational amplifier; the circuit is a passive filter consisting of a resistor R4 and a capacitor C3 and used for filtering high-frequency noise.
4. The non-contact physiological signal monitor according to claim 1, wherein the phase-locked amplifying circuit is implemented by an AD630 chip and a low-pass filter circuit.
5. The non-contact physiological signal monitor according to any one of claims 1-4, wherein the microprocessor converts the original signal into a digital signal by the ADC, and then calls the signal processing algorithm module to perform calculation;
when blood oxygen calculation is carried out, assuming that a scattering coefficient is constant and known, a radiation transfer equation gives spatial derivatives of absorption after different distances of reflected light intensity are obtained, and the following analytic solutions are obtained by combining Fick diffusion law:
where λ is the wavelength of light, μaIs the absorption coefficient of the medium, ρ is the distance between the light source and the detector, k is a constant, h is the normalized slope between the scattering coefficient and the wavelength; a is the optical density, defined as:
wherein, IoAnd I is the light intensity of the origin and distance ρ, respectively; absorption coefficient muaExpressed as:
wherein epsilonHbAnd
as a specific extinction coefficient, cHbAnd
the molarity of the deoxygenated hemoglobin and the deoxygenated hemoglobin, respectively; for two wavelengths, this equation is rewritten as:
brain blood oxygen concentration is expressed as:
when calculating the heart rate, calculating the period between two signal peaks by adopting a peak value detection algorithm so as to obtain the heart rate;
when the cerebral vascular tension is calculated, the magnitude of the cerebral vascular tension is reflected by calculating the magnitude of the signal peak value.
6. The non-contact physiological signal monitor according to claim 1, wherein the specific process inside the optical signal detection and conversion module is as follows:
the photodiode converts the optical signal into corresponding current, and converts the current signal into a voltage signal by combining with a photoelectric conversion circuit;
the voltage signal enters an automatic gain circuit to be amplified; after the signal is amplified by the automatic gain circuit, the signal is divided into two parts: one part of the signal enters a phase-locked amplifier for extracting the original cerebral blood oxygen signal; the other part of the signals passes through a band-pass filter to extract a heart rate original signal and a cerebral vascular tension original signal; the microprocessor performs digital-to-analog conversion on the original signal, and then performs calculation through an algorithm to obtain a result.
7. The non-contact physiological signal monitor according to claim 1, wherein the upper computer comprises: serial number searching, baud rate setting, stop bit setting, data bit setting and check bit setting; the upper computer is provided with a receiving area and a sending area; the upper computer is communicated with the microprocessor through a Bluetooth serial port, an 'open' button is clicked, communication is started, and a receiving area performs waveform drawing on received data; clicking a 'close' button to finish communication; the upper computer stores the received data into a txt file; clicking a 'clear' key, the waveform of the receiving area is cleared; by clicking the "send" button, the command from the send area is transmitted to the microprocessor.
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