CN107595274B - Back cushion for detecting emotional pressure based on heart rate variability in non-contact mode - Google Patents
Back cushion for detecting emotional pressure based on heart rate variability in non-contact mode Download PDFInfo
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Abstract
The invention provides a back cushion for detecting emotional pressure based on heart rate variability in a non-contact mode, which comprises: the back cushion body is installed on the back cushion body data operation box to and install at the back cushion body and with the piezoelectric sensor of data operation box contact. This cushion based on heart rate variability non-contact detects mood pressure simple structure, high durability and convenient use, cooperation between piezoelectric sensor and the data operation box through setting up in the cushion, can keep under the prerequisite of non-direct contact with the user, gather user's body vibrations conduction signal, and handle pressure signal that piezoelectric sensor gathered and to pressure signal through the data operation box, calculate the HRV parameter that contains in the pressure signal, match with the index model through the HRV parameter, realize individual mood pressure to the tester, compressive capacity, fatigue index's analysis, thereby can monitor user's mood pressure and health at any time, be convenient for in time make intervention, prevent that the user state from continuing to worsen.
Description
Technical Field
The invention relates to emotional stress monitoring equipment, in particular to a back cushion for detecting emotional stress based on heart rate variability in a non-contact mode.
Background
Heart rate variability (Heart rate variability HRV) refers to the change in Heart rate rhythm speed over time. HRV is the analysis of subtle temporal changes from cardiac cycle to cardiac cycle and their regularity. Such changes are often difficult to measure or slightly negligible on conventional electrocardiograms recorded on the body surface, which are customarily described as regular sinus rhythms which are never equal to no variation in heart rate. HRV was studied with only temporal differences from cardiac cycle to cardiac cycle, and the differences between the individual cardiac cycles in the listed individuals showed that a large stack of seemingly disordered parameters reflected the continuous, transient fluctuations in heart rate. Fluctuations in heart rate are not incidental but rather regulated by the neurohumoral content of the receptor, responding to different physiological conditions or to certain pathological conditions.
The HF component of HRV reflects the fluctuating heart rate caused by the modulation of respiratory activity ultimately by cardiac vagal fibre conduction, also known in the literature as "respiratory arrhythmia" (RSA). The respiration activity has modulation effect on the heart rate through two ways of central mechanism and mechanical influence, and the peak height of HF of HRV is obviously related to the modulation degree of the heart rate by the activity of the cardiac fan.
Spectral analysis found that heart rate variability generally included High Frequency (HF) and Low Frequency (LF) components, with some researchers further separating LF into ultra low frequency and low frequency. In which the high frequency component, which is considered by the scholars to reflect the parasympathetic function, and the ratio of the low frequency component to the high frequency component (LF/HF) reflects the sympathetic activity, is synchronized with the respiratory motion, and therefore also referred to as the respiratory component, occurs once in about 3 seconds.
Existing HRV measurement methods include short-range testing and long-range testing. Although the accuracy of the long-range test is high, the general test time needs about 24 hours, the measured patient needs to wear the dynamic electrocardiogram monitor all day long, and many actions of the user are limited, so that the user can select the short-range test in many cases. The short-range test refers to measurement by a special device in a short time (for example, 5 minutes), and although the method has the advantages of short measurement time and convenience in use, the measured result data has large fluctuation, poor repeatability and larger result error, and the HRV is often measured by the method only in the research field.
According to the principle, a series of methods and products are developed at home and abroad, and the methods and the products have the following publication numbers: CN106859625A chinese patent application discloses a method and apparatus for HRV measurement, wherein after an original RR interval data set is obtained, a filtering process is performed on the original RR interval data set to obtain an RR interval data set corresponding to sinus rhythm, then the RR interval data set is subjected to fast fourier transform and/or wavelet transform to remove errors in the RR interval data set, thereby removing errors caused by respiratory rhythm variations and the like to obtain a standard RR interval data set, and then an HRV time domain index is calculated according to the standard RR interval data set, in this process, the influence of external factors on the RR interval data can be removed to a great extent, even if the measured time is short, the RR interval data is small, a more accurate calculation result can be obtained in the time domain, and after a short-range HRV index is obtained, the HRV time domain index can be calculated according to the short-range HRV time domain index, the 24-hour HRV time domain index is calculated by using the calculation model, so that the time required by measurement is shortened on the premise of ensuring the calculation precision by using the HRV calculation method, and meanwhile, accurate 24-hour HRV data can be finally obtained. However, when the method is used for data acquisition, the patient needs to be in contact with the human body, namely wearing equipment for assistance, so that the measured patient needs to wear a dynamic electrocardiogram monitor or other special measuring equipment, and many actions of the user are limited.
There are also some scientific studies that have proposed the use of non-contact piezoelectric sensing methods to acquire HRV signals, such as: the Chinese patent application with the publication number of CN103824420A discloses a fatigue driving recognition system based on heart rate variability non-contact measurement, which comprises an image acquisition device, an image processing device and an alarm device, wherein the image acquisition device is used for acquiring facial images of a driver in real time and transmitting the acquired facial images of the driver to the image processing device; the image processing device is used for acquiring the heart rate variability of the driver according to the image of the driver and acquiring the driving fatigue state of the driver according to the heart rate variability of the driver; and the alarm device is used for giving an alarm when the image processing device judges that the driver is in the driving fatigue state. The system utilizes the image acquisition device to acquire the face image of a human body in real time, transmits the acquired image of the driver to the image processing device, acquires the heart rate variability of the driver, and acquires the driving fatigue state of the driver according to the heart rate variability of the driver. However, the system uses complex image recognition technology and algorithm, and the whole system is high in cost and not easy to popularize. Since the physiological signals of the Ballistocardiogram (BCG) acquired by the piezoelectric sensing are passively sensed, the acquired signals are difficult to process, a good processing method does not exist at present, and many researches finally result in that accurate HRV signals cannot be acquired because the rejected Ballistocardiogram (BCG) signals cannot be processed, so that industrialization cannot be realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the back cushion for detecting the emotional pressure based on the heart rate variability in a non-contact mode.
In order to realize the technical scheme, the invention provides a back cushion for detecting emotional pressure based on heart rate variability in a non-contact manner, which comprises the following components:
the back cushion comprises a back cushion body, wherein a groove is formed in the back cushion body, and the back cushion body can be made of sponge, memory cotton or some soft cloth;
the data operation box is placed in the groove of the back cushion body and fixed through the rear cover, the data operation box comprises an operation box upper cover and an operation box lower cover which are matched with each other and detachably connected, the main control board is installed between the operation box upper cover and the operation box lower cover, a battery for supplying power to the main control board is also installed between the operation box upper cover and the operation box lower cover, the wireless communication module and the central processor are both installed on the main control board, and the data operation box is a core component of the whole back cushion and is used for data aggregation and operation;
the piezoelectric sensor is fixed on the operation box through the packaging cover, a protrusion is arranged at a position, corresponding to the piezoelectric sensor, of the packaging cover, a limiting structure for limiting the movement of the packaging cover is arranged on the operation box, the packaging cover and the piezoelectric sensor are arranged in parallel, after the packaging cover is stressed, the protrusion is stressed to press the piezoelectric sensor, the piezoelectric sensor outputs signals under pressure, the packaging cover returns to the original position when the force is removed or the force is statically given, the signal output end of the piezoelectric sensor is connected with the signal input end of the main control board of the data operation box, the main control board in the data operation box receives pressure signals collected by the piezoelectric sensor and processes the pressure signals, HRV parameters contained in the pressure signals are calculated, and the analysis on the emotional pressure, the pressure resistance and the fatigue index of a tester is realized through the matching of the HRV parameters and the index model.
Preferably, the data operation box processes the pressure signal received by the piezoelectric sensor according to the following mode:
3, deleting the baseline interference combined in the BCG signal by adopting a 0.02Hz high-pass filter to finish the pretreatment of the BCG signal;
step 4, defining the BCG signal processing cycle to be 30-120 times/minute, presetting an ideal BCG signal waveform with the main lobe width of 100, solving the covariance of the ideal BCG signal and the stack signal, and extracting the envelope;
step 5, identifying a J peak of the BCG signal subjected to noise reduction, solving J-J Interval Interval _ JJ (i) of the BCG signal, then solving an average Mean _ JJ of all J-J intervals in a detection time range, and finally calculating time domain parameters SDNN and PNN50 of the HRV according to the solved J-J Interval Interval _ JJ (i) of the BCG signal and the average Mean _ JJ of all J-J intervals;
step 6, performing frequency domain power spectrum analysis on the HRV time domain parameters in the acquisition time range in the step 5 by adopting a Fast Lomb-Scargle periodogram spectrum analysis function to obtain an HRV power spectrum, and solving a heart total power value TP, a high frequency power value HF, a low frequency power value LF and an LF/HF ratio according to the HRV power spectrum;
step 7, establishing an emotional stress index LF/HF model, a fatigue index PNN50 model and a stress resistance index SDNN model;
and 8, matching the acquired HRV time domain parameters and frequency domain parameters with the model established in the step 7 to realize the analysis of the emotional stress, the compressive capacity and the fatigue index of the tester.
Preferably, the specific process of extracting the envelope in step 4 is as follows:
step 41: defining a BCG signal processing period to be 30-120 times/minute, wherein a stack signal is x (t) under the condition of no body movement interference, t is 1, the right and 6000, and constructing an ideal BCG signal, which is defined as y (t), t is 1, the right and 6000, and t is a time domain sampling point number;
step 42: setting the main lobe width of the BCG signal as 100, complementing the rest side lobe vectors as 0, solving covariance functions of x (t) and y (t), wherein the covariance functions comprise the following specific formula:
z (t) Cov { x (t), y (t) } formula 1
Wherein: z (t) is a covariance function of x (t) and y (t), x (t) is a sign signal collected and stored in a stack, and y (t) is a constructed ideal BCG signal;
step 43: through covariance processing, the interference of Gaussian noise to the BCG signal is inhibited to the greatest extent by utilizing the strong correlation between the signals in the stack and the BCG signal;
step 44: solving for z (t) the hilbert transform z (f), the specific formula is as follows:
z (f) ═ Hilbert { z (t) } formula 2
Wherein Z (f) represents the Hilbert transform of z (t), and takes the module value of Z (f) to obtain the characteristic envelope of Z (f) signal, and the smoothing process is carried out by using a time window function with the length of 5 to eliminate high-frequency interference.
Preferably, the specific process of calculating the time domain parameters SDNN and PNN50 of the HRV in step 5 is as follows:
step 51, the central processor in the main control board identifies a peak point J peak of the BCG signal after the noise reduction processing, and then uses a sliding time window with a period of 1s to select a maximum peak point in the time window as the J peak, that is, peak (i) ═ max { z (f);
and step 52, solving the J-J interval, wherein the specific calculation formula is as follows:
interval _ jj (i) ═ Peak (i +1) -Peak (i) formula 3
Wherein: interval _ JJ (i) is the value of the J-J Interval of the BCG signal; peak (i +1) represents the i +1 th BCG signal J Peak, and Peak (i) represents the i th BCG signal J Peak;
step 53, removing unreasonable J-J intervals, wherein the removing conditions are as follows:
condition 1): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) <0.5 s;
condition 2): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) >2.0 s;
condition 3): when adjacent J-J intervals, such as Interval _ JJ (i), Interval _ JJ (i +1), fluctuate by more than 30% of the average J-J Interval;
step 54, after eliminating abnormal J-J interval data, solving the Mean _ JJ of all J-J intervals in the detection time range,
step 55, respectively calculating time domain parameters SDNN and PNN50 of the HRV according to the solved BCG signal J-J Interval Interval _ JJ (i) and the average Mean _ JJ of all J-J intervals and formulas 4 to 5, wherein the specific calculation formulas are as follows:
wherein: SDNN is the standard deviation of all sinus heartbeat RR intervals, and is the time domain parameter of HRV; interval _ JJ (i) is the value of the J-J Interval of the BCG signal; mean _ JJ is the average of all J-J intervals; t is the effective J-J period number in the detection time range;
the HRV time domain parameter PNN50 is calculated as follows:
N{JJ>Mean_JJ}representing the number of JJ intervals and the average JJ interval greater than 50ms, NtotalRepresents the total number of JJ intervals.
Preferably, the specific process of establishing the emotional stress index LF/HF model, the fatigue index PNN50 model and the stress resistance index SDNN model in step 7 is as follows:
step 71, establishing an emotional stress index LF/HF model: mainly determined by the endocrine index LF/HF, defined as follows: when the LF/HF range is (0-0.4) & (3.46-4), the stress ability score is 0-30; when the LF/HF range is (0.4-1) & (2.92-3.46), the stress ability score is 30-60 points; when the LF/HF range is (1-1.6) & (2.56-2.92), the stress ability score is 60-80 points; when the LF/HF range is (1.6-2.2) & (2.2-2.56), the stress ability score is 80-100;
step 72, establishing a fatigue index PNN50 model: determined by the temporal HRV parameter PNN50, defined as follows: when PNN50> -49, the fatigue index score is 0-30; when the PNN50 is in the range of (1-2) & (12-49), the fatigue index score is 30-60; when the PNN50 is in the range of (3-4) & (8-12), the fatigue index score is 60-80; when the PNN50 is in the range (5-8), the fatigue index score is 80-100;
step 73, establishing an SDNN model of the compression resistance index: determined by the temporal HRV parameters SDNN, defined as follows: when the SDNN is in the range of (0-25) & (960+), the compression resistance index is scored to be 0-30; when the SDNN is in the range of (25-50) & (240-; when the SDNN is in the range of (50-100), the compression resistance index score is 60-80; when the SDNN is in the range of (100-.
When the HRV time domain parameters and the frequency domain parameters obtained by calculation in the operation box are respectively matched with the emotional stress index LF/HF model, the fatigue index PNN50 model and the compression resistance index SDNN model loaded in the operation box according to the requirements in the step 7, the analysis of the emotional stress, the compression resistance and the fatigue resistance index of a tester can be rapidly realized, so that the emotional stress and the body state of a user can be monitored at any time, the intervention can be conveniently made in time, and the state of the user is prevented from continuously deteriorating and generating danger.
Preferably, the device further comprises an alarm, the alarm is connected with a main control board on the data operation box, when the data calculated by the data operation box is higher than a set value, the alarm automatically gives an alarm, and a user can find problems in time.
Preferably, still be provided with the head that charges on the cushion body, the head that charges comprises the first upper cover that charges, the electricity core that charges and the first lower cover that charges, and the core that charges is installed between the first upper cover that charges and the first lower cover that charges, and the electricity core that charges passes through the wire to be connected with the battery in the data operation box, can realize the quick charge to the battery in the data box through the head that charges, makes things convenient for the use of this cushion.
Preferably, the lower cover of the operation box is further provided with a radiating fin, the radiating fin is adhered below the main control board through heat conducting glue, and the data operation box is large in workload and easy to generate heat, so that the transmission of heat of the main control board in the data operation box to the radiating fin can be accelerated through the heat conducting glue, and then the heat in the main control board is dissipated rapidly through the radiating fin, so that overheating of the main control board is avoided.
Preferably, the wireless communication module is a WIFI communication module or a bluetooth communication module, and the connection between the back cushion and terminal devices such as a mobile phone, a PC, a PAD and the like can be realized through the WIFI communication module or the bluetooth communication module.
Preferably, the surface of the rear cover is provided with heat dissipation holes which are uniformly distributed, so that the heat dissipation of the data operation box is facilitated.
The back cushion for detecting emotional pressure based on heart rate variability in a non-contact manner and the application thereof have the advantages that:
1) the back cushion for detecting emotional pressure based on heart rate variability in a non-contact mode is simple in structure and convenient to use, through the cooperation between the piezoelectric sensor and the data operation box arranged in the back cushion, the body vibration conduction signals of a user can be collected on the premise that the back cushion is not in direct contact with the user, the pressure signals collected by the piezoelectric sensor are processed through the data operation box, HRV parameters contained in the pressure signals are calculated, the HRV parameters are matched with an index model, analysis of the emotional pressure, the pressure resistance and the fatigue index of a tester is achieved, the emotional pressure and the body state of the user can be monitored at any time, intervention can be made in time conveniently, the state of the user is prevented from being continuously deteriorated, and danger is caused;
2) according to the cushion for detecting emotional pressure in a non-contact manner based on heart rate variability, on the premise that the cushion keeps non-direct contact with a tester, the stimulation of a body vibration conduction signal to piezoelectricity is collected, a BCG signal of a continuous ballistocardiogram is obtained, accurate extraction and noise reduction processing of the BCG signal are achieved through data discrete processing, stack storage, second-order IIR low-pass filtering processing, differential filtering processing, covariance processing and the like, and finally time domain parameters and frequency domain parameters of HRV are accurately obtained through solving the average values of J-J intervals and J-J intervals of the BCG signal. Therefore, the problem that inconvenience is caused by the fact that signals acquired by an existing wearable product need to directly contact a test object and limit the action of the test object is solved, all physical characteristics of BCG signals can be completely reserved by adopting the signals through the piezoelectric sensor, and analysis based on BCG signal cardiac cycle, HRV and the like is supported more accurately; the back cushion realizes accurate extraction of the refuted BCG signals, obtains high-precision HRV information, matches with the built emotional stress index LF/HF model, the fatigue index PNN50 model and the compression resistance index SDNN model, realizes monitoring of the emotional stress and the body state of a user at any time, is convenient for timely intervention, is convenient and rapid, has high accuracy, can be used in families, offices or automobiles, and is convenient for industrialization and popularization.
Drawings
Fig. 1 is a top view of a three-dimensional structure of a back cushion for non-contact detection of emotional stress based on heart rate variability.
Fig. 2 is a bottom view of a three-dimensional structure of the back cushion for detecting emotional stress based on heart rate variability in a non-contact mode.
Fig. 3 is an explosion chart I of the back cushion for detecting emotional stress based on heart rate variability in a non-contact mode.
Fig. 4 is an explosion chart II of the back cushion for detecting emotional stress based on heart rate variability in a non-contact mode.
Fig. 5 is a time domain envelope diagram of the BCG signal of the inventive ballistocardiogram.
In the figure: 1. a back cushion body; 11. installing a groove; 2. a charging head; 21. a charging head upper cover; 22. a charging chip; 23. a charging head lower cover; 3. a rear cover; 4. a data operation box; 41. an operation box upper cover; 42. pressing line sheets; 43. a battery; 44. a main control board; 45. a heat sink; 46. a wireless communication module; 47. a lower cover of the operation box; 48. heat conducting glue; 5. a piezoelectric sensor; 6. and (7) packaging the cover.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step are within the scope of the present invention.
Example (b): a back cushion for detecting emotional stress based on heart rate variability in a non-contact mode.
Referring to fig. 1 to 5, a back pad for non-contact detection of emotional stress based on heart rate variability includes:
the back cushion comprises a back cushion body 1 made of memory cotton, wherein a groove 11 is arranged on the back cushion body 1, a data operation box 4 is placed in the groove 11 of the back cushion body 1 and is fixed through a rear cover 3, heat dissipation holes are uniformly distributed in the surface of the rear cover 3, the heat dissipation of the data operation box 4 is facilitated, the data operation box 4 comprises an operation box upper cover 41 and an operation box lower cover 47 which are matched with each other and are detachably connected, a main control board 44 is arranged between the operation box upper cover 41 and the operation box lower cover 47, a battery 43 for supplying power to the main control board 44 is also arranged between the operation box upper cover 41 and the operation box lower cover 47, a WIFI wireless communication module 46 and a central processor are both arranged on the main control board 44, the connection between the back cushion and terminal equipment such as a mobile phone, a PC, a PAD and the like can be realized through the WIFI communication module 46, the central processor is used for signal processing and automatic matching of subsequent index models, and a heat dissipation, the heat radiating fins 45 are adhered below the main control board 44 through the heat conducting glue 48, and the data operation box is large in workload and easy to heat, so that the heat of the main control board 44 in the data operation box 4 can be accelerated to be transmitted to the heat radiating fins 45 through the heat conducting glue 48, then the heat in the main control board 44 is quickly dissipated through the heat radiating fins 45, the main control board 44 is prevented from being overheated, and the data operation box 4 is a core component of the whole back cushion and is used for data aggregation and operation;
the device is characterized by further comprising a piezoelectric sensor 5, wherein the piezoelectric sensor 5 is fixed on an operation box upper cover 41 through a packaging cover 6, a protrusion is arranged at a position, corresponding to the piezoelectric sensor 5, of the packaging cover 6, a limiting groove for limiting the movement of the packaging cover 6 is formed in the operation box upper cover 41, the packaging cover 6 is arranged in parallel with the piezoelectric sensor 5, when the packaging cover 6 is stressed, the protrusion is stressed to press the piezoelectric sensor 5, the piezoelectric sensor 5 outputs a signal in a pressed mode, the packaging cover returns to the original position when the force is removed or statically applied, a signal output end of the piezoelectric sensor 5 is connected with a signal input end of a data operation box main control board 44 and fixed through a line pressing sheet 42, the main control board 44 in the data operation box 4 receives a pressure signal collected by the piezoelectric sensor 5 and processes the pressure signal, HRV parameters contained in the pressure signal are calculated, and the emotional pressure, the, Analyzing the compressive capacity and the fatigue index;
the alarm is connected with a main control board 44 on the data operation box 4, and when the data calculated by the data operation box 4 is higher than a set value, the alarm automatically alarms, so that a user can find problems in time;
still be provided with the head 2 that charges on the cushion body 1, the head 2 that charges comprises the head upper cover 21 that charges, the core 22 that charges and the head lower cover 23 that charges, and the core 22 that charges is installed between the head upper cover 21 that charges and the head lower cover 23 that charges, and the core 22 that charges passes through the wire to be connected with the battery 43 in the data operation box 4, can realize the quick charge to battery 43 in the data box 4 through the head 2 that charges to make things convenient for the use of this cushion.
In this embodiment, when specifically using, after human contact cushion, install the encapsulation lid 6 atress in cushion body 1 after, protruding atress oppression piezoelectric sensor 5, piezoelectric sensor 5 pressurized output signal, piezoelectric sensor 5 shakes the body and converts the analog signal of telecommunication into, and piezoelectric sensor 5 handles the central treater that data operation box 4 was transmitted to analog signal of telecommunication, and data operation box 4 handles the pressure signal that piezoelectric sensor 5 received according to following mode:
step 4, defining the BCG signal processing cycle to be 30-120 times/minute, presetting an ideal BCG signal waveform with the main lobe width of 100, solving the covariance of the ideal BCG signal and the stack signal, and extracting the envelope;
the specific process comprises the following steps: embedding the signal processing scheme into a central processor, performing targeted noise reduction processing on the pre-processed signals after filtering,
firstly, defining a stack signal as x (t) under the condition of no body motion interference, wherein t is 1. Then, an ideal BCG signal (as shown in fig. 5) is constructed, defined as y (t), t ═ 1.., 6000; considering that the period of the conventional physical sign BCG signal is 30-120 times/min, the main lobe width of the BCG signal is set as 100, the rest side lobe vectors are complemented by 0, meanwhile, the peak value max (y (t)) max (x (t)) of the BCG is defined, and the covariance functions of x (t) and y (t) are solved as follows:
z(t)＝Cov{x(t),y(t)}
wherein: z (t) is a covariance function of x (t) and y (t), x (t) is a sign signal collected and stored in a stack, and y (t) is a constructed ideal BCG signal;
then, through covariance processing, the interference of Gaussian noise to the BCG signal is suppressed to the maximum extent by utilizing the strong correlation between the signals in the stack and the BCG signal;
finally, considering the high-frequency component interference generated by the difference between the constructed BCG signal and the actually acquired BCG signal, solving z (t) Hilbert transform Z (f) Hilbert { z (t) and a modulus value to obtain Z (f) signal characteristic envelope, and smoothing by using a time window function with the length of 5 to eliminate the high-frequency interference;
through the processing of the BCG signals in the steps 2, 3 and 4, the physical characteristics of the BCG signals are completely reserved, the rejected BCG signals are accurately extracted, the analysis based on the BCG signal cardiac cycle, the HRV and the like is supported, and the strong support is provided for the acquisition of subsequent high-precision HRV information;
step 5, solving a signal envelope peak point through a central processor, identifying a peak point, namely a J peak, of the preprocessed BCG signal, defining a sliding time window with the period of 1s (1000 points) in consideration of the conventional 30-120 times/minute range of the BCG in the acquisition environment, and selecting a maximum peak point in the time window as the J peak, namely the J peak
Peak(i)＝max{Z(f)}
And solving for J-J intervals:
Interval_JJ(i)＝Peak(i+1)-Peak(i)
wherein: interval _ JJ (i) is the value of the J-J Interval of the BCG signal; peak (i +1) represents the i +1 th BCG signal J Peak, and Peak (i) represents the i th BCG signal J Peak;
considering that the J-J interval corresponding to the constraint condition that the heart rate is within the range of 30-120 is 0.5-2 s (500 points-2000 points) and the heart rate does not have the characteristic of mutation in a short time, firstly, unreasonable J-J intervals are removed, and the removal conditions are as follows:
condition 1): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) <0.5 s;
condition 2): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) >2.0 s;
condition 3): when adjacent J-J intervals, such as Interval _ JJ (i), Interval _ JJ (i +1), fluctuate by more than 30% of the average J-J Interval;
after removing abnormal J-J interval data, solving the average value of all limited J-J intervals in the detection time range, which is defined as Mean _ JJ, and then the time domain characteristic parameters of the HRV comprise Standard Deviation (SDNN) of all sinus heart beat RR intervals (based on BCG signal J-J intervals), which is defined as follows:
wherein: SDNN is the standard deviation of all sinus heartbeat RR intervals, and is the time domain parameter of HRV; interval _ JJ (i) is the value of the J-J Interval of the BCG signal; mean _ JJ is the average of all J-J intervals; t is the effective J-J period number in the detection time range;
meanwhile, based on time domain HRV, the characteristic parameters of PNN50, DC and the like can be solved, and the definition is as follows:
the HRV time domain parameter PNN50 is calculated as follows:
N{JJ>Mean_JJ}representing the number of JJ intervals and the average JJ interval greater than 50ms, NtotalRepresents the total number of JJ intervals;
step 6, carrying out frequency domain analysis on the time domain HRV parameters and JJ interval data and applying the time-frequency analysis, carrying out frequency domain power spectrum analysis on the HRV time domain parameters in the acquisition time range in the step 5 by adopting a Fast Lomb-Scargle periodogram spectrum analysis function to obtain an HRV power spectrum, and automatically solving a heart total power value TP, a high frequency power value HF, a low frequency power value LF and an LF/HF ratio by the central processor according to the HRV power spectrum;
high-precision acquisition of frequency domain HRV parameters and time domain HRV parameters is realized through the steps 5 and 6, so that accurate comparison data can be provided for subsequent model matching;
step 7, establishing an emotional stress index LF/HF model, a fatigue index PNN50 model and a stress index SDNN model, wherein the specific process is as follows:
step 71, establishing an emotional stress index LF/HF model: mainly determined by the endocrine index LF/HF, defined as follows: when the LF/HF range is (0-0.4) & (3.46-4), the stress ability score is 0-30; when the LF/HF range is (0.4-1) & (2.92-3.46), the stress ability score is 30-60 points; when the LF/HF range is (1-1.6) & (2.56-2.92), the stress ability score is 60-80 points; when the LF/HF range is (1.6-2.2) & (2.2-2.56), the stress ability score is 80-100;
step 72, establishing a fatigue index PNN50 model: determined by the temporal HRV parameter PNN50, defined as follows: when PNN50> -49, the fatigue index score is 0-30; when the PNN50 is in the range of (1-2) & (12-49), the fatigue index score is 30-60; when the PNN50 is in the range of (3-4) & (8-12), the fatigue index score is 60-80; when the PNN50 is in the range (5-8), the fatigue index score is 80-100;
step 73, establishing an SDNN model of the compression resistance index: determined by the temporal HRV parameters SDNN, defined as follows: when the SDNN is in the range of (0-25) & (960+), the compression resistance index is scored to be 0-30; when the SDNN is in the range of (25-50) & (240-; when the SDNN is in the range of (50-100), the compression resistance index score is 60-80; when the SDNN is in the range (100-;
after the model is established, the model can be directly stored in a central processor of the data operation box 4, a corresponding alarm limit value is set in each index model, and once the HRV time domain parameter or the frequency domain parameter obtained by calculation in the data operation box 4 is higher than the set alarm limit value, the alarm automatically alarms;
and 8, automatically matching the HRV time domain parameters and the frequency domain parameters obtained by calculation in the calculation box with the emotional stress index LF/HF model, the fatigue index PNN50 model and the compression resistance index SDNN model loaded in the calculation box according to the requirements in the step 7 respectively, rapidly realizing the analysis of the emotional stress, the compression resistance and the fatigue index of the testee through different numerical value display, and automatically alarming by an alarm once the HRV time domain parameters or the frequency domain parameters obtained by calculation in the data calculation box 4 are higher than a set alarm limit value, thereby monitoring the emotional stress and the body state of the user at any time, being convenient for timely intervening, and preventing the state of the user from continuously deteriorating and generating danger.
The above description is only for the preferred embodiment of the present invention, but the present invention should not be limited to the embodiment and the disclosure of the drawings, and therefore, all equivalent or modifications that do not depart from the spirit of the present invention are intended to fall within the scope of the present invention.
Claims (8)
1. A back pad for non-contact detection of emotional stress based on heart rate variability, comprising:
the back cushion comprises a back cushion body, wherein a groove is formed in the back cushion body;
the data operation box is placed in the groove of the back cushion body and fixed through the rear cover, the data operation box comprises an operation box upper cover and an operation box lower cover which are matched with each other and detachably connected, the main control board is installed between the operation box upper cover and the operation box lower cover, a battery for supplying power to the main control board is also installed between the operation box upper cover and the operation box lower cover, and the wireless communication module and the central processor are both installed on the main control board;
the piezoelectric sensor is fixed on the upper cover of the operation box through a packaging cover, a protrusion is arranged at a position of the packaging cover corresponding to the piezoelectric sensor, a limiting structure for limiting the movement of the packaging cover is arranged on the upper cover of the operation box, the packaging cover and the piezoelectric sensor are arranged in parallel, when the packaging cover is stressed, the protrusion is stressed to press the piezoelectric sensor, the piezoelectric sensor outputs signals under pressure, the packaging cover returns to the original position when force is eliminated or statically applied, the signal output end of the piezoelectric sensor is connected with the signal input end of the main control board of the data operation box, the main control board in the data operation box receives pressure signals collected by the piezoelectric sensor and processes the pressure signals, HRV parameters contained in the pressure signals are calculated, and emotional pressure, compressive capacity and fatigue index of a tester are analyzed through the matching of the HRV parameters and the index model;
the data operation box processes the pressure signals received by the piezoelectric sensor according to the following modes:
step 1, collecting a body vibration pressure signal through a piezoelectric sensor, converting the pressure signal into an analog electric signal, and transmitting the analog electric signal to an operation box main control board;
step 2, the central processor samples the analog electric signal by using the sampling frequency of 1000Hz, performs discrete processing on the sampled data and stores the data in a stack form, designs the stack cache time to be 6s, then performs 50Hz wave-limiting and 1Hz second-order IIR low-pass filtering processing on the signals in the stack, and separates BCG signals;
3, deleting the baseline interference combined in the BCG signal by adopting a 0.02Hz high-pass filter to finish the pretreatment of the BCG signal;
step 4, defining the BCG signal processing cycle to be 30-120 times/minute, presetting an ideal BCG signal waveform with the main lobe width of 100, solving the covariance of the ideal BCG signal and the stack signal, and extracting the envelope;
step 5, identifying a J peak of the BCG signal subjected to noise reduction, solving J-J Interval Interval _ JJ (i) of the BCG signal, then solving an average Mean _ JJ of all J-J intervals in a detection time range, and finally calculating time domain parameters SDNN and PNN50 of the HRV according to the solved J-J Interval Interval _ JJ (i) of the BCG signal and the average Mean _ JJ of all J-J intervals;
step 6, performing frequency domain power spectrum analysis on the HRV time domain parameters in the acquisition time range in the step 5 by adopting a Fast Lomb-Scargle periodogram spectrum analysis function to obtain an HRV power spectrum, and solving a heart total power value TP, a high frequency power value HF, a low frequency power value LF and an LF/HF ratio according to the HRV power spectrum;
step 7, establishing an emotional stress index LF/HF model, a fatigue index PNN50 model and a stress resistance index SDNN model;
step 8, matching the obtained HRV time domain parameters and frequency domain parameters with the emotional stress index LF/HF model, the fatigue index PNN50 model and the compression resistance index SDNN model established in the step 7 to realize the analysis of the emotional stress, the compression resistance and the fatigue index of the testers;
the specific process of calculating the time domain parameters SDNN and PNN50 of the HRV in step 5 is as follows:
step 51, the central processor in the main control board identifies a peak point J peak of the BCG signal after the noise reduction processing, and then uses a sliding time window with a period of 1s to select a maximum peak point in the time window as the J peak, that is, peak (i) ═ max { z (f);
and step 52, solving the J-J interval, wherein the specific calculation formula is as follows:
interval _ jj (i) ═ Peak (i +1) -Peak (i) formula 1
Wherein: interval _ JJ (i) is the value of the J-J Interval of the BCG signal; peak (i +1) represents the i +1 th BCG signal J Peak, and Peak (i) represents the i th BCG signal J Peak;
step 53, removing unreasonable J-J intervals, wherein the removing conditions are as follows:
condition 1): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) <0.5 s;
condition 2): when Interval _ jj (i) ═ Peak (i +1) -Peak (i) >2.0 s;
condition 3): when adjacent J-J intervals, such as Interval _ JJ (i), Interval _ JJ (i +1), fluctuate by more than 30% of the average J-J Interval;
step 54, after eliminating abnormal J-J interval data, solving the Mean _ JJ of all J-J intervals in the detection time range,
step 55, respectively calculating time domain parameters SDNN and PNN50 of the HRV according to the solved BCG signal J-J Interval Interval _ JJ (i) and the average Mean _ JJ of all J-J intervals and formulas 2 to 3, wherein the specific calculation formulas are as follows:
wherein: SDNN is the standard deviation of all sinus heartbeat RR intervals, and is the time domain parameter of HRV; interval _ JJ (i) is the value of the J-J Interval of the BCG signal; mean _ JJ is the average of all J-J intervals; t is the effective J-J period number in the detection time range;
the HRV time domain parameter PNN50 is calculated as follows:
N{JJ>Mean_JJ}representing the number of JJ intervals and the average JJ interval greater than 50ms, NtotalRepresents the total number of JJ intervals.
2. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the specific process of extracting the envelope in the step 4 is as follows:
step 41: defining a BCG signal processing period to be 30-120 times/minute, wherein a stack signal is x (t) under the condition of no body movement interference, t is 1, the right and 6000, and constructing an ideal BCG signal, which is defined as y (t), t is 1, the right and 6000, and t is a time domain sampling point number;
step 42: setting the main lobe width of the BCG signal as 100, complementing the rest side lobe vectors as 0, solving covariance functions of x (t) and y (t), wherein the covariance functions comprise the following specific formula:
z (t) Cov { x (t), y (t) } formula 4
Wherein: z (t) is a covariance function of x (t) and y (t), x (t) is a sign signal collected and stored in a stack, and y (t) is a constructed ideal BCG signal;
step 43: through covariance processing, the interference of Gaussian noise to the BCG signal is inhibited to the greatest extent by utilizing the strong correlation between the signals in the stack and the BCG signal;
step 44: solving for z (t) the hilbert transform z (f), the specific formula is as follows:
z (f) Hilbert { z (t) } formula 5
Wherein Z (f) represents the Hilbert transform of z (t), and takes the module value of Z (f) to obtain the characteristic envelope of Z (f) signal, and the smoothing process is carried out by using a time window function with the length of 5 to eliminate high-frequency interference.
3. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the specific process of establishing the emotional stress index LF/HF model, the fatigue index PNN50 model and the stress index SDNN model in the step 7 is as follows:
step 71, establishing an emotional stress index LF/HF model: mainly determined by the emotional stress index LF/HF, defined as follows: when the LF/HF range is (0-0.4) & (3.46-4), the stress ability score is 0-30; when the LF/HF range is (0.4-1) & (2.92-3.46), the stress ability score is 30-60 points; when the LF/HF range is (1-1.6) & (2.56-2.92), the stress ability score is 60-80 points; when the LF/HF range is (1.6-2.2) & (2.2-2.56), the stress ability score is 80-100;
step 72, establishing a fatigue index PNN50 model: determined by the temporal HRV parameter PNN50, defined as follows: when PNN50> -49, the fatigue index score is 0-30; when the PNN50 is in the range of (1-2) & (12-49), the fatigue index score is 30-60; when the PNN50 is in the range of (3-4) & (8-12), the fatigue index score is 60-80; when the PNN50 is in the range (5-8), the fatigue index score is 80-100;
step 73, establishing an SDNN model of the compression resistance index: determined by the temporal HRV parameters SDNN, defined as follows: when the SDNN is in the range of (0-25) & (960+), the compression resistance index is scored to be 0-30; when the SDNN is in the range of (25-50) & (240-; when the SDNN is in the range of (50-100), the compression resistance index score is 60-80; when the SDNN is in the range of (100-.
4. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the alarm is connected with a main control board on the data operation box, when the data calculated by the data operation box is higher than a set value, the alarm automatically gives an alarm, and the data calculated by the data operation box is HRV time domain parameters or frequency domain parameters.
5. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the back cushion body is also provided with a charging head, the charging head consists of a charging head upper cover, a charging core and a charging head lower cover, the charging core is arranged between the charging head upper cover and the charging head lower cover, and the charging core is connected with a battery in the data operation box through a wire.
6. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: and the lower cover of the operation box is also provided with a radiating fin which is adhered below the main control board through a heat conducting adhesive.
7. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the wireless communication module is a WIFI communication module or a Bluetooth communication module.
8. The back pad for non-contact detection of emotional stress based on heart rate variability of claim 1, wherein: the surface of the rear cover is provided with heat dissipation holes which are uniformly distributed.
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| CN109044322A (en) * | 2018-08-29 | 2018-12-21 | 北京航空航天大学 | A kind of contactless heart rate variability measurement method |
| CN109431482A (en) * | 2018-11-13 | 2019-03-08 | 杭州菲诗奥医疗科技有限公司 | A kind of mattress and its detection method based on contactless detection heart rate variability |
| CN109276241B (en) * | 2018-11-28 | 2022-08-09 | 深圳市元征科技股份有限公司 | Pressure identification method and equipment |
| CN110811646B (en) * | 2019-10-23 | 2022-05-17 | 北京点滴灵犀科技有限公司 | Emotional stress comprehensive detection and analysis method and device |
| CN112294264A (en) * | 2020-10-31 | 2021-02-02 | 无锡中物云信息科技有限公司 | Sleep staging method based on BCG and blood oxygen saturation rate |
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