CN111796697A - Mouse of intelligent monitoring human health state - Google Patents
Mouse of intelligent monitoring human health state Download PDFInfo
- Publication number
- CN111796697A CN111796697A CN201910643432.7A CN201910643432A CN111796697A CN 111796697 A CN111796697 A CN 111796697A CN 201910643432 A CN201910643432 A CN 201910643432A CN 111796697 A CN111796697 A CN 111796697A
- Authority
- CN
- China
- Prior art keywords
- heart rate
- module
- mouse
- data transmission
- acquisition module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 230000036541 health Effects 0.000 title claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 45
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000000034 method Methods 0.000 claims description 11
- 230000036772 blood pressure Effects 0.000 claims description 10
- 230000003862 health status Effects 0.000 claims description 8
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 6
- 201000010099 disease Diseases 0.000 abstract description 5
- 230000008859 change Effects 0.000 description 22
- 239000000306 component Substances 0.000 description 12
- INGWEZCOABYORO-UHFFFAOYSA-N 2-(furan-2-yl)-7-methyl-1h-1,8-naphthyridin-4-one Chemical compound N=1C2=NC(C)=CC=C2C(O)=CC=1C1=CC=CO1 INGWEZCOABYORO-UHFFFAOYSA-N 0.000 description 9
- 108010064719 Oxyhemoglobins Proteins 0.000 description 9
- 238000010521 absorption reaction Methods 0.000 description 9
- 206010000891 acute myocardial infarction Diseases 0.000 description 9
- 108010002255 deoxyhemoglobin Proteins 0.000 description 9
- 108010054147 Hemoglobins Proteins 0.000 description 7
- 102000001554 Hemoglobins Human genes 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 230000003247 decreasing effect Effects 0.000 description 6
- 230000003595 spectral effect Effects 0.000 description 6
- 230000035882 stress Effects 0.000 description 6
- 230000002889 sympathetic effect Effects 0.000 description 6
- 206010019280 Heart failures Diseases 0.000 description 5
- 210000000467 autonomic pathway Anatomy 0.000 description 5
- 230000001105 regulatory effect Effects 0.000 description 5
- 210000001186 vagus nerve Anatomy 0.000 description 5
- 206010042434 Sudden death Diseases 0.000 description 4
- 206010012601 diabetes mellitus Diseases 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000007774 longterm Effects 0.000 description 4
- 230000033764 rhythmic process Effects 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 208000024172 Cardiovascular disease Diseases 0.000 description 3
- 208000028389 Nerve injury Diseases 0.000 description 3
- 206010003119 arrhythmia Diseases 0.000 description 3
- 230000006793 arrhythmia Effects 0.000 description 3
- 239000008280 blood Substances 0.000 description 3
- 210000004369 blood Anatomy 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 208000010125 myocardial infarction Diseases 0.000 description 3
- 210000005036 nerve Anatomy 0.000 description 3
- 230000008764 nerve damage Effects 0.000 description 3
- 238000004393 prognosis Methods 0.000 description 3
- 238000011282 treatment Methods 0.000 description 3
- 206010049993 Cardiac death Diseases 0.000 description 2
- 208000031229 Cardiomyopathies Diseases 0.000 description 2
- 206010008479 Chest Pain Diseases 0.000 description 2
- 206010056370 Congestive cardiomyopathy Diseases 0.000 description 2
- 206010011906 Death Diseases 0.000 description 2
- 201000010046 Dilated cardiomyopathy Diseases 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 210000003403 autonomic nervous system Anatomy 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 150000003943 catecholamines Chemical class 0.000 description 2
- 208000029078 coronary artery disease Diseases 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 206010042772 syncope Diseases 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 208000012219 Autonomic Nervous System disease Diseases 0.000 description 1
- 206010008874 Chronic Fatigue Syndrome Diseases 0.000 description 1
- 208000002249 Diabetes Complications Diseases 0.000 description 1
- 206010012655 Diabetic complications Diseases 0.000 description 1
- 208000001640 Fibromyalgia Diseases 0.000 description 1
- 206010019233 Headaches Diseases 0.000 description 1
- 208000037093 Menstruation Disturbances Diseases 0.000 description 1
- 206010027339 Menstruation irregular Diseases 0.000 description 1
- 208000008589 Obesity Diseases 0.000 description 1
- 206010033557 Palpitations Diseases 0.000 description 1
- 208000013738 Sleep Initiation and Maintenance disease Diseases 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000002567 autonomic effect Effects 0.000 description 1
- 238000010009 beating Methods 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 239000012503 blood component Substances 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 230000036760 body temperature Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000005189 cardiac health Effects 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 208000035475 disorder Diseases 0.000 description 1
- 230000009429 distress Effects 0.000 description 1
- 208000002173 dizziness Diseases 0.000 description 1
- 201000006549 dyspepsia Diseases 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 208000024756 faint Diseases 0.000 description 1
- 206010016256 fatigue Diseases 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 231100000869 headache Toxicity 0.000 description 1
- 230000001121 heart beat frequency Effects 0.000 description 1
- 230000010247 heart contraction Effects 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 230000004217 heart function Effects 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 206010022437 insomnia Diseases 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 208000018883 loss of balance Diseases 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000029766 myalgic encephalomeyelitis/chronic fatigue syndrome Diseases 0.000 description 1
- 230000002182 neurohumoral effect Effects 0.000 description 1
- 235000020824 obesity Nutrition 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 210000004789 organ system Anatomy 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 210000005037 parasympathetic nerve Anatomy 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000004962 physiological condition Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 230000011514 reflex Effects 0.000 description 1
- 230000036387 respiratory rate Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000391 smoking effect Effects 0.000 description 1
- 208000011580 syndromic disease Diseases 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 208000003663 ventricular fibrillation Diseases 0.000 description 1
- 230000002618 waking effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
- G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
- G06F3/0354—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks
- G06F3/03543—Mice or pucks
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
-
- 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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
- A61B5/02427—Details of sensor
- A61B5/02433—Details of sensor for infrared radiation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6897—Computer input devices, e.g. mice or keyboards
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/016—Input arrangements with force or tactile feedback as computer generated output to the user
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Cardiology (AREA)
- Human Computer Interaction (AREA)
- General Physics & Mathematics (AREA)
- Physiology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a mouse for intelligently monitoring the health state of a human body, which comprises a mouse body, a heart rate acquisition module, a vibration feedback module, a data transmission module and a terminal data processing module, wherein the heart rate acquisition module is connected with the vibration feedback module; the heart rate acquisition module, the vibration feedback module and the data transmission module are integrated in the mouse body; the heart rate acquisition module is connected with the data transmission module; the vibration feedback module is connected with the data transmission module; and the terminal data processing module is connected with the data transmission module. The invention can monitor the heart rate of a user using the mouse in real time, record the heart rate data of the user for a long time, observe the heart rate fluctuation to obtain the HRV value, detect the health condition of the user and prevent diseases.
Description
Technical Field
The invention belongs to the technical field of human health monitoring, and particularly relates to a mouse with a health monitoring function.
Background
At the time of hospital interrogation, a doctor typically examines a patient for 4 vital signs: heart rate, blood pressure, respiratory rate, and body temperature. The cardiologist, sunet Mittal, at the new jersey brook hospital, introduced that examining these vital signs 1 or 2 times a year is often inadequate and also tends to cause the physician to make medical decisions based on "wrong data from wrong time". This is due, in part, to the fact that existing brain and heart tests do not reveal what symptoms had occurred before the patient was ill. Physicians prefer to have a device that can help detect potential problems that exist before a patient faints or has suffered a stroke.
In fact, most of early heart attacks and stroke can be prevented, and the most important thing is to find early treatment. The symptoms of palpitation, chest distress, chest pain, dizziness or syncope which seem to be out of sight are likely to be distress signals sent by the heart, and most people miss the optimal treatment time due to the carelessness.
Each pulse of the human pulse represents an effective heart beat, and the number of heart beats per minute is the heart rate. Heart rate is the most direct marker reflecting our heart health. The heart is the power of blood pumping and also the guarantee of the normal operation of each organ system and the whole body.
Due to the complexity of heart disease, multiple examinations to exclude interfering factors are necessary. In addition to hospital visits, long-term continuous monitoring of heart rate can establish a first line of defense against cardiovascular disease in high risk populations. Through continuous and accurate electrocardio monitoring and analysis, abnormal heart activity expressions are found in time, and valuable treatment and intervention time is won for patients.
Society is continuously developing, and with the popularization of networks, people use computers for longer and longer time. For the white collar on duty, the computer is used for a longer time, and except for the white collar on duty, the white collar goes home or goes to the internet, which means that the white collar on duty always goes with the computer except for sleeping. In addition, the sudden death event of the internet bar happens occasionally, different heart rate conditions and physical conditions can be known by means of a mouse capable of intelligently detecting the heart rate, and the sudden death event can help to prevent diseases.
Meanwhile, the heart rate fluctuation condition of the detected person can be observed through long-term heart rate detection. The theory of Heart Rate Variability (HRV) suggests that fluctuations in heart rate are not incidental but rather regulated by the neurohumoral functions of the receptors, responding to different physiological conditions or certain pathological conditions. The loss of balance in the autonomic nervous system caused by stress can lead to a variety of functional disorders, including: violent, indigestion, chronic fatigue syndrome, headache, fibromyalgia, insomnia, irregular menstruation, cardiovascular diseases, diabetes, obesity, etc. The balance of the autonomic nervous system can thus be assessed and analyzed in terms of factors related to heart rate variability. Therefore, the analysis of the heart rate variation can be used for sub-health physical examination, and the fatigue syndrome can be effectively diagnosed. The clinical application range includes: stress management, alerting chronic autonomic nervous system disorders due to stress, assessing the effect of stress relief therapy, assessing autonomic nervous activity, balancing body structure function, APG testing blood circulation system, and detecting elasticity and aging degree of blood vessels.
Clinical experiments show that: HRV of patients with cardiovascular diseases such as coronary heart disease, heart failure, arrhythmia and dilated cardiomyopathy is obviously reduced, which indicates that autonomic nerve function is damaged in the process of disease development. HRV is used as a predictor of sudden death and severe arrhythmia in patients with Acute Myocardial Infarction (AMI); as an index for evaluating the impaired autonomic nerve function of a diabetic patient. Healthy persons have a higher HRV at night, morning to peak, with the HRV gradually decreasing with age. The HRV has the strongest correlation with sudden death, the death rate of the patient with SDNN <50ms is 5.4 times higher than that of the patient with SDNN >100ms, and the predicted value of the cardiac death is the highest when the SDNN <50ms predicts the occurrence of the cardiac death. HRV is the most important index for detecting patients with high malignant arrhythmia, and the HRV before ventricular fibrillation is obviously reduced. The HRV is significantly correlated with the change of cardiac function (significantly decreased HRV) in heart failure patients, and the average heart rate is increased and the HRV is decreased in heart failure patients. Patients with dilated cardiomyopathy have worsening parasympathetic nerve damage, hyperfunction of sympathetic nerve, and decreased HRV, which are more obvious at night along with the hypofunction of cardiac contraction. The HRV of diabetic patients is obviously reduced, and the HRV is a sensitive index for early diagnosis of diabetic autonomic nerve damage and is related to the occurrence of diabetic complications. Moderate smoking patients are accompanied by sympathetic hyperfunction, marked by vagus nerve damage, and decreased HRV. HRV is closely related to high-risk types of AMI patients. Acute HRV of AMI patients is obviously reduced (without clinical significance), and the HRV continuously rises back to a normal level two weeks after AMI indicates that the prognosis is poor. There are studies demonstrating that catecholamine levels in AMI patients after 4h are negatively correlated with HRV. In patients with coronary heart disease, the peak HRV appeared 3-6 in the early morning, and the HRV rapidly decreased after waking up at 6 hours of sleep, so changes in HRV were considered to be closely related to catecholamine levels in the blood. AMI patients SDNN <50ms was high risk patients and <100ms was moderate risk patients.
Therefore, those skilled in the art are dedicated to develop a mouse capable of intelligently monitoring the health status of a human body, and the mouse can monitor the health status of a mouse user, so as to find problems in time and prevent diseases.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to realize long-term monitoring of the heart rate of a user using a mouse, the HRV and other physical health conditions, aiming at the need of long-term monitoring of the health status of the human body in real life. In order to achieve the purpose, the invention provides a mouse for intelligently monitoring the health state of a human body, which comprises a mouse body, a heart rate acquisition module, a data transmission module, a vibration feedback module and a terminal data processing module, wherein the heart rate acquisition module, the vibration feedback module and the data transmission module are integrated in the mouse body; the heart rate acquisition module is connected with the data transmission module; the vibration feedback module is connected with the data transmission module; and the terminal data processing module is connected with the data transmission module. .
Furthermore, the heart rate acquisition module adopts a reflection type heart rate acquisition module or a video heart rate acquisition module.
Further, the data transmission module may be a wired data transmission module or a WiFi wireless transmission module.
Further, reflective heart rate collection module includes photoelectric detector, analog-to-digital converter, 730nm and 850nm light source, and photoelectric detector surveys the light intensity change of two kinds of light, converts light signal into the signal of telecommunication, and analog-to-digital converter converts analog signal into digital signal, handles signal of telecommunication transmission to terminal data processing module through data transmission module, calculates the concentration change that obtains oxygenated hemoglobin and the change of the concentration of deoxyhemoglobin through light reflection to measure the rhythm of the heart.
Further, video rhythm of heart collection module includes infrared detector, white light source and camera, in order to protect individual privacy, infrared detector detects whether the people is using mouse that is whether the palm covers mouse, then think when detecting the palm and cover and use mouse, video rhythm of heart collection module starts this moment, white light source is luminous, the palm position photo is shot to the camera, convey the photo to terminal data processing module through data transmission module and handle, obtain human rhythm of heart and HRV data.
Further, wifi wireless transmission module gives end user with the data transmission who records, and end user can look over measurand heart rate data.
Further, the vibration feedback module is connected with the data transmission module, and the vibration feedback module is set to remind the user through vibration when the pressure of the user is detected to be larger or the physical health condition is not good.
A method for monitoring human health information by using a mouse device for intelligently monitoring human health states comprises the following steps:
step one, when a user uses a mouse, a heart rate acquisition module and a data transmission module start to work;
secondly, a heart rate acquisition module acquires heart rate data of a user;
and step three, the data transmission module transmits the obtained data to the terminal.
And further, the fourth step is that the terminal data processing module records the heart rate characteristics of the user for a long time and detects the HRV of the user.
Further, the monitored human health information includes characteristics of heart rate, blood pressure, HRV and the like.
The principle of measuring the blood oxygen saturation, the heart rate and the blood pressure by adopting the reflection type heart rate acquisition module is as follows: the beating of the heart causes changes in the blood components, i.e., the oxygenated and deoxygenated hemoglobin concentrations, of other parts of the body, including the measurement site; the concentration changes of oxyhemoglobin and deoxyhemoglobin can cause the change of the absorption coefficient of light according to the improved beer-Lambert law, the light emitted by the light source of the reflective heart rate acquisition module is modulated by the changes, the changed light intensity is converted into an electric signal by the photoelectric detector, and the electric signal is further converted into a digital signal by the analog-digital converter, so that pulse wave data are obtained. Because the pulse waves are caused by the heart beat, the frequency of the pulse waves is consistent with the heart beat frequency, the heart rate can be obtained by calculating the peak periods of the two pulse waves and taking the reciprocal, and the heart rate change curve of the tested person can be obtained by extracting the frequency of the pulse waves in each period and recording. On the other hand, a secondary guide is obtained by comparing the complete pulse waves, the pulse wave propagation time is obtained through the secondary guide of the pulse waves, and then the blood pressure is calibrated by utilizing the pulse wave propagation time.
The principle of collecting the heart rate by using a video heart rate collecting module is that the sum is carried out on three components of RGB in each frame of image, then the average value is taken to obtain R, G and B, and then R, G and B are converted to IQ components. And acquiring the variation quantity of IQ components of all image frames in the video along with time, performing trend-removing processing on the variation quantity of the IQ components along with the time, and projecting all the IQ components onto an IQ plane to acquire an elliptical distribution model. And fitting the long axis of the ellipse model by using an RANSAC algorithm, rotating the ellipse model according to the fitted long axis, and extracting the change E _ L of the skin color projected to the long axis of the ellipse model along with the heart rate. And carrying out band-pass filtering on the E _ L by using a Butterhols filter, decomposing the E _ L subjected to the band-pass filtering by using a complete ensemble empirical mode decomposition method to obtain a frequency v corresponding to a frequency spectrum peak value, and taking an eigenmode component of the frequency spectrum peak value in a heart rate range as a heart rate component of the video, wherein the frequency v is the heart rate of the measured person. After a heart rate curve (namely pulse wave) is obtained, a secondary guide is obtained by comparing the complete pulse wave, the pulse wave propagation time is obtained through the secondary guide of the pulse wave, and then the blood pressure is calibrated by utilizing the pulse wave propagation time.
The terminal data processing module records heart rate data of a user for a long time, observes heart rate fluctuation of the user, detects HRV of the heart rate data by adopting a 24-hour Holter and evaluates the heart rate data. And simultaneously, two methods of time domain analysis and frequency domain analysis are used, and four indexes such as SDNN, SDANN, rMSSD, HRV triangular index (when the heart rate variability is large, the triangular index is also large) and the like are adopted.
(1) Time domain analysis: SDNN is standard deviation of an R-R interval within a certain time interval (24 h), and the normal value is 50-100 ms, which reflects that HRV is still good; SDNN >100ms, reflecting good HRV; SDSNN <50ms, reflecting poor HRV. 24hSD can be used as an independent index for predicting the prognosis of AMI.
(2) Frequency domain analysis: generally, the frequency spectrum curve is divided into two frequency bands, namely a low frequency band (0.04-0.15 Hz) and a high frequency band (0.15-0.40 Hz). The spectral curve of the instantaneous heart rate is related to the autonomic nerve regulation function, and when the sympathetic nerve is excited, the low frequency band of the spectral curve has a peak; when the vagus nerve is excited, the high frequency segment of the spectral curve spikes. A peak of 0.15Hz or less is a low frequency peak (LF); the peak at 0.15-0.50 Hz is called high frequency peak (HF); HF is vagally regulated, and LF is co-regulated by the sympathetic and vagus nerves.
(3) Heart rate trend graph: the change of the heart rate along with the time can be represented by the change of an R-R interval, and in order to observe the change trend of the instantaneous heart rate along with the time, the variation condition of the heart rate is usually visually reflected by an instantaneous heart rate change trend graph.
(4) Histogram of heart rate variability: the specified R-R interval is a sampling interval to count the number of heartbeats of different R-R intervals. For normal people with great heart rate variation, the R-R interval histogram is in an open type multi-peak shape; when heart rate variability such as myocardial infarction, heart failure and cardiomyopathy is reduced, the R-R interval histogram is mostly in a unimodal shape. When the graph is high and narrow, the heart rate variability is small; when the pattern is low and wide, the heart rate variability is large.
Drawings
FIG. 1 is an overall block diagram of a preferred embodiment of the present invention;
FIG. 2 is a block diagram of the preferred embodiment of the invention;
FIG. 3 is a diagram of the light transmission path of the reflection type heart rate acquisition module according to the preferred embodiment of the invention;
FIG. 4 is a schematic diagram of a video heart rate acquisition module apparatus according to a preferred embodiment of the present invention;
FIG. 5 is a flow chart of a video heart rate acquisition module process according to a preferred embodiment of the present invention;
in the figure, 1-the whole mouse, 2-the heart rate acquisition module, 3-the data transmission line, 4-the palm, 5-730nm light source, 6-850nm light source, 7-the photoelectric detector, 8-the wifi wireless transmission module, 9-the white light source, 10-the camera, 11-the vibration feedback module, 12-the infrared detector, 13-the analog-to-digital converter.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1 and 2, a mouse for intelligently monitoring the health status of a human body comprises a mouse body 1, a heart rate acquisition module, a data transmission module, a vibration feedback module and a terminal data processing module, wherein the heart rate acquisition module, the vibration feedback module and the data transmission module are integrated in the mouse body; the heart rate acquisition module is connected with the data transmission module; the vibration feedback module is connected with the data transmission module; and the terminal data processing module is connected with the data transmission module. The heart rate acquisition module acquires human health information data, the data transmission module transmits the data to the terminal data processing module for processing, the terminal data processing module analyzes the data, and when the abnormal condition is found, the data transmission module is connected with the vibration feedback module, so that the vibration feedback module sends out vibration to warn a user.
In a first embodiment, a reflex heart rate acquisition module is used to acquire the heart rate.
As shown in fig. 3, the reflective heart rate acquisition module includes a 5-730nm light source, a 6-850nm light source, a 7-photoelectric detector, a 12-infrared detector and a 13-analog-to-digital converter, the 12-infrared detector is connected with the 7-photoelectric detector, the 7-photoelectric detector is connected with the 13-analog-to-digital converter, and the 13-analog-to-digital converter is connected with the 8-wifi wireless transmission module. When the 12-infrared detector detects that the 4-palm covers the mouse, the 5-730nm light source and the 6-850nm light source alternately emit light, the 7-photoelectric detector detects light intensity change, the light signals are converted into electric signals and transmitted to the terminal data processing module for analysis, the heart rate and blood pressure of the human body are obtained, the heart rate data of the human body is recorded for a long time, and the HRV data can be obtained through analysis.
A modified beer-lambert law may be used to convert the acquired intensity changes into oxyhemoglobin and deoxyhemoglobin concentration changes. This law is based on the following assumptions: the absorption is uniform throughout the illuminated area and the scattering loss time variation is constant. According to the original light intensity change data acquired by the detector, attenuation can be calculated by using a formula 1:
wherein A is absorbance;
is the incident light intensity;
is the light intensity of the incident light after passing through the medium; l is the average length of the photon migration path in the medium;
is the absorption coefficient of the medium; g is a geometric scattering factor, which is related to the geometry of the medium and can be considered as a constant. The geometric scattering factor G is generally unknown, so it is necessary to eliminate the influence of the geometric scattering factor G by calculating the variation of the emergent light intensity with respect to the initial state using equation 2.
The average propagation path length L of photons in the medium is related to the distance between the light source and the detector, as shown in equation (3). Wherein the DPF is a differential path factor, is related to the structure of the detected medium, and can be generally obtained through Mont Carlo simulation; d is the distance between the light source and the detector.
wherein
Is the change in concentration of oxygenated hemoglobin;
is the change in concentration of deoxygenated hemoglobin;
is the molar absorption coefficient of oxyhemoglobin at an incident light wavelength of 730 nm;
is the molar absorption coefficient of oxyhemoglobin at an incident light wavelength of 850 nm;
is the molar absorption coefficient of deoxyhemoglobin at an incident wavelength of 730 nm;
is the molar absorption coefficient of deoxyhemoglobin at an incident wavelength of 850 nm.
Concentration change of oxyhemoglobin by equation 4
Change in concentration with deoxyhemoglobin
Comprises the following steps:
substituting formula 2 into formula 5 yields:
because near-infrared light with different wavelengths propagates in the same medium with the same path stiffness, the near-infrared light with different wavelengths propagates in the same medium with the same path stiffness
The incident wavelengths at 730nm and 850nm can be considered the same (there is a very small, negligible difference in reality), i.e.
=
Equation 6 can be simplified as:
in the tissueTotal hemoglobin concentration change
The concentration change of the oxyhemoglobin and the deoxyhemoglobin can be obtained, and the specific formula is as follows:
therefore, it is possible to detect the relative concentration changes of deoxyhemoglobin, oxyhemoglobin, and total hemoglobin by detecting the change of near-infrared light from the initial state after passing through the tissue over a period of time. The heart rate variation curve of the tested person can be obtained through the relative concentration variation of the deoxyhemoglobin, the oxyhemoglobin and the total hemoglobin. After a heart rate curve (namely pulse wave) is obtained, a secondary guide is obtained by comparing the complete pulse wave, the pulse wave propagation time is obtained through the secondary guide of the pulse wave, and then the blood pressure is calibrated by utilizing the pulse wave propagation time.
In a second embodiment, a video heart rate detection module is used to detect heart rate.
As shown in fig. 4, the video heart rate detection module includes a 12-infrared detector, a 9-white light source and a 10-camera, the 12-infrared detector is connected with the 9-white light source, and the 10-camera is connected with the 8-wireless data transmission module. When the 4-palm covers the mouse, the 12-infrared detector detects the 4-palm to judge that the mouse is in a use state, the 9-white light source emits light, the 10-camera shoots a picture of the palm part, the picture is transmitted to the terminal data processing module through the data transmission module to be processed, the heart rate and the blood pressure of the human body are obtained, the heart rate data of the human body is recorded for a long time, and the HRV data can be obtained through analysis.
As shown in fig. 5, the flow of processing data using the video heart rate acquisition module is as follows.
Firstly, summing the RGB component values in each frame image, then taking the average value to obtain R, G and B, and then transforming the matrix
Separation of brightness and color is achieved, and the average of all images on the IQ component is obtained. Carrying out detrending processing on IQ components of all image frames in the video by utilizing a smooth prior method to obtain
And
and will be
And
and projecting the data to an IQ plane to obtain an elliptical distribution model of skin color changing along with the heart rate. Equation (10) for detrending based on smooth priors is as follows:
wherein
Is the initial signal of the signal that is,
is the signal after the detrended signal and,
is an identity matrix
Matrix of
Can be expressed as:
according to
And
projecting the elliptic distribution model on an IQ plane, fitting the long axis of the elliptic distribution model by using a RANSAC algorithm to obtain the slope of the long axis
Determining the inclination angle of the major axis
According to the angle of inclination
The rotated ellipse model is obtained using equation (12).
Order to
And acquiring a heart rate signal of skin color projected on the long axis of the ellipse model in an IQ space. To pair
Performing band-pass filtering with a Butterhols filter, and decomposing the band-pass filtered signal with a complete ensemble empirical mode decomposition method
And selecting the eigen-mode component of the frequency corresponding to the maximum amplitude within the heart rate range as a heart rate component, wherein the frequency corresponding to the maximum amplitude is used as the heart rate of the person in the video. Obtaining heart rateAfter the curve (namely the pulse wave), a secondary guide is obtained by comparing the complete pulse wave, the pulse wave propagation time is obtained through the secondary guide of the pulse wave, and then the blood pressure is calibrated by utilizing the pulse wave propagation time.
The terminal data processing and analyzing module records heart rate data of a user for a long time, observes heart rate fluctuation of the user, detects HRV of the user by adopting a 24-hour Holter and evaluates the HRV. And simultaneously, two methods of time domain analysis and frequency domain analysis are used, and four indexes such as SDNN, SDANN, rMSSD, HRV triangular index (when the heart rate variability is large, the triangular index is also large) and the like are adopted.
(1) Time domain analysis: SDNN is the standard deviation of the R-R interval over a certain time interval (24 h). The normal value is 50-100 ms, and the HRV is reflected to be good; SDNN >100ms, reflecting good HRV; SDSNN <50ms, reflecting poor HRV. 24hSD can be used as an independent index for predicting the prognosis of AMI.
(2) Frequency domain analysis: generally, the frequency spectrum curve is divided into two frequency bands, namely a low frequency band (0.04-0.15 Hz) and a high frequency band (0.15-0.40 Hz). The spectral curve of the instantaneous heart rate is related to the autonomic nerve regulation function, and when the sympathetic nerve is excited, the low frequency band of the spectral curve has a peak; when the vagus nerve is excited, the high frequency segment of the spectral curve spikes. A peak of 0.15Hz or less is a low frequency peak (LF); the peak at 0.15-0.50 Hz is called high frequency peak (HF); HF is vagally regulated, and LF is co-regulated by the sympathetic and vagus nerves.
(3) Heart rate trend graph: the change of the heart rate along with the time can be represented by the change of an R-R interval, and in order to observe the change trend of the instantaneous heart rate along with the time, the variation condition of the heart rate is usually visually reflected by an instantaneous heart rate change trend graph.
(4) Histogram of heart rate variability: the specified R-R interval is a sampling interval to count the number of heartbeats of different R-R intervals. For normal people with great heart rate variation, the R-R interval histogram is in an open type multi-peak shape; when heart rate variability such as myocardial infarction, heart failure and cardiomyopathy is reduced, the R-R interval histogram is mostly in a unimodal shape. When the graph is high and narrow, the heart rate variability is small; when the pattern is low and wide, the heart rate variability is large.
The heart rate and the HRV condition of the user are recorded for a long time, so that the user can know the physical condition of the user. When the stress of the user is monitored to be large or the physical health state of the user is not good, the mouse can remind the user through the vibration feedback module, and the office leader can make targeted instructions in time after obtaining related data, such as rest on vacation, medical treatment in time and the like, so that the mouse has a good precaution effect on the stress or diseases and the like.
The above embodiments are only routine descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and those skilled in the art can make various changes and modifications to the technical solution of the present invention without departing from the design concept of the present invention.
Claims (10)
1. A mouse capable of intelligently monitoring the health state of a human body is characterized by comprising a mouse body, a heart rate acquisition module, a vibration feedback module, a data transmission module and a terminal data processing module; the heart rate acquisition module, the vibration feedback module and the data transmission module are integrated in the mouse body; the heart rate acquisition module is connected with the data transmission module; the vibration feedback module is connected with the data transmission module; and the terminal data processing module is connected with the data transmission module.
2. The mouse of claim 1, wherein the data transmission module is a wired data transmission module or a wifi wireless data transmission module.
3. The mouse for intelligently monitoring the health status of a human body according to claim 1, wherein the heart rate acquisition module is a reflection type heart rate acquisition module or a video heart rate acquisition module.
4. The mouse of claim 1, wherein the vibration feedback module is connected to the data transmission module using a linear vibration motor, and the vibration feedback module is configured to vibrate to alert the user when the user's stress is detected to be high or the health condition is not good.
5. The mouse for intelligently monitoring the health status of a human body according to claim 2, wherein the reflective heart rate acquisition module comprises a photodetector, an analog-to-digital converter, 730nm and 850nm light sources.
6. The mouse for intelligently monitoring the health status of a human body according to claim 2, wherein the video heart rate acquisition module comprises an infrared detector, an analog-to-digital converter, a white light source and a camera.
7. The mouse for intelligently monitoring the health status of a human body according to claim 5, wherein the video heart rate acquisition module is configured to start the operation of the camera only when the infrared detector detects that the user starts using the mouse, and record data such as the heart rate of the user.
8. A method of monitoring human health information using the device of claim 1, comprising the steps of:
step one, when a user uses a mouse, a heart rate acquisition module and a data transmission module start to work;
secondly, a heart rate acquisition module acquires heart rate data of a user;
and step three, the data transmission module transmits the obtained data to the terminal data processing module for processing and analysis.
9. The method of claim 7, further comprising, after the step three, a step four of recording the heart rate characteristics of the user for a long period of time and detecting the HRV thereof.
10. The method of claim 7, wherein the monitored health information comprises heart rate, blood pressure, HRV, and other characteristics.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910281827 | 2019-04-09 | ||
| CN2019102818277 | 2019-04-09 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111796697A true CN111796697A (en) | 2020-10-20 |
Family
ID=72805389
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910643432.7A Pending CN111796697A (en) | 2019-04-09 | 2019-07-17 | Mouse of intelligent monitoring human health state |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111796697A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114520054A (en) * | 2020-11-18 | 2022-05-20 | 英业达科技有限公司 | Heart failure prediction module and heart failure prediction method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750015A (en) * | 2011-04-22 | 2012-10-24 | 鸿富锦精密工业（深圳）有限公司 | Mouse with physiological parameter measurement function |
| CN103462596A (en) * | 2013-09-03 | 2013-12-25 | 中国农业大学 | Intelligent health mouse for human sign information and real-time monitoring system |
| CN107153470A (en) * | 2016-03-02 | 2017-09-12 | 中国科学院软件研究所 | A kind of multichannel health mouse system and health monitoring method |
| CN209962229U (en) * | 2019-04-09 | 2020-01-17 | 亘冠智能技术（杭州）有限公司 | Mouse of intelligent monitoring human health state |
-
2019
- 2019-07-17 CN CN201910643432.7A patent/CN111796697A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750015A (en) * | 2011-04-22 | 2012-10-24 | 鸿富锦精密工业（深圳）有限公司 | Mouse with physiological parameter measurement function |
| CN103462596A (en) * | 2013-09-03 | 2013-12-25 | 中国农业大学 | Intelligent health mouse for human sign information and real-time monitoring system |
| CN107153470A (en) * | 2016-03-02 | 2017-09-12 | 中国科学院软件研究所 | A kind of multichannel health mouse system and health monitoring method |
| CN209962229U (en) * | 2019-04-09 | 2020-01-17 | 亘冠智能技术（杭州）有限公司 | Mouse of intelligent monitoring human health state |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114520054A (en) * | 2020-11-18 | 2022-05-20 | 英业达科技有限公司 | Heart failure prediction module and heart failure prediction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11445983B2 (en) | Non-invasive determination of disease states | |
| US11160459B2 (en) | Monitoring health status of people suffering from chronic diseases | |
| RU2652071C2 (en) | Device and method for determining the blood oxygen saturation of a subject | |
| Ray et al. | A review of wearable multi-wavelength photoplethysmography | |
| JP3824615B2 (en) | Sleep apnea diagnostic apparatus and method | |
| US8428673B2 (en) | Medical measuring device | |
| JP2019524187A (en) | Method and apparatus for determining respiratory information of a subject | |
| CN111386068A (en) | Camera-based pressure measurement system and method | |
| US20220039705A1 (en) | System and method for non-invasive monitoring of hematocrit concentration | |
| US20230148961A1 (en) | Systems and methods for computationally efficient non-invasive blood quality measurement | |
| JP2023532319A (en) | Apparatus and method for compensating assessment of peripheral arterial tone | |
| JP5080550B2 (en) | Autonomic nerve function evaluation device | |
| Surrel et al. | Low-power wearable system for real-time screening of obstructive sleep apnea | |
| US10327648B2 (en) | Blood vessel mechanical signal analysis | |
| CN209962229U (en) | Mouse of intelligent monitoring human health state | |
| CN111796697A (en) | Mouse of intelligent monitoring human health state | |
| US10827972B1 (en) | Sleep staging using an in-ear photoplethysmography (PPG) | |
| US20240016401A1 (en) | Systems and methods for analysis of pulse and physiological, pathological, and emotional state and applications thereof | |
| US20200205707A1 (en) | Wearable sensor for acquisition of biometrics data | |
| Ouchi et al. | Simultaneous measurement under resting-state of autonomic nervous activity and brain activity by near-infrared spectroscopy alone | |
| JP2000262480A (en) | Homeostasis maintenance evaluation device | |
| Khong et al. | Contact and non-contact heart beat rate measurement techniques: Challenges and issues | |
| Khong et al. | The evolution of heart beat rate measurement techniques from contact based photoplethysmography to non-contact based photoplethysmography imaging | |
| Daly | Video camera monitoring to detect changes in haemodynamics | |
| US20050027183A1 (en) | Method for non-invasive monitoring of blood and tissue glucose |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20201020 |