CN111276249A - Human behavior activity degree measuring method and system based on human behavior information entropy - Google Patents
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Abstract
The invention relates to a human behavior activity degree measuring method and system based on human behavior information entropy, wherein the method is characterized by comprising a human behavior activity HBV method: the complexity of a human body system macroscopic system is measured by calculating the entropy of the human body behavior continuous dynamic data sequence information, and then the activity degree of the human body microscopic system is measured. The invention solves the problems that the prior art can not accurately and systematically measure the whole function state of the human body on the whole so as to accurately judge the whole health and function conditions of the human body and lacks of unified quantitative standards, and has great significance for the measurement of the health state of the human body, the intervention of active health behaviors and the monitoring of scientific exercise training.
Description
Technical Field
The invention belongs to the technical field of exercise health and exercise training monitoring, and particularly relates to a human behavior vitality measurement method and system based on human behavior information entropy.
Background
The active health is a novel health medical mode provided by Li auspicious minister researchers, and the core of the active health is that the human body needs to be actively pushed away from a relative balance state, namely a normal range in which disease medicine refers, so that the self-organization behavior of the human body is excited, and the purpose of improving the functional state of the human body is achieved. The core concept of the method has great guiding significance for scientific training and health intervention. How to measure the functional state of the human body is a key technology and is also the basis for reasonably arranging the load of intervention and training. At present, the method which is widely applied mainly divides Exercise intensity by the percentage of maximum oxygen uptake, blood lactic acid content, quiet heart rate and the percentage of maximum heart rate, and a Cardio-Pulmonary Exercise test (CPET) of a human body evaluates the movement of the human body by measuring the maximum oxygen uptake and is used as a main mode for guiding the formulation of an Exercise training scheme (prescription) and evaluating the rehabilitation efficacy; heart rate data is often obtained by means of heart rate belt (table) and other methods, however, the human body system is a complex and huge system with nonlinear, uncertain and random subsystems working cooperatively, and the above conventional methods attempt to collect cross-sectional static data of skeletal muscle, cardiovascular and respiratory subsystems and other subsystems of the human body subsystems in a 'photographing' manner (as shown in fig. 5), and then achieve the purpose of overall measurement by independent or linear calculation, such as establishing constants to evaluate exercise intensity and overall functional state by maximum heart rate percentage (as shown in fig. 6) (see: JANSSEN p. lactate Threshold Training [ M ]. IL, USA: HumanKinetics,2001. changyu, zhuangyui, chenjikuei. chinese athlete cardiovascular physiological constant [ M ]. chinese athlete physiological constant and nutritional status survey. beijing; national sports press publication, 2006. naval, changyu, chenjikuei, junji aerobic endurance physiological constant [ M ]. chinese athlete and nutritional status regulation) Checking Beijing; people's sports press 2006), according to the complex scientific theory, the exercise load is the integral response of the human body system to the stimulation, the regulation and control process involves various functional subsystems such as the respiration, the nerve and the brain, all the subsystems are mutually influenced, the relationship between the signal and the effect is nonlinear, and the complex interrelation of multiple coexistence of time and space exists (see for example: the third generation of the study, the new theory system of the integrated physiology and medicine, human body function integrated autonomous regulation [ J ], the journal of chinese circulation, 2013,28(2), 88-92, etc., neglecting the whole use of isolated indexes and sampling analysis mode can ignore a large amount of important information hidden in continuous dynamic data, although many studies prove that CPET is effective in guiding and formulating individualized high-intensity exercise training and rehabilitation therapy for stable heart failure (see: the method is characterized in that the method is a preliminary summary report [ J ] of the rehabilitation of individual heart failure patients guided by the heart and lung movement in Liuyangling, Sun Xingsheng, Gaohua, et al, China journal of applied physiology 2015,31(4):374-377), however, the method for measuring heart rate, respiration and other single indexes and establishing a regression equation to predict the measured values of maximum oxygen uptake, anaerobic threshold and the like is still essentially that the linear calculation of each statically separated index is simply equal to the quantitative evaluation of the whole body, and is still a mechanical reduction theory;
in addition, reminding, encouraging and the like are required to be adopted in the actual operation process of the CPET, and a non-language communication mode is included, so that the patient (a testee) is expected to be ensured to reach the exercise limit (see, for example, the standardized operation requirement and the difficulty _ data analysis graphic representation and interpretation principle of the heart-lung exercise test in Sun Xingguan [ J ]. China application physiology journal 2015,31(4):361 and 364. and the like). the measurement mode has a large number of subjective factors, and the most common method for interpreting the maximum oxygen uptake is to artificially observe whether the ' sudden ' increase ' occurs on printing paper or a display (see, the standardized operation requirement and the difficulty-data analysis graphic representation and interpretation principle of the heart-lung exercise test in Sun Xingguan [ J ]. application physiology journal 2015,31(4):361 and 364.); the oxygen ventilation quantity regularly rises, the oxygen difference between inhaled air and exhaled air begins to drop carbon dioxide and does not drop, and the like are taken as anaerobic threshold criteria (see: field. anaerobic threshold test and evaluation [ M ]. advanced course of motion physiology. Beijing; advanced education publishers 2003: 906), which are still qualitative judgment modes, accurate quantitative results cannot be really obtained actually, whether a tested person is exhausted or not cannot be determined, so that the human motion capability cannot be accurately measured finally, the functional state of the whole human body cannot be accurately judged by depending on a subsystem, and in addition, if a male officer and the like (see: if the male officer is male, Suzhongsheng, and still draws rain, et al, quantitative and graded research progress of motion load strength and motion fatigue degree [ J ]. China journal of rehabilitation medicine, 2013,28(2):188 and 192.) indicate that the current motion strength experimental research is messy in the literature review, the problem of lack of a uniform quantization standard;
in summary, the prior art has the problems that the overall function state of the human body cannot be measured systematically and accurately on the whole, the overall health and function conditions of the human body can be judged accurately, and a unified quantification standard is not available.
Disclosure of Invention
The invention provides a human behavior activity degree measuring method and system based on human behavior information entropy, and aims to solve the problems that the prior art cannot accurately and systematically measure the whole function state of a human body on the whole, further accurately judge the whole conditions of human health and function, and lack of unified quantitative standards.
The technical problem solved by the invention is realized by adopting the following technical scheme: a human behavior activity degree measurement method based on human behavior information entropy comprises the following steps:
human behavior activity HBV method: and measuring the complexity measurement of the macroscopic behavior of the human body system by calculating the information entropy of the continuous dynamic data sequence of the human body behavior, and further measuring the activity degree of the human body microscopic system.
Further, the HBV method comprises:
in the human behavior process, a human behavior activity degree BVD is calculated by measuring specified continuous dynamic indexes through a human behavior activity degree function, wherein the BVD is the amount of the human behavior activity degree.
Further, the continuous dynamic index comprises continuous dynamic indexes such as heart rate, RR interval, heart rate variability, blood pressure, respiration and the like which are commonly monitored in sports training, and the continuous dynamic index is preferably a continuous dynamic heart rate, RR interval or HRV index of heart rate variability.
Further, the human behavior activity degree function comprises:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
Further, the entropy PVE of the physiological activity information needs to pass through continuous tests and calculate an average value, and the continuous test time is preferably more than two days.
Further, the method for preferentially determining the physiological activity information entropy PVE comprises the following steps:
the method comprises the following steps: a specific behavior number sequence, such as a heart rate data sequence during deep sleep;
{HRi-HR (2), HR (3) in sequence of numbers form a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is the probability that two sequences match m points with a similarity tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function;
the physiological activity information function includes:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
Further, the behavior real-time information entropy determination method RTE is as follows:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or other human behaviors;
the method of steps two to five corresponds to the corresponding steps two to five described for the PVE.
Further, a preferred value of m is 1 or 2, and a preferred range of r is r ═ 0.15std to 0.2std, where std is original data x (i), and i ═ 1, 2.
A human behavior activity degree measuring system based on human behavior information entropy comprises a human behavior activity HBV module;
the human behavior vitality HBV module is used for measuring the human system macroscopic behavior complexity measure by calculating the information entropy of the human behavior continuous dynamic data sequence, and further measuring the human microscopic system vitality degree.
Further, the human behavior vitality HBV module is used for measuring and specifying a continuous dynamic index through a human behavior vitality function to calculate the human behavior vitality BVD in the human behavior process, wherein the BVD is the amount of the human behavior vitality.
Further, the human behavior vitality HBV module further comprises a continuous dynamic index measurement submodule, such as a heart rate measurement submodule, an RR interval measurement submodule, a heart rate variability measurement submodule, a blood pressure measurement submodule, a respiration measurement submodule and the like, which is used for continuous monitoring commonly used in exercise training, wherein the human behavior vitality HBV module preferentially adopts the heart rate measurement submodule to obtain the heart rate continuous dynamic index.
Further, the human behavior activity degree function comprises:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
Further, the heart rate measuring submodule comprises a physiological activity information entropy determining module and a behavior real-time information entropy determining module.
Further, the physiological activity information entropy determination module is used for determining the physiological activity information entropy through continuous heart rate sample data during deep sleep;
the behavior real-time information entropy determining module is used for determining the real-time information entropy according to continuous heart rate sample data in the human body behavior period.
Further, the physiological activity information entropy determination module is configured to:
the method comprises the following steps: a specific behavior number sequence, such as a heart rate data sequence during deep sleep;
{HRi-HR (2), HR (3) in sequence of numbers form a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+ 1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+ 1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is the probability that two sequences match m points with a similarity tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function;
the physiological activity information function includes:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
Further, the behavior real-time information entropy determination module is configured to:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or human body behaviors;
the method of steps two to five corresponds to the corresponding steps two to five described for the PVE.
Further, a preferred value of m is 1 or 2, and a preferred range of r is r ═ 0.15std to 0.2std, where std is original data x (i), and i ═ 1, 2.
The invention has the beneficial effects that:
the patent adopts a human behavior activity HBV method: the method measures the complexity measure of the macroscopic behavior of a human body system by calculating the information entropy of a continuous dynamic data sequence of the human body behavior, and further measures the activity degree of the human body microscopic system, and is a new idea for measuring the human body motion intensity from the dimension of the information because at present, more people consider that life is not material and energy but information (see the documents of Leideyi, Douker 40546, Artificial Intelligence 50 years M. uncertain Artificial intelligence (version 2). Beijing; national defense industry Press: 47, and the like). The measurement information also has a definite parameter, namely entropy. In 1854, the concept of entropy was first proposed by the german physicist clausius (r.j. clausius) in the study of thermodynamics (see: seiko guang. complex system methodology and traditional chinese medical syndrome modeling [ M ]. beijing: scientific press, 2010.) to show the degree of uniformity of the distribution of any kind of energy in space. The more uniform the energy distribution, the greater the entropy. In 1877, boltzmann again gives a statistical definition of entropy, which is proportional to the logarithm of the thermodynamic probability, the higher the thermodynamic probability, the more chaotic the system is, and this is used to describe a measure of the degree of disorder of the system. In 1948, Claud E.Shannon (SHANNON) (SHAANNON C E.A. Mathematical Theory of communication [ J ]. The Bell System Technical Journal,1948,27(3): 379-423.) The definition of entropy was given by introducing entropy into The information field. The road is paved for the entropy entering the information science, the life science and other contemporary frontier science from the thermodynamics. Today, entropy is an important index for measuring system uncertainty, and can describe the human behavior complexity. Harken (see: h. harken. information & self-organization [ M ] sichuan; sichuan education press 2010:6-7.) proposed that the change in microstructure that produces macroscopic behavior processes can be presumed by processing complex systems through macroscopic observations. Therefore, the entropy value of the human body macroscopic quantity becomes an important parameter for measuring the activity of the microscopic elements, a new thought is provided for observing the state of the human body microstructure, and a theoretical basis is provided for measuring the self-organization capability of the human body microscopic system by using the change of the macroscopic behavior information entropy. Based on the above, the method provides a method for measuring the complexity of human body system macroscopic behaviors based on human body Behavior information entropy and further measuring the activity degree of the human body microscopic system, which is called as HBV (human Behavior Vigor) method, and the method is a new method for acquiring information such as deep time sequence, relationship, structure and the like by constructing a video type (as shown in figure 5) acquisition continuity overall data sequence, measuring the overall state of the system (as shown in figure 7), revealing the overall change rule hidden in the system, and establishing a method for stably, objectively and uniformly quantitatively measuring the human body function state, so that the method has great significance for scientific exercise training monitoring and evaluation.
Drawings
FIG. 1 is a general flowchart of the human behavior activity measurement method of the present invention;
FIG. 2 is a branched flow chart of the human behavior activity measurement method of the present invention;
FIG. 3 is a flowchart of a physiological activity information entropy determination method of the human behavior activity degree measurement method of the present invention;
FIG. 4 is a flow chart of the sampling data collection of the integrated regulation process of the oxygen metabolism process of the human body in the method for measuring human body behavior activity;
FIG. 5 is a flow chart of continuously and dynamically collecting data of the integrated regulation process of the oxygen metabolism process of the human body in the method for measuring human body behavior activity;
FIG. 6 is a linear graph of heart rate static transverse data of a human heart rate data sequence of the human behavioral activity measure method of the present invention;
fig. 7 is a linear graph of heart rate longitudinal dynamic continuous data of a human heart rate data sequence of the human behavior activity measurement method.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
in the figure:
s101, calculating the information entropy of a human behavior continuous dynamic data sequence;
s102, measuring the complexity measure of the macroscopic behavior of the human body system;
s103-further measuring the activity degree of the human microsystem;
s201, in the human behavior process, passing a human behavior activity degree function;
s202, measuring specified continuous dynamic indexes and calculating human behavior activity BVD;
s301, forming a group of m-dimensional vectors by the heart rate data sequence during the deep sleep according to the sequence number;
s302-sequencing the heart rate data sequence HR during deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence;
s303, setting the dimensionality of the maximum sequences d [ X (i) and X (j)) as m, and counting statistical sequences which are smaller than a set threshold value r in the maximum sequences d [ X (i) and X (j));
s304-maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
s305-determining physiological activity information entropy through a physiological activity information function;
example (b):
the first embodiment is as follows: as shown in fig. 1, a human behavior vitality measurement method based on human behavior information entropy includes:
human behavior activity HBV method: the information entropy S101 of the human body behavior continuous dynamic data sequence is calculated, the complexity measurement S102 of the human body system macroscopic behavior is measured, and then the activity degree S103 of the human body microscopic system is measured.
Due to the adoption of the human behavior activity HBV method: the method measures the complexity measure of the macroscopic behavior of a human body system by calculating the information entropy of a continuous dynamic data sequence of the human body behavior, and further measures the activity degree of the human body microscopic system, and is a new idea for measuring the human body motion intensity from the dimension of the information because at present, more people consider that life is not material and energy but information (see the documents of Leideyi, Douker 40546, Artificial Intelligence 50 years M. uncertain Artificial intelligence (version 2). Beijing; national defense industry Press: 47, and the like). The measurement information also has a definite parameter, namely entropy. In 1854, the concept of entropy was first proposed by the german physicist clausius (r.j. clausius) in the study of thermodynamics (see: seiko guang. complex system methodology and traditional chinese medical syndrome modeling [ M ]. beijing: scientific press, 2010.) to show the degree of uniformity of the distribution of any kind of energy in space. The more uniform the energy distribution, the greater the entropy. In 1877, boltzmann again gives a statistical definition of entropy, which is proportional to the logarithm of the thermodynamic probability, the higher the thermodynamic probability, the more chaotic the system is, and this is used to describe a measure of the degree of disorder of the system. In 1948, Claud E.Shannon (SHANNON) (SHAANNON C E.AMATHEMICAL THERORY of Communication [ J ]. The Bell System Technical Journal,1948,27(3): 379-423.) The definition of entropy was given. The road is paved for the entropy entering the information science, the life science and other contemporary frontier science from the thermodynamics. Today, entropy is an important index for measuring system uncertainty, and can describe the human behavior complexity. Harken (see: h. harken. information & self-organization [ M ] sichuan; sichuan education press 2010:6-7.) proposed that the change in microstructure that produces macroscopic behavior processes can be presumed by processing complex systems through macroscopic observations. Therefore, the entropy value of the human body macroscopic quantity becomes an important parameter for measuring the activity of the microscopic elements, a new thought is provided for observing the state of the human body microstructure, and a theoretical basis is provided for measuring the self-organization capability of the human body microscopic system by using the change of the macroscopic behavior information entropy. Based on the above, the method provides a method for measuring the complexity of human body system macroscopic behaviors based on human body Behavior information entropy and further measuring the activity degree of the human body microscopic system, which is called as HBV (human Behavior Vigor) method, and the method is a new method for acquiring information such as deep time sequence, relationship, structure and the like by constructing a video type (as shown in figure 5) acquisition continuity overall data sequence, measuring the overall state of the system (as shown in figure 7), revealing the overall change rule hidden in the system, and establishing a method for stably, objectively and uniformly quantitatively measuring the human body function state, so that the method has great significance for scientific exercise training monitoring and evaluation.
As shown in fig. 2, the HBV method comprises: in the human behavior process, a specified continuous dynamic index is measured through a human behavior activity degree function S201 to calculate a human behavior activity degree BVD S202, wherein the BVD is the amount of the human behavior activity degree;
the specified continuous dynamic indexes comprise continuously monitored continuous dynamic indexes such as heart rate, RR interval, heart rate variability, blood pressure, respiration and the like commonly used in sports training, and any continuous dynamic index is preferably a continuous dynamic heart rate, RR interval or HRV index of heart rate variability.
In the human behavior process, the BVD is calculated by measuring the specified continuous dynamic index through the human behavior activity function, and is the amount of the human behavior activity; the specified continuous dynamic indexes comprise continuously monitored continuous dynamic indexes such as heart rate, RR interval, heart rate variability, blood pressure, respiration and the like commonly used in sports training, any continuous dynamic index is preferably a continuous dynamic heart rate, RR interval or Heart Rate Variability (HRV) index of the heart rate, and theoretically, the overall state of the system (as shown in figure 5) such as the heart rate, HRV, blood pressure, respiration and the like can be evaluated by measuring any continuous dynamic index in a system information loop. Because the heart rate is a commonly used continuous monitoring means for sports training, the method adopts continuous dynamic heart rate information entropy in the human body sports training process as an example to establish a measuring system for evaluating the whole function state.
The human behavior activity degree function comprises:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
The human behavior activity degree function is adopted to comprise:
wherein: the BVD is the amount of human behavior activity; the RTE is a behavior real-time information entropy;the PVEs is physiological activity information entropy. The parameters of the human behavior activity degree function comprise: the human behavior activity is the ratio of the individual real-time information entropy to the physiological activity information entropy; the real-time information entropy is the information entropy of the current time period during the human behavior activity; the physiological activity information entropy is behavior information entropy of an individual during deep sleep as a specific reference value, since first, a reference value, namely a measurement zero point, is determined. Maddalena Costa believes that aging and disease follow the general "loss of complexity" theory (see: COSTA M, GOLDBERGER A L, PENG C-K. multiscale analysis of biological signals [ J)]Phys Rev E,2005,71(2):021906.), i.e., the entropy of the human body will decrease as the body ages or is in a diseased state. When the human body is in a frequent dying state, the heart rate information entropy of the human body infinitely approaches to a 0 value, so the 0 value is selected as a reference value, and then a measuring unit is determined. To quantitatively express the magnitude of a quantity, a specific quantity with a value of 1 needs to be chosen as the basis for comparison (Lidong liter. quantity and unit [ M ]]The metrology base, Beijing; 15-22.) the specific value is required to have objectivity, stability and reliability, less interference from other factors such as subjectivity and the like, and high repeatability. Because the deep sleep state of the human body is relatively easy to measure, is not interfered by subjective consciousness and is the activity expression of the physiological state of the human body, the state information entropy can be selected as a measuring unit, is called as physiological activity information entropy, and defines one of the following steps: physiological activity information Entropy (PVE) is the behavior information Entropy during the deep sleep of an individual as a specific reference value, i.e. a measurement unit. Defining this entropy value as 1, and agreeing on a unit symbol PV, and specifying 1PVE 1000mPV, defines two: the Real-Time Entropy of Behavior (RTE) is the Entropy of information for the current Time period during human behavioral activity. Defining three: the human Behavior activity Degree (BVD: Behavior Vigor Degreee) is the ratio of the individual real-time information entropy to the reference information entropy, namely the quantity representing the human Behavior activity Degree, and the expression (Lidong liter, quantity and unit M) of the quantity is PV]The metrology base, Beijing; mechanical industry Press 15-22.) is: BVD describes the relative course of change of individual body with the stimulation inside and outside the systemSince BVD is a relative value of individual's own variation, BVD of different individuals can be used as an objective amount of relative comparison.
The physiological activity information entropy PVE needs to pass continuous test and calculate an average value, and the continuous test time is preferably more than two days.
Because continuous tests are carried out for a period of time and the average value is calculated in the measurement of the physiological activity information entropy, in practical application, the average value is calculated in the continuous tests for a period of time in the measurement of the PVEs, so that errors caused by randomness are eliminated, and the value needs to be measured and updated in time.
As shown in fig. 3, the preferable determination method of the physiological activity information entropy PVE is as follows:
the method comprises the following steps: a specific behavior number sequence, such as a heart rate data sequence during deep sleep;
{HRi-HR (2), HR (3) in sequence of numbers form a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+ 1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+ 1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is two sequences in phaseProbability of matching m points under tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function;
the physiological activity information function includes:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
Because the physiological activity information entropy determination method is adopted, because the continuous dynamic heart rate is a common test parameter for exercise training at present, continuous heart rate sample entropy (RA, J R J.A review on sample entropy applications for the non-innovative analysis of physiological fibrous characteristics is adopted]Biological Signal Processing and Control,2010,5(1):1-14.) calculate behavioral activity. Let { HRiThe method comprises the following steps of (1), (2), (HR), (N) and (N) in sequence:
(1) will { HR }iAre formed by the sequence of numbersSet of m-dimensional vectors { HR }iH (N-m-1) }, where HR (1), HR (2)m(i) (ii) { HR (i), (i +1) }, HR (i + m-1) }, 1 ≦ i ≦ N-m +1 (1), which vectors represent m consecutive HR values starting from point i;
(2) setting d [ X (i), X (j)]Is HRm(i) Sequence and HRm(j) The maximum absolute value of the difference between the corresponding elements of the sequence is as follows:
d[HRm(i)，HRm(j)]＝maxk＝0，...，m-1(| HR (i + k) -HR (j + k) |), i, j ═ 1,.., N-m +1(2) (3) statistics d [ x (i), x (j)]The number of the particles smaller than the set threshold r is marked as Bij(1≤j≤N-m，j≠i)。
Defining:
(4) adding dimension to m +1 while (3) calculating HRm+1(i) The number of the distances between the HRm +1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and r is recorded as Aij(1≤j≤N-m，j≠i)。
Defining:
then it is determined that,
thus, Bm(r) is the probability that two sequences match m points with a similarity tolerance of r, and Am(r) is the probability that two sequences match m +1 points.
Then the PVEs are:
or;
PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
As shown in fig. 4, the method for determining the behavior real-time information entropy includes:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or other human behaviors;
the method of steps two to five corresponds to the corresponding steps two to five described for the PVE.
Due to the fact that the behavior real-time information entropy determination method is adopted, the behavior information entropy is the basis of human body vitality degree measurement. Sample entropy (see: RICHMAN J S, R.M. physical time-series analysis using entropy and sample entropy [ J ]. American Journal of physics-Heart and Circulatory physics, 2000,278(6):2039-2049.) is also an information entropy, a new event sequence complexity measure proposed by Richman, whose physical significance represents the rate at which a nonlinear dynamical system generates new information, used to measure the magnitude of the probability of generating a new pattern in a signal. The larger the entropy value, the more complex the sequence, the more complex the time sequence, and the greater the probability that a new pattern will be generated. The method has better application in a time sequence system, and people such as Zhuang Jianjun (Zhuang Jianjun, Ningxinbao, Zhonghua, et al., consistency of two entropy measurement values on the complexity of the quantitative shooter short-time heart rate variability signal [ J ]. Physics, 2008,05): 2805-plus 2811.) recently verify that the HRV data sample entropy of the shooter can well measure the change of the HRV data sample entropy in different states of shooting and resting, similarly, the values can be calculated according to the formulas (2) to (7), wherein the heart rate data sequence in the deep sleep period in the step (1) in the specific calculation step is replaced by the heart rate data sequence in other human body behaviors, and the other human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises, etc.
A preferred value of m is 1 or 2, and a preferred range of r is r 0.15std to 0.2std, where std is original data x (i), and i is a standard deviation of 1, 2.
Since the preferred value of M is 1 or 2, the preferred range of r is r 0.15 std-0.2 std, and std is original data x (i), i 1, 2, and N is the standard deviation, since 1 or 2 is the standard deviation of original data according to the research result of Pincus (see: M ps. assembling servicer and matters informatics for health [ J ]. ann.n.y.acad.sci,2002,954): 245-.
Meanwhile, the invention also provides a human behavior activity degree measuring system based on the human behavior information entropy, which comprises a human behavior activity HBV module;
the human behavior vitality HBV module is used for measuring the human microscopic system vitality degree S102 by measuring the human system macroscopic behavior complexity S101 based on the human behavior information entropy.
The human behavior vitality HBV module is used for measuring the complexity measurement S102 of the macroscopic behavior of the human system and further measuring the vitality degree S103 of the microscopic system of the human body by calculating the information entropy S101 of the continuous dynamic data sequence of the human behavior;
the human behavior vitality HBV module is used for calculating human behavior vitality BVD S202 by measuring S201 specified continuous dynamic indexes through a human behavior vitality function in the human behavior process, wherein the BVD is the amount of human behavior vitality.
The human behavior vitality HBV module also comprises a continuous dynamic index measuring submodule such as a heart rate measuring submodule, an RR interval measuring submodule, a heart rate variability measuring submodule, a blood pressure measuring submodule, a respiration measuring submodule and the like which are used for continuous monitoring commonly used for exercise training, wherein the human behavior vitality HBV module preferentially adopts the heart rate measuring submodule to obtain the heart rate continuous dynamic index.
The human behavior activity degree function comprises:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
The heart rate measuring submodule comprises a physiological activity information entropy determining module and a behavior real-time information entropy determining module.
The physiological activity information entropy determination module is used for determining physiological activity information entropy through continuous heart rate sample data in a deep sleep period;
the behavior real-time information entropy determining module is used for determining the real-time information entropy according to continuous heart rate sample data in the human body behavior period.
The physiological activity information entropy determination module is used for:
the method comprises the following steps: sequence of specific activity numbers, e.g. heart rate data during deep sleep
{HRi-HR (2), HR (3) in sequence of numbers form a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+ 1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding element by an absolute value determining function, anForm a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+ 1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is the probability that two sequences match m points with a similarity tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function;
the physiological activity information function includes:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, the approximation is 0.618, which is called the human physiology Φ entropy.
The behavior real-time information entropy determination module is configured to:
the behavior real-time information entropy determination module is configured to:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or human body behaviors;
the method of steps two to five corresponds to the corresponding steps two to five described for the PVE.
The real-time information function is:
a preferred value of m is 1 or 2, and a preferred range of r is r 0.15std to 0.2std, where std is original data x (i), and i is a standard deviation of 1, 2.
Meanwhile, the invention provides a human behavior vitality measuring system based on human behavior information entropy, the system provides a human behavior vitality HBV module, such as a heart rate measuring submodule, and a heart rate measuring submodule is preferentially adopted to obtain heart rate continuous dynamic indexes, wherein the heart rate measuring submodule comprises a physiological vitality information entropy determining module and a behavior real-time information entropy determining module.
The working principle is as follows:
the patent adopts the human behavior activity HBV method: the method measures the complexity measure of the macroscopic behavior of a human body system by calculating the information entropy of a continuous dynamic data sequence of the human body behavior, and further measures the activity degree of the human body microscopic system, and is a new idea for measuring the human body motion intensity from the dimension of the information because at present, more people consider that life is not material and energy but information (see the documents of Leideyi, Douker 40546, Artificial Intelligence 50 years M. uncertain Artificial intelligence (version 2). Beijing; national defense industry Press: 47, and the like). The measurement information also has a definite parameter, namely entropy. In 1854, the concept of entropy was first proposed by the german physicist clausius (r.j. clausius) in the study of thermodynamics (see: seiko guang. complex system methodology and traditional chinese medical syndrome modeling [ M ]. beijing: scientific press, 2010.) to show the degree of uniformity of the distribution of any kind of energy in space. The more uniform the energy distribution, the greater the entropy. In 1877, boltzmann again gives a statistical definition of entropy, which is proportional to the logarithm of the thermodynamic probability, the higher the thermodynamic probability, the more chaotic the system is, and this is used to describe a measure of the degree of disorder of the system. In 1948, Claud E.Shannon (SHANNON) (SHAANNON C E.A. Mathematical Theory of communication [ J ]. The Bell System Technical Journal,1948,27(3): 379-423.) The definition of entropy was given by introducing entropy into The information field. The road is paved for the entropy entering the information science, the life science and other contemporary frontier science from the thermodynamics. Today, entropy is an important index for measuring system uncertainty, and can describe the human behavior complexity. Harken (see: h. harken. information & self-organization [ M ] sichuan; sichuan education press 2010:6-7.) proposed that the change in microstructure that produces macroscopic behavior processes can be presumed by processing complex systems through macroscopic observations. Therefore, the entropy value of the human body macroscopic quantity becomes an important parameter for measuring the activity of the microscopic elements, a new thought is provided for observing the state of the human body microstructure, and a theoretical basis is provided for measuring the self-organization capability of the human body microscopic system by using the change of the macroscopic behavior information entropy. Based on the method, the method provides the method for measuring the complexity of the macroscopic behavior of the human body system based on the human body behavior information entropy, further measure the activity of human microsystem, called HBV (human Behavior Vigor) method, the invention solves the problem that the prior art can not accurately and systematically measure the whole function state of the human body on the whole, thereby accurately judging the overall condition of human health and function and lacking uniform quantitative standards, has great significance for human health state measurement, active health behavior intervention and scientific exercise training monitoring.
The technical solutions of the present invention or similar technical solutions designed by those skilled in the art based on the teachings of the technical solutions of the present invention are all within the scope of the present invention to achieve the above technical effects.
Claims (16)
1. A human behavior activity degree measurement method based on human behavior information entropy is characterized by comprising the following steps:
human behavior activity HBV method: and measuring the complexity measurement of the macroscopic behavior of the human body system by calculating the information entropy of the continuous dynamic data sequence of the human body behavior, and further measuring the activity degree of the human body microscopic system.
2. The human behavioral activity measurement method according to claim 1, wherein the HBV method comprises:
in the human behavior process, a human behavior activity degree BVD is calculated by measuring specified continuous dynamic indexes through a human behavior activity degree function, wherein the BVD is the amount of the human behavior activity degree.
3. The method for measuring human behavior activity according to claim 2, wherein the any continuous dynamic index comprises continuously monitored continuous dynamic indexes such as heart rate, RR interval, heart rate variability, blood pressure, respiration and the like commonly used in sports training, and the any continuous dynamic index is preferably a continuous dynamic heart rate, RR interval or HRV index of heart rate variability.
4. The human behavior vitality measurement method according to claim 3, wherein the human behavior vitality function includes:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
5. The human behavior vitality measurement method according to claim 4, wherein the entropy of physiological vitality information (PVE) is obtained by continuously measuring and calculating an average value, and the continuous measurement time is preferably more than two days.
6. The human behavior vitality measurement method according to claim 5, wherein the physiological vitality information entropy PVE optimal determination method comprises the following steps:
the method comprises the following steps: a specific behavior number sequence, such as a heart rate data sequence during deep sleep;
{HRi-HR (1), HR (2), HR (n) } in sequence number order, a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is the probability that two sequences match m points with a similarity tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function, wherein the physiological activity information function is any one of the following two functions:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
7. The human behavior vitality measurement method according to claim 6, wherein the behavior real-time information entropy determination method RTE is:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or other human behaviors;
the method of steps two through five remains the same as the corresponding steps two through five described for the PVE.
8. The method for measuring human behavior activity according to claims 6 and 7, wherein the preferred value of m is 1 or 2, the preferred range of r is r 0.15std to 0.2std, and std is the standard deviation of original data X (i), i is 1, 2.
9. A human behavior activity degree measuring system based on human behavior information entropy is characterized by comprising a human behavior activity HBV module;
the human behavior vitality HBV module is used for measuring the human system macroscopic behavior complexity measure by calculating the information entropy of the human behavior continuous dynamic data sequence, and further measuring the human microscopic system vitality degree.
10. The system according to claim 9, wherein the human behavior vitality measuring module is configured to measure a specific continuous dynamic indicator through a human behavior vitality function to calculate a human behavior vitality BVD during the human behavior process, and the BVD is a measure of the human behavior vitality.
11. The system according to claim 10, wherein the human behavior vitality measuring module further comprises a continuous dynamic index measuring submodule, such as a heart rate measuring submodule, an RR interval measuring submodule, a heart rate variability measuring submodule, a blood pressure measuring submodule, a respiration measuring submodule, and the like, for continuous monitoring commonly used in athletic training, wherein the human behavior vitality measuring module preferentially adopts the heart rate measuring submodule to obtain the heart rate continuous dynamic index.
12. The human behavior vitality measurement system of claim 11, wherein the human behavior vitality function comprises:
wherein:
the RTE is human body real-time behavior information entropy which is the information entropy of continuous dynamic data with a specific length intercepted in the current time period during human body behavior activities;
the PVEs is physiological activity information entropy which is human behavior specified length continuous dynamic data sequence information entropy used as a specific reference value during deep sleep of a human body;
the BVD is the amount of human behavior activity, and is the ratio of real-time human behavior information entropy RTE to physiological activity information entropy PVE;
the PV is a unit of measurement of the amount of human behavior activity, and 1PV is defined as 1000 mPV.
13. The human behavior vitality measurement system according to claim 12, wherein the heart rate measurement sub-module comprises a physiological vitality information entropy determination module and a behavior real-time information entropy determination module;
the physiological activity information entropy determination module is used for determining physiological activity information entropy through continuous heart rate sample data in a deep sleep period;
the behavior real-time information entropy determining module is used for determining the real-time information entropy according to continuous heart rate sample data in the human body behavior period.
14. The system for measuring human behavior vitality degree according to claim 13, wherein the physiological vitality information entropy determination module is configured to:
the method comprises the following steps: a specific behavior number sequence, such as a heart rate data sequence during deep sleep;
{HRi-HR (2), HR (3) in sequence of numbers form a set of m-dimensional vectors;
f1：HRm(i)＝{HR(i)，HR(i+1)，...，HR(i+m-1)}，1≤i≤N-m+1；
step two: sequencing the heart rate data sequence HR during the deep sleepm(i) And HRm(j) Determining a value with the maximum absolute value of the difference value of the corresponding elements through an absolute value determining function, and forming a maximum sequence d [ X (i), X (j)]The absolute value determination function is: d [ HR ]m(i)，HRm(j)]＝maxk＝0，...，m-1(|HR(i+k)-HR(j+k)|)，i，j＝1，...，N-m+1；
Step three: taking the dimension of the maximum sequence d [ X (i), X (j) ] as m, and counting the statistical sequences which are smaller than a set threshold value r in the maximum sequence d [ X (i), X (j) ];
f2：
f3：
b ism(r) is the probability that two sequences match m points with a similarity tolerance r;
step four: the maximum sequence d [ X (i), X (j)]Is set as m +1, and the heart rate data sequence HR during the deep sleep period after the self-increment is countedm+1(i) And HRm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) and the distance is less than or equal to r;
f4：
f5：
a is describedm(r) is the probability that two sequences match m +1 points with a similarity tolerance of r;
step five: determining physiological activity information entropy through a physiological activity information function, wherein the physiological activity information function is any one of the following functions:
the method comprises the following steps:
f6：PVE＝-ln|Am(r) |, which is called human physiological e entropy;
the second is as follows:
f7：PVE＝logΦ|Am(r) |, Φ ═ 0.618; wherein the content of the first and second substances,
preferably, Φ is approximated as 0.618, which is referred to as the human physiological Φ entropy.
15. The system for measuring activity degree of human behavior according to claim 14, wherein the behavior real-time information entropy determination module is configured to:
replacing the heart rate data sequence during the deep sleep period in the step one with a heart rate data sequence during human body behaviors, wherein the human body behaviors comprise: cycling, running, climbing, swimming, sitting still, meeting, qigong, doing exercises or human body behaviors;
the method of steps two through five remains the same as the corresponding steps two through five described for the PVE.
16. The human behavior activity degree measurement system according to claim 15, wherein the preferred value of m is 1 or 2, the preferred range of r is r-0.15 std-0.2 std, and std is the standard deviation of the original data x (i), i-1, 2.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150201850A1 (en) * | 2014-01-21 | 2015-07-23 | Life Beam Technologies Ltd. | Method and system for determining life threatening condition via bodily worn sensors |
| CN104921713A (en) * | 2015-05-28 | 2015-09-23 | 中国科学院合肥物质科学研究院 | Dynamic monitoring system and warning method for myocardial blood oxygen supply and demand state based on heart rate and pulses |
| CN105205303A (en) * | 2015-06-29 | 2015-12-30 | 郭明 | 32 physical health holographic assessment cloud system |
| CN107610765A (en) * | 2017-08-08 | 2018-01-19 | 深圳智能表芯科技有限公司 | A kind of human activity is worth monitoring method and system |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150201850A1 (en) * | 2014-01-21 | 2015-07-23 | Life Beam Technologies Ltd. | Method and system for determining life threatening condition via bodily worn sensors |
| CN104921713A (en) * | 2015-05-28 | 2015-09-23 | 中国科学院合肥物质科学研究院 | Dynamic monitoring system and warning method for myocardial blood oxygen supply and demand state based on heart rate and pulses |
| CN105205303A (en) * | 2015-06-29 | 2015-12-30 | 郭明 | 32 physical health holographic assessment cloud system |
| CN107610765A (en) * | 2017-08-08 | 2018-01-19 | 深圳智能表芯科技有限公司 | A kind of human activity is worth monitoring method and system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113488169A (en) * | 2021-07-01 | 2021-10-08 | 新绎健康科技有限公司 | Human health assessment method and device based on energy metabolism |
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