CN117174246A - Individualized respiratory movement mode reconstruction training device and method thereof - Google Patents

Abstract

The application discloses an individualized respiratory movement pattern reconstruction training device, which comprises: the device comprises a physiological parameter receiving module, a guiding signal module and a respiration fitness calculating module; the physiological parameter receiving module is configured to receive a respiratory signal of a subject; the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal; the respiratory fitness calculating module is configured to calculate the fitness between the respiratory signal received by the physiological parameter receiving module and the guiding signal when the user performs respiratory motion under the guiding of the guiding signal. The individualized respiratory movement mode reconstruction training device can objectively compare the coincidence degree of actual respiration and guided respiration, and provide objective evaluation indexes for training.

Description

Individualized respiratory movement mode reconstruction training device and method thereof

Technical Field

The application relates to a respiratory reconstruction technology, in particular to an individualized respiratory pattern reconstruction training device and a method thereof.

Background

According to statistics of death causes of ten main diseases in the first ten cities of the whole country in 2006, respiratory diseases (excluding lung cancer) account for the fourth (13.1%) in death causes of cities and the third (16.4%) in rural areas. In recent years, respiratory diseases, especially chronic respiratory diseases represented by chronic obstructive pulmonary diseases (chronic bronchitis, emphysema, pulmonary heart disease, etc.) and asthma, are significantly increased in prevalence and mortality due to the influence of factors such as physical and chemical factors, biological factors, and aging population caused by industrial economic development, atmospheric pollution, smoking, etc., wherein the prevalence of chronic obstructive pulmonary diseases in the population of 40 years and older reaches 13.6%, the total prevalence is nearly 1 million, and asthmatics exceed 3000 ten thousand. In addition, chronic respiratory diseases have long disease course and repeated attacks, and seriously affect the physical health and life quality of patients.

Chronic respiratory diseases often lead to abnormal breathing patterns, including chest breathing, abdominal breathing, shoulder breathing, shortness of breath, etc., with multiple abnormal breathing patterns. Therefore, reconstructing the normal respiratory movement pattern, especially the deep and slow abdominal respiration, is very important for the rehabilitation of patients with chronic respiratory diseases. If the respiration of the patient with the slow resistance lung is characterized by incompletely reversible airflow limitation, the respiratory action is usually aggravated, the respiratory action is mainly finished through chest respiration and even shoulder respiration, the respiration is short, the inhaled gas is mainly concentrated at the middle upper part of the lung, alveoli at the lower part of the lung are difficult to be filled, a large amount of fresh air cannot be inhaled, hypoxia, carbon dioxide retention and the like are caused, and the lung function is further damaged; by carrying out breathing mode reconstruction training on a patient with slow lung resistance, the patient is prompted to exercise abdominal respiration, the vital capacity is enlarged, the chest is expanded to the maximum extent, and the alveoli at the lower part of the lung are stretched, so that the lung function system of the patient can be effectively improved, the course of disease is shortened, and the life quality is improved.

At present, effective auxiliary tools and methods for reconstructing the respiratory motion mode are lacking for doctors and patients, and the patients often stay in a dictation or physical education mode, and no objective indexes for checking the training level and effect are available.

Disclosure of Invention

In view of the above problems, the present application aims to propose an individualized respiratory movement pattern reconstruction training device and a method thereof, which can give objective evaluation to respiratory movement training of a subject; the application also aims to provide an individualized respiratory movement pattern reconstruction training device and a method thereof, which are individualized to the training of a subject; the application also aims to provide an individualized respiratory movement pattern reconstruction training device and a method thereof, which can analyze the contribution ratio of each pattern of respiration in the respiratory movement of a subject in the whole respiratory movement and provide information feedback for reconstructing a healthy respiratory movement pattern.

The application relates to an individualized respiratory movement pattern reconstruction training device, which comprises: a physiological parameter receiving module, a guiding signal module, a respiratory anastomosis degree calculating module,

The physiological parameter receiving module is configured to receive a respiratory signal of a subject;

the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal;

the respiratory fitness calculating module is configured to calculate the fitness between the respiratory signal received by the physiological parameter receiving module and the guiding signal when the user performs respiratory motion under the guiding of the guiding signal.

Preferably, the method further comprises: a baseline data module;

the baseline data module is configured to obtain breath pattern baseline data according to the breath signals received by the physiological parameter receiving module during resting spontaneous breathing for a predetermined time period before the subject is subjected to breath pattern training;

the pilot signal module outputs a pilot signal based on the breathing pattern baseline data.

Preferably, the respiration fitness calculation module calculates at least one of tidal volume curve fitness, chest and abdomen respiration curve fitness and respiration rate fitness:

the tidal volume curve fitness is calculated as follows:

1) The breathing waveform is fitted using the following interpolation function:

in [ a, b ]]The n+1 points a.ltoreq.x are given above ₀ <x ₁ <…<x _n B, f (x) is [ a, b ]]The function of the above, the function s (x; a ₀ ,...,a _n ) Fitting is as follows: s (x) _i ；a ₀ ,...,a _n )＝f(x _i ),i＝0,1,…,n；

Wherein s (x) is f (x) is represented by [ a, b]Interpolation function above, if s (x) is related to parameter a ₀ ,a ₁ ,...,a _n Is a linear relationship, namely:

s(x)＝a ₀ s ₀ (x)+a ₁ s ₁ (x)+…+a _n s _n (x)

s (x) is a polynomial interpolation function;

2) Tidal volume curve waveforms are calculated by chest and abdomen respiratory waves: vt=k×rc+m×ab;

3) Setting a curve tolerance range: range= + -0.05 (maxS-minS);

wherein: range is the tolerance Range;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

4) Setting a window as 5s, and calculating the number N of each sampling point of the tidal volume waveform in the 5s within a tolerance range;

5) Calculating the anastomosis degree: g=n×100/a

Wherein: g is the fitness, N is the number of sampling points within a tolerance range of 5s, and A is the number of all sampling points within 5 s;

wherein,

[ a, b ]: intercepting respiratory signals of a-b time periods on chest and abdomen respiratory curves;

f (x): a discrete function over a time period of the chest and abdomen respiration curve [ a, b ];

s (x): an interpolation function of f (x) over [ a, b ];

VT: tidal volume;

m, K: chest and abdomen respiratory signal fitting coefficients;

RC, AB: chest and abdomen respiration signals;

range: tolerance ranges;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

g: tidal volume compliance;

n: the number of sampling points within a tolerance range of 5 s;

a: the number of all sampling points in 5 s;

the calculation of the coincidence degree of the chest and abdomen breathing curve is as follows:

1) The respiratory rate and the chest-abdomen respiratory contribution ratio are set to be C, A respectively by fitting the related chest-abdomen respiratory waveforms;

2) The actual chest and abdomen respiration waveform is ch, ab;

3) Setting a chest-abdomen curve tolerance range: crange, arange

Crange＝±0.05(maxC-minC)

Arange＝±0.05(maxA-minA)

4) Setting a window as 5s, and calculating the coincidence degree of the chest and abdomen respiration waveforms in 5s, wherein the coincidence degree is GC and GA respectively

GC＝Nc*100/Ac；

GA＝Na*100/Aa；

Nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

Ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

5) Calculating the final fitness: g= (gc+ga)/2;

wherein,

C. a: fitting chest and abdomen respiration waveforms;

ch. ab: actual chest and abdomen respiration waveforms;

crange, arange: chest and abdomen curve tolerance ranges;

maxC, minC: the set crest value and trough value of the chest respiratory wave signal;

maxA, minA: the set peak value and trough value of the abdominal respiration signal;

GC. GA: chest and abdomen respiration waveform coincidence degree;

nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

g: respiratory signal fitness;

the respiration rate fitness is calculated as follows:

the fitness is calculated by g=a×100/N, wherein,

g is the anastomosis degree;

a is the respiration number with the respiratory rate difference less than 3;

n is the actual breath number.

Preferably, the guidance signal module generates the guidance signal based on a set training time period, inspiration time, expiration time, breath-hold time.

Preferably, the training period, inspiration time, expiration time, breath-hold time are set by an operator.

Preferably, the training period, the inspiration time, the expiration time and the breath-hold time are set according to the breath pattern baseline data obtained by the baseline data module.

Preferably, the method further comprises: a breathing pattern recognition module;

the respiratory pattern recognition module is configured to calculate a chest-abdomen respiratory contribution ratio of the subject in the respiratory exercise training process according to the respiratory signals of the chest and the abdomen of the subject received by the physiological parameter receiving module.

Preferably, the guiding signal module outputs a corresponding guiding signal according to the chest-abdomen respiration contribution ratio so as to adjust the chest-abdomen respiration contribution ratio.

The application relates to an individualized respiratory movement pattern reconstruction training method, which comprises the following steps:

receiving, by a physiological parameter receiving module, a respiratory signal of a subject;

the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal;

calculating the coincidence degree between the respiratory signal received by the physiological parameter receiving module and the guiding signal when a user performs respiratory motion under the guiding of the guiding signal by the respiratory coincidence degree calculating module, wherein the coincidence degree comprises tidal volume curve coincidence degree, chest-abdomen respiratory curve coincidence degree and respiratory rate coincidence degree;

the tidal volume curve fitness is calculated as follows:

1) The breathing waveform is fitted using the following interpolation function:

in [ a, b ]]The n+1 points a.ltoreq.x are given above ₀ <x ₁ <…<x _n B, f (x) is [ a, b ]]The function of the above, the function s (x; a ₀ ,...,a _n ) Fitting is as follows: s (x) _i ；a ₀ ,...,a _n )＝f(x _i ),i＝0,1,…,n；

Wherein s (x) is f (x) is represented by [ a, b]Interpolation function above, if s (x) is related to parameter a ₀ ,a ₁ ,...,a _n Is a linear relationship, namely:

s(x)＝a ₀ s ₀ (x)+a ₁ s ₁ (x)+…+a _n s _n (x)

s (x) is a polynomial interpolation function;

2) Tidal volume curve waveforms are calculated by chest and abdomen respiratory waves: vt=k×rc+m×ab;

3) Setting a curve tolerance range: range= + -0.05 (maxS-minS);

wherein: range is the tolerance Range;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

4) Setting a window as 5s, and calculating the number N of each sampling point of the tidal volume waveform in the 5s within a tolerance range;

5) Calculating the anastomosis degree: g=n×100/a

Wherein: g is the fitness, N is the number of sampling points within a tolerance range of 5s, and A is the number of all sampling points within 5 s;

wherein,

[ a, b ]: intercepting respiratory signals of a-b time periods on chest and abdomen respiratory curves;

f (x): a discrete function over a time period of the chest and abdomen respiration curve [ a, b ];

s (x): an interpolation function of f (x) over [ a, b ];

VT: tidal volume;

m, K: chest and abdomen respiratory signal fitting coefficients;

RC, AB: chest and abdomen respiration signals;

range: tolerance ranges;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

G: tidal volume compliance;

n: the number of sampling points within a tolerance range of 5 s;

a: the number of all sampling points in 5 s;

the calculation of the coincidence degree of the chest and abdomen breathing curve is as follows:

1) The respiratory rate and the chest-abdomen respiratory contribution ratio are set to be C, A respectively by fitting the related chest-abdomen respiratory waveforms;

2) The actual chest and abdomen respiration waveform is ch, ab;

3) Setting a chest-abdomen curve tolerance range: crange, arange

Crange＝±0.05(maxC-minC)

Arange＝±0.05(maxA-minA)

4) Setting a window as 5s, and calculating the coincidence degree of the chest and abdomen respiration waveforms in 5s, wherein the coincidence degree is GC and GA respectively

GC＝Nc*100/Ac；

GA＝Na*100/Aa；

Nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

5) Calculating the final fitness: g= (gc+ga)/2;

wherein,

C. a: fitting chest and abdomen respiration waveforms;

ch. ab: actual chest and abdomen respiration waveforms;

crange, arange: chest and abdomen curve tolerance ranges;

maxC, minC: the set crest value and trough value of the chest respiratory wave signal;

maxA, minA: the set peak value and trough value of the abdominal respiration signal;

GC. GA: chest and abdomen respiration waveform coincidence degree;

nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

G: respiratory signal fitness;

the respiration rate fitness is calculated as follows:

the fitness is calculated by g=a×100/N, wherein,

g is the anastomosis degree;

a is the respiration number with the respiratory rate difference less than 3;

n is the actual breath number.

Preferably, the baseline data of the breathing pattern is obtained by configuring the baseline data module according to the breathing signals received by the physiological parameter receiving module during resting spontaneous breathing for a predetermined period of time before the subject is subjected to the breathing pattern training;

the guiding signal module generates guiding signals based on the set training time, inspiration time, expiration time and breath-hold time;

the respiratory pattern recognition module is configured to calculate the chest-abdomen respiratory contribution ratio of the subject in the respiratory exercise training process according to the respiratory signals of the chest and the abdomen of the subject received by the physiological parameter receiving module.

The individualized respiratory movement mode reconstruction training device and the method thereof can objectively compare the coincidence degree of actual respiration and guided respiration and provide objective evaluation indexes for training; the application relates to an individualized respiratory movement mode reconstruction training device and a method thereof, wherein training parameters are set based on the condition of a subject; the individualized respiratory movement mode reconstruction training device and the method thereof can quantitatively analyze the total proportion of the breathing of each mode in the whole breath and provide feedback information for reconstructing healthy breath.

Drawings

FIG. 1 is a schematic diagram of a breathing pattern training presentation;

FIG. 2 is a graph of actual respiratory signal versus expert set respiratory signal following;

FIG. 3 is a graph of a chest-abdomen respiration waveform and a chest-abdomen respiration contribution ratio;

FIG. 4 is a graph showing the trend of heart rate and blood oxygen saturation;

FIG. 5 is a breathing pattern evaluation interface;

FIG. 6 is a schematic diagram of a respiratory pattern reconstruction training interface in expert mode;

FIG. 7 is a schematic diagram of a respiratory mode reconstruction training interface in a normal mode;

FIG. 8 is a schematic diagram of an expert mode reconstruction training report;

FIG. 9 is a schematic diagram of a normal mode reconstruction training report;

FIG. 10 is a wearable device for physiological parameter measurement in respiratory pattern training;

FIG. 11 is a block diagram of the construction of an individualized respiratory movement pattern reconstruction training device of the present application;

FIGS. 12-14 are tidal volume following profiles;

fig. 15-17 are schematic views of respiratory curve anastomosis;

fig. 18-20 are schematic views of respiratory rate anastomosis.

Detailed Description

The individualized respiratory movement pattern reconstruction training device and the method thereof according to the present application will be described in detail with reference to the accompanying drawings.

The individualized respiratory movement pattern reconstruction training device is realized by a computer or a tablet computer, and the computer or the computer comprises: the device comprises a physiological parameter receiving module, a guiding signal module, a respiration fitness calculating module, a baseline data module and a respiration mode identifying module. In other words, the corresponding program is executed, so that the computer or the tablet personal computer correspondingly realizes the functions of the physiological parameter receiving module, the guiding signal module, the respiratory fitness calculating module, the baseline data module and the respiratory pattern identifying module.

The physiological parameter receiving module is configured to receive a respiratory signal of a subject; the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal; the respiratory fitness calculating module is configured to calculate the fitness between the respiratory signal received by the physiological parameter receiving module and the guiding signal when the user performs respiratory motion under the guiding of the guiding signal. The baseline data module is configured to obtain breath pattern baseline data according to the breath signals received by the physiological parameter receiving module during resting spontaneous breathing for a predetermined time period before the subject is subjected to breath pattern training; the pilot signal module outputs a pilot signal based on the breathing pattern baseline data. The respiratory pattern recognition module is configured to calculate a chest-abdomen respiratory contribution ratio of the subject in the respiratory exercise training process according to the respiratory signals of the chest and the abdomen of the subject received by the physiological parameter receiving module.

The wearable physiological parameter monitoring terminal can monitor physiological parameters such as chest and abdomen respiratory motion, heart rate, blood oxygen and the like; the wearable physiological parameter monitoring terminal data are transmitted to the PDA or the large screen in a wireless mode, and the chest and abdomen breathing motion curve, the breathing mode related information and the cardiovascular system parameter change information are displayed on the large screen in real time. The large screen has a breathing motion guiding curve for guiding the subject to perform breathing motion training.

The wearable physiological parameter monitoring terminal is provided with a respiratory induction plethysmograph sensor arranged on the chest and abdomen to obtain a real respiratory motion curve of the chest and abdomen, and the tidal volume is obtained through calibration; the electrocardiographic and blood oxygen saturation is measured by means of an electrocardiographic and blood oxygen wristwatch.

Before respiratory reconstruction training, resting spontaneous respiration is carried out for 1-2 minutes, respiratory mode baseline data of a subject is obtained, including respiratory rate, inspiration and expiration time, chest and abdomen respiratory contribution ratio and the like, respiratory mode evaluation is carried out through the respiratory mode baseline data, and auxiliary decision support information is provided for guiding a patient to carry out respiratory mode reconstruction training by a doctor.

After the breath pattern is evaluated, the doctor sets the individualized breath reconstruction training mode of the patient, including an expert mode and a common mode. The method comprises the steps of setting target respiratory rate, inspiration time, expiration time and the like, then generating a guide curve or graph on a PDA or a large screen based on the set target respiratory mode, and interactively guiding to realize the reconstruction training of the respiratory mode of the subject.

Expert mode

(1) After the breath pattern is evaluated, a doctor sets target breath parameters including training time, inspiration time, expiration time, breath-hold time, respiration rate, respiration ratio and other parameters, and deeply quantifies the breath pattern;

(2) An interactive respiratory guidance interface that enables guidance in visual and/or audible terms;

the presentation mode is as follows: comprises a trapezoid and a sphere, and is switched according to personal preference. The trapezoid presenting mode is as follows: the trapezoid side length is correspondingly changed according to the relative value of the set parameters, and the moving point moves along the trapezoid track when the breathing training is carried out; the ball presenting mode is as follows: according to the parameter setting, the initial value of the sphere is determined according to the value of the setting parameter (inspiration time/expiration time) and is changed accordingly; during inspiration, the radius of the sphere is gradually increased, the sphere is expanded from the circle 2 to the circle 1, during expiration, the sphere is contracted from the circle 1 to the circle 2, and during breath-hold, the sphere size is unchanged. As shown in fig. 1

Respiratory pattern reconstruction training guidance method: respiration training is performed according to the voice prompt.

Trapezoid presentation mode: the moving point is at the starting point of the trapezoid, and when the voice prompt is "inhaling", the moving point moves along the track, so that the patient does inhaling action; when the moving point moves to the position 1, the patient stops inhaling, and the voice prompts the breath-hold, so as to perform the breath-hold action; when the moving point moves to the position 2, the voice prompts 'expiration', and the patient starts to perform expiration action; when the moving point moves to the position 3, the patient stops breathing, the breath-hold is prompted by voice, after the breath-hold action is finished, a breathing cycle is completed, the moving point returns to the starting point, and the action is repeated to perform breathing training;

Ball presentation mode: taking the initial value of the sphere size as an example, and taking expiration time as an expiration time, when a voice prompt is "inspiration", a patient performs inspiration movement, the radius of the sphere is gradually enlarged, and the sphere is changed from a circle 2 to a circle 1; the voice prompt is 'breath-hold', and the radius of the sphere is unchanged and unchanged; after the breath-hold action is finished, the voice prompt is used for 'breathing out', the radius of the sphere is gradually reduced, the direction from the circle 1 to the circle 2 is changed, then the voice prompt is used for 'breath-hold', the radius of the sphere is unchanged and unchanged, and a breathing cycle is completed.

Provides an index of the coincidence degree,

the tidal volume curve fitness and chest and abdomen respiration curve fitness and respiration rate fitness are included;

tidal volume curve fitting degree

1) The breathing waveform is fitted using the following interpolation function:

in [ a, b ]]The n+1 points a.ltoreq.x are given above ₀ <x ₁ <…<x _n B, f (x) is [ a, b ]]The function of the above, the function s (x; a ₀ ,...,a _n ) Fitting is as follows: s (x) _i ；a ₀ ,...,a _n )＝f(x _i ) I=0, 1, …, n wherein s (x) is f (x) is represented by [ a, b ]]If s (x) is related to parameter a ₀ ,a ₁ ,...,a _n Is a linear relationship, namely:

s(x)＝a ₀ s ₀ (x)+a ₁ s ₁ (x)+…+a _n s _n (x)

s (x) is a polynomial interpolation function.

2) Tidal volume curve waveforms are calculated by chest and abdomen respiratory waves: vt=k×rc+m×ab

3) Setting a curve tolerance range: range= + -0.05 (maxS-minS)

Wherein: range is the tolerance Range;

maxS, setting the peak value of respiratory wave

minus, setting the trough value of respiratory wave

4) Setting a window as 5s, and calculating the number N of each sampling point of the tidal volume waveform in the 5s within a tolerance range;

5) Calculating the anastomosis degree: g=n×100/a

Wherein: g is the coincidence degree, N is the number of sampling points in the tolerance range of 5s, and A is the number of all sampling points in 5 s.

Related parameters:

a. b: intercepting respiratory signals of a-b time periods on chest and abdomen respiratory curves;

f (x): a discrete function over a time period of the chest and abdomen respiration curve [ a, b ];

s (x): an interpolation function of f (x) over [ a, b ];

VT: tidal volume;

m, K: chest and abdomen respiratory signal fitting coefficients;

RC, AB: chest and abdomen respiration signals;

range: tolerance ranges;

maxS, setting the peak value of respiratory wave

minus, setting the trough value of respiratory wave

G: tidal volume compliance;

n: the number of sampling points within a tolerance range of 5 s;

a: and 5s of all sampling points.

Chest and abdomen respiration curve anastomosis degree

1) The respiratory rate and the chest-abdomen respiratory contribution ratio are set to be C, A respectively by fitting the related chest-abdomen respiratory waveforms;

2) The actual chest and abdomen respiration waveform is ch, ab;

3) Setting a chest-abdomen curve tolerance range: crange, arange

Crange＝±0.05(maxC-minC)

Arange＝±0.05(maxA-minA)

4) Setting a window as 5s, and calculating the coincidence degree of the chest and abdomen respiration waveforms in 5s, wherein the coincidence degree is GC and GA respectively

GC＝Nc*100/Ac

GA＝Na*100/Aa

Nc, na: chest respiratory signal and abdomen respiratory signal sampling point number within 5s tolerance range

Ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal

5) Calculating the final fitness: g= (gc+ga)/2

Related parameters:

C. a: fitting chest and abdomen respiration waveforms;

ch. ab: actual chest and abdomen respiration waveforms;

crange, arange: chest and abdomen curve tolerance ranges;

maxC, minC: the set crest value and trough value of the chest respiratory wave signal;

maxA, minA: the set peak value and trough value of the abdominal respiration signal;

GC. GA: chest and abdomen respiration waveform coincidence degree;

nc, na: chest respiratory signal and abdomen respiratory signal sampling point number within 5s tolerance range

Ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal

G: respiration signal matching degree

Respiration rate matching degree

The fitness is calculated by g=a×100/N, wherein,

g is the anastomosis degree;

a is the respiration number with the respiratory rate difference less than 3;

n is the actual breath number.

And synchronously displaying a breathing waveform chart and a waveform coincidence degree parameter set by an actual breathing and expert on a PDA or a screen, and quantitatively displaying the following degree of the breathing of the patient in real time. The following degree of the actual respiratory signal and the respiratory signal set by the expert is shown in fig. 2. Wherein the actual breathing curve is obtained by fitting a chest-abdomen breathing curve.

(6) Based on the actually measured tidal volume, the tidal volume calibration is carried out by adopting a least square method according to a formula (2), the chest and abdomen respiration signal fitting coefficient is obtained, the chest and abdomen respiration contribution ratio is calculated according to the chest and abdomen respiration signal fitting coefficient, the doctor further judges the participation degree of the abdomen respiration motion of the subject according to the chest and abdomen respiration contribution ratio, the subject is further guided to strengthen the participation degree of the abdomen respiration based on the index, and the transition from shoulder respiration and chest respiration to abdomen respiration of the subject is gradually guided, so that a better training effect is achieved.

VT＝K*RC+M*AB (2)

r＝K*RC/(M*AB) (3)

VT: tidal volume

K: chest respiratory signal fitting coefficient

M: abdomen respiratory signal fitting coefficient

RC: chest respiration signal

AB: abdominal respiration signal

r: ratio of respiratory contribution to chest and abdomen

(7) The curve of the chest and abdomen respiratory motion and the chest and abdomen respiratory contribution ratio are drawn on the PDA or the screen in real time, and can be intuitively fed back to doctors and subjects, so that the doctors and the subjects can observe the following situation and the training effect conveniently, as shown in fig. 3.

(8) The heart rate and blood oxygen saturation change trend is displayed in real time, and doctors and patients can intuitively see the heart and lung physiological parameter change in the respiratory reconstruction process, so that the confidence is further enhanced, as shown in fig. 4.

(9) Based on heart rate variation, HRV components and RSA components are calculated and also displayed to doctors and patients, further enhancing their confidence.

Normal mode

(1) In the normal mode, the patient can set the breathing parameters for breathing mode reconstruction training.

Breathing parameter setting: the method comprises the steps of inspiration time, expiration time, training duration, respiratory rate, inhalation-exhalation ratio and the like, and can meet the requirement of a nurse on simple setting of parameters;

(2) interactive breathing guidance: the method comprises the steps of presenting interactive breathing guidance, voice guidance and character demonstration guidance in real time, wherein the three guidance methods are matched with each other, so that a patient is guided more clearly and accurately in visual and/or auditory aspects;

(3) the presentation mode is as follows: the same as the expert mode;

(4) and displaying parameters such as the breathing fitness data, the actual breathing rate, the effective training time length, the actual breathing ratio and the like set by the expert on the basis of real time. After training is finished, scoring the training effect according to the fitness data, and mobilizing training enthusiasm of the patient to obtain higher scores.

(5) In the normal mode, the system can adaptively adjust and guide the breathing mode according to the actual breathing training result of the patient, as shown in table 1.

Table 1: adaptively adjusting breathing patterns

Through the self-adaptation adjustment breathing mode, can be according to patient's actual breathing condition, select suitable breathing training mode for the patient voluntarily, make up the breathing mode that the patient set up by oneself probably is unsuitable for its not enough of breathing training.

The wearable physiological parameter monitoring terminal comprises a vest, wherein the chest and the abdomen are respectively provided with a breathing belt, and the breathing belt surrounds the chest and the abdomen for one circle and is used for collecting breathing signals; the blood oxygen wristwatch is worn on the wrist and is used for monitoring blood oxygen saturation; the data collector is connected with the vest, and is internally provided with an electrocardiosignal for monitoring electrocardiosignals and storing respiratory signals, electrocardiosignals and blood oxygen saturation signals, as shown in figure 10

1. Tidal volume calibration

And (3) taking the tidal volume acquired by the flowmeter as a gold standard, and carrying out tidal volume calibration on the acquired chest and abdomen respiration signals by adopting a least square method to obtain chest and abdomen fitting coefficients, and acquiring chest and abdomen tidal volume based on the chest and abdomen fitting coefficients so as to obtain the chest and abdomen respiration contribution ratio.

(1) Chest and abdomen tidal volume amplitude: and calculating the chest and abdomen tidal volume according to the fitting coefficient through the formula (2) and the formula (3).

VT _RC ＝K×RC (2)

VT _AB ＝M×AB (3)

VT _RC : chest tidal volume amplitude

VT _AB : abdominal tidal volume amplitude

(2) Chest-to-abdomen respiratory contribution ratio: and calculating the chest-abdomen respiration contribution ratio through a formula (4).

r＝VT _RC /VT _AB (4)

r: ratio of respiratory contribution to chest and abdomen

2. Baseline determination

Before respiratory reconstruction training, resting spontaneous respiration is carried out for 1-2 minutes, a respiratory mode test is carried out, respiratory mode baseline data of a subject is obtained, including heart rate, respiratory rate, inspiration and expiration time, chest and abdomen respiratory contribution ratio and the like, respiratory mode evaluation is carried out through the respiratory mode baseline data, and auxiliary decision support information is provided for guiding a patient to carry out respiratory mode reconstruction training by doctors. The breathing pattern evaluation interface is shown in fig. 5.

3. Individualised interactive guidance

After the breath pattern evaluation is finished, entering an individual interactive guiding interface to guide a patient to carry out breath pattern reconstruction training. The individualized interactive guidance comprises an expert mode and a common mode, wherein in the expert mode, a doctor or a nurse sets breathing parameters to guide a patient to carry out breathing mode reconstruction training; in the normal mode, the patient can set breathing parameters according to the self condition, and the system can automatically select a proper breathing training mode for the patient according to the baseline data.

Expert mode

The patient is guided in expert mode to a breathing pattern reconstruction training interface as shown in fig. 6.

(1) Setting target breathing parameters including training time, inspiration time, expiration time, breath-hold time and the like by doctors, and deeply quantifying a breathing mode;

(2) an interactive respiratory guidance interface that enables guidance in visual and/or audible terms;

(3) the presentation mode is as follows: comprises a trapezoid and a sphere, and is switched according to personal preference.

(4) The expert mode is used for guiding the patient to carry out breathing mode reconstruction training in a trapezoid presentation mode.

The specific guiding method comprises the following steps: the moving point is at the starting point of the trapezoid, and when the voice prompt is "inhaling", the moving point moves along the track, so that the patient does inhaling action; when the moving point moves to the position 1, the patient stops inhaling, and the voice prompts the breath-hold, so as to perform the breath-hold action; when the moving point moves to the position 2, the voice prompts 'expiration', and the patient starts to perform expiration action; when the moving point moves to the position 3, the patient stops breathing, the breath-hold is prompted by voice, after the breath-hold action is finished, a breathing cycle is completed, the moving point returns to the starting point, and the action is repeated to perform breathing training;

(5) And synchronously displaying the actual breath, a breath waveform chart and a waveform coincidence degree parameter set by an expert on a breath mode reconstruction training interface under the expert mode, and quantitatively displaying the following degree of the patient's breath in real time. The expert sets various parameters for respiratory training: for example, the inspiration time is 5.0s, the breath-hold time is 1.0s, the expiration time is 5.0s, and the breath-hold time is 0.5s, so that 1 respiratory cycle is completed. And the patient performs respiratory training according to the set parameters and evaluates the fitness. The tidal volume following is shown in figures 12-14. The breathing curve fit is shown in figures 15-17. The respiration rate compliance is shown in figures 18-20.

(6) The respiratory mode reconstruction training interface under the expert mode draws a chest-abdomen respiratory motion curve and a chest-abdomen respiratory contribution ratio in real time, can be intuitively fed back to doctors and subjects, is convenient for the doctors and the subjects to observe following conditions and training effects, and guides and timely corrects patients to carry out respiratory training.

(7) The heart rate and blood oxygen saturation change trend is displayed in real time, and doctors and patients can intuitively see the heart and lung physiological parameter change in the respiratory reconstruction process, so that the confidence is further enhanced.

Normal mode

The breathing pattern reconstruction training interface in normal mode is shown in fig. 7.

(1) In normal mode, the patient may set breathing parameters for breathing pattern reconstruction training by himself or by a nurse.

Breathing parameter setting: the method comprises the steps of inspiration time, expiration time, training duration, respiratory rate, inhalation-exhalation ratio and the like, and can meet the requirement of a nurse on simple setting of parameters;

(2) interactive breathing guidance: the method comprises the steps of presenting interactive breathing guidance, voice guidance and character demonstration guidance in real time, wherein the three guidance methods are matched with each other, so that a patient is guided more clearly and accurately in visual and/or auditory aspects;

(3) the presentation mode is as follows: comprises a trapezoid and a sphere, and is switched according to personal preference.

(4) The expert mode is used for guiding the patient to carry out breathing mode reconstruction training in a sphere presentation mode, and the consistency of the breathing signal waveforms is not considered in the sphere presentation mode.

The specific guiding method comprises the following steps: and carrying out breath training according to the voice prompt or the breath training guide diagram of the character in the interactive interface. When the voice prompts 'inhaling', the character in the interface performs inhaling action, and displays the character of 'inhaling and inhaling' and an arrow facing to nostrils, and simultaneously, the abdomen of the character slowly bulges, the character of 'inhaling abdomen bulge' is prompted, and the radius of the sphere gradually becomes larger; when the voice prompts the breath-hold, people in the interface do the breath-hold action, the character of the breath-hold is displayed, the arrow disappears, the abdomen is not changed, the character of the breath-hold abdomen is not moved, and the ball size is not changed; when the voice prompts 'exhaling', the person in the interface performs exhaling action, an arrow opposite to the direction of the inhaling arrow is displayed, meanwhile, the abdomen of the person slowly contracts, the word of 'exhaling abdomen contracts' is prompted, and the radius of the sphere is gradually reduced; the voice prompts the person to perform breath-hold action in the interface, the arrows at the nostrils disappear, the breath-hold character is displayed, the abdomen is not changed, the size of the ball is not changed, and one breathing cycle is completed. When the voice prompts the inspiration again, the character in the interface performs the inspiration action as above, the same prompting word is displayed, and the ball changes the same as the ball changes in the inspiration process, so that the training of the next breathing cycle is performed.

(5) Under the ordinary mode, the system can also adaptively adjust and guide the breathing mode according to the actual breathing training result of the patient, and can automatically select a proper breathing training mode for the patient according to the actual breathing condition of the patient through the self-adaptive adjustment of the breathing mode, so that the defect that the breathing mode set by the patient is possibly not suitable for the breathing training of the patient is overcome.

Example 1

1. Expert mode performs breathing mode training: stretch XX, female, height: 159cm, body weight: 66kg, age: copd patient wears physiological parameter monitoring terminals according to fig. 10 and then sets respiratory training parameters at the PDA interface, including: inspiration time, expiration time, breath-hold time, training duration, etc., and after setting the parameters, the wearable device is turned on, and the PDA interface display is shown in fig. 5. Under the prompting of voice (hearing) and an interactive guiding schematic diagram (vision), carrying out breathing mode reconstruction training according to set breathing training parameters, displaying a breathing waveform diagram, a chest type breathing waveform, an abdomen type breathing waveform, a contribution ratio, a heart rate and blood oxygen trend diagram, which are set by an expert, in real time, and observing the coincidence degree of the actual breathing of a subject and the breathing waveform, the chest-abdomen breathing contribution ratio parameters, the heart rate value and the blood oxygen value, knowing the breathing training condition of the subject, and after the training is finished, reporting and describing the breathing training condition. The report includes a summary of training of blood oxygen saturation and a trend graph of blood oxygen, heart rate, respiration rate, chest respiration, abdominal respiration, and abdominal respiration contribution ratio, as shown in fig. 8. Through respiratory training, the abdominal respiratory contribution ratio is gradually increased, so that the heart and lung respiratory function of a patient can be improved.

Example 2

Normal mode: the subject was Cao Mou, and respiratory mode reconstruction training was performed in normal mode, wearing a physiological parameter monitoring terminal according to fig. 10, and entering the PDA interface as shown in fig. 7. Setting respiratory training parameters such as inhalation time, exhalation time, training duration and the like, after the wearable device is started, carrying out respiratory mode reconstruction training under the guidance of voice (hearing) and character demonstration dynamic images (vision), and when the voice prompts 'inhalation', the character in the interface performs inhalation action, and displays a character of 'inhalation' and an arrow facing to nostrils, and simultaneously, the abdomen of the character slowly bulges to prompt the character of 'inhalation abdomen bulge', and the radius of the sphere gradually becomes larger; when the voice prompts the breath-hold, people in the interface do the breath-hold action, the character of the breath-hold is displayed, the arrow disappears, the abdomen is not changed, the character of the breath-hold abdomen is not moved, and the ball size is not changed; when the voice prompts 'exhaling', the person in the interface performs exhaling action, an arrow opposite to the direction of the inhaling arrow is displayed, meanwhile, the abdomen of the person slowly contracts, the word of 'exhaling abdomen contracts' is prompted, and the radius of the sphere is gradually reduced; the voice prompts the person to perform breath-hold action in the interface, the arrows at the nostrils disappear, the breath-hold character is displayed, the abdomen is not changed, the size of the ball is not changed, and one breathing cycle is completed. When the voice prompts the inspiration again, the character in the interface performs the inspiration action as above, the same prompting word is displayed, and the ball changes the same as the ball changes in the inspiration process, so that the training of the next breathing cycle is performed. After the breath training is completed, a report is sent, and the report pattern is shown in fig. 9.

The invention has the advantages that:

1. respiratory pattern reconstruction training includes expert patterns and normal patterns. In the expert mode, the patient can carry out interactive breathing training under the guidance of the voice guidance and the presentation mode, and meanwhile, a doctor can set breathing guidance parameters in real time along with the training condition of the patient, and can check the change condition of other physiological parameters of the patient in real time; in the common mode, the patient performs interactive respiration training according to the voice prompt and presentation mode, and the patient can perform training according to the expert prescription and also can perform basic respiration training under the guidance of nurses;

2. the guidance of the respiratory training of the patient in visual and auditory terms can be achieved both in expert mode and in normal mode. In the expert mode, the presentation mode of the interactive guidance comprises the following steps: the method comprises the steps that a patient can switch according to personal preference, in the mode, a moving point moves along the track of the trapezoid or the sphere according to voice prompts, and the presenting mode of the trapezoid or the sphere is to display different shapes and sizes according to the relative values of set parameters, so that the synchronization of voice and the presenting mode is realized; in the normal mode, the presenting mode is a trapezoid, the moving point moves along the track of the trapezoid according to the voice prompt, the trapezoid shape displays different sizes according to the relative value of the setting parameters, and the synchronization of the voice and the presenting mode is realized;

3. In the expert mode, the actual breath and the expert set breathing waveform are displayed in real time, and the following degree of the patient breath is quantized in real time by comparing the actual breath with the expert set breathing waveform and the displayed waveform coincidence degree data; the setting parameters can be adjusted in real time, so that the patient can perform breathing training under proper parameter setting;

4. in the expert mode, the chest/abdomen respiration waveform and chest-abdomen respiration contribution ratio data are displayed in real time, the respiration mode is subjected to data quantification, and the respiration mode of a patient is determined more intuitively;

5. in the normal mode, the display mode is trapezoid, the moving point moves along the trapezoid track according to the voice prompt, meanwhile, the actual motion curve of the respiration of the patient is displayed in real time, the actual respiration is compared with the expert set respiration waveform in real time, the following degree of the patient can be more intuitively represented, the patient is guided to adjust the respiration, and the patient is stimulated to exercise; simultaneously displaying the fitness data in real time, grading the training effect of the patient according to the fitness data, and mobilizing the enthusiasm of the patient for training to obtain higher score;

6. in the normal mode, a character demonstration drawing is set, and the character demonstration drawing is synchronous with the voice prompt. Under the voice prompt, the character demonstration drawing performs inspiration or expiration actions, prompts the abdomen to be in a bulge or contraction state, more clearly and accurately guides the patient from both visual and/or auditory aspects, and stimulates the interest of the patient in respiratory training;

7. In the normal mode, the character actions, voice prompts and dynamic guidance (trapezia and spheres) in the interactive guidance mode can be perfectly connected according to the set algorithm.

8. Under the ordinary mode, the system is provided with a self-adaptive breathing mode, and the breathing training mode can be automatically adjusted to be suitable for the patient according to the actual breathing result of the patient, so that the defect that the breathing parameters set manually are possibly unsuitable for the patient to carry out breathing training is overcome.

Unless defined otherwise, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application relates. The materials, methods, and examples mentioned herein are illustrative only and not intended to be limiting.

Although the present application has been described in connection with specific embodiments thereof, those skilled in the art will appreciate that various substitutions, modifications and changes may be made without departing from the spirit of the application.

Claims (10)

1. An individualized respiratory movement pattern reconstruction training device, comprising: the device comprises a physiological parameter receiving module, a guiding signal module and a respiration fitness calculating module;

The physiological parameter receiving module is configured to receive a respiratory signal of a subject;

the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal;

the respiratory fitness calculating module is configured to calculate the fitness between the respiratory signal received by the physiological parameter receiving module and the guiding signal when the user performs respiratory motion under the guiding of the guiding signal.

2. The personalized respiratory movement pattern reconstruction training device of claim 1, further comprising: a baseline data module;

the baseline data module is configured to obtain breath pattern baseline data according to the breath signals received by the physiological parameter receiving module during resting spontaneous breathing for a predetermined time period before the subject is subjected to breath pattern training;

the pilot signal module outputs a pilot signal based on the breathing pattern baseline data.

3. The personalized respiratory movement pattern reconstruction training device of claim 2, wherein:

the respiration fitness calculation module calculates at least one of tidal volume curve fitness, chest and abdomen respiration curve fitness and respiration rate fitness:

the tidal volume curve fitness is calculated as follows:

1) The breathing waveform is fitted using the following interpolation function:

in [ a, b ]]The n+1 points a.ltoreq.x are given above ₀ <x ₁ <…<x _n B, f (x) is [ a, b ]]The function of the above, the function s (x; a ₀ ,...,a _n ) Fitting is as follows: s (x) _i ；a ₀ ,...,a _n )＝f(x _i ),i＝0,1,…,n；

Wherein s (x) is f (x) is represented by [ a, b]Interpolation function above, if s (x) is related to parameter a ₀ ,a ₁ ,...,a _n Is a linear relationship, namely:

s(x)＝a ₀ s ₀ (x)+a ₁ s ₁ (x)+…+a _n s _n (x)

s (x) is a polynomial interpolation function;

2) Tidal volume curve waveforms are calculated by chest and abdomen respiratory waves: vt=k×rc+m×ab;

3) Setting a curve tolerance range: range= + -0.05 (maxS-minS);

wherein: range is the tolerance Range;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

4) Setting a window as 5s, and calculating the number N of each sampling point of the tidal volume waveform in the 5s within a tolerance range;

5) Calculating the anastomosis degree: g=n×100/a

Wherein: g is the fitness, N is the number of sampling points within a tolerance range of 5s, and A is the number of all sampling points within 5 s;

wherein,

[ a, b ]: intercepting respiratory signals of a-b time periods on chest and abdomen respiratory curves;

f (x): a discrete function over a time period of the chest and abdomen respiration curve [ a, b ];

s (x): an interpolation function of f (x) over [ a, b ];

VT: tidal volume;

m, K: chest and abdomen respiratory signal fitting coefficients;

RC, AB: chest and abdomen respiration signals;

range: tolerance ranges;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

g: tidal volume compliance;

n: the number of sampling points within a tolerance range of 5 s;

a: the number of all sampling points in 5 s;

the calculation of the coincidence degree of the chest and abdomen breathing curve is as follows:

1) The respiratory rate and the chest-abdomen respiratory contribution ratio are set to be C, A respectively by fitting the related chest-abdomen respiratory waveforms;

2) The actual chest and abdomen respiration waveform is ch, ab;

3) Setting a chest-abdomen curve tolerance range: crange, arange

Crange＝±0.05(maxC-minC)

Arange＝±0.05(maxA-minA)

4) Setting a window as 5s, and calculating the coincidence degree of the chest and abdomen respiration waveforms in 5s, wherein the coincidence degree is GC and GA respectively

GC＝Nc*100/Ac；

GA＝Na*100/Aa；

Nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

5) Calculating the final fitness: g= (gc+ga)/2;

wherein,

C. a: fitting chest and abdomen respiration waveforms;

ch. ab: actual chest and abdomen respiration waveforms;

crange, arange: chest and abdomen curve tolerance ranges;

maxC, minC: the set crest value and trough value of the chest respiratory wave signal;

maxA, minA: the set peak value and trough value of the abdominal respiration signal;

GC. GA: chest and abdomen respiration waveform coincidence degree;

nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

Ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

g: respiratory signal fitness;

the respiration rate fitness is calculated as follows:

the fitness is calculated by g=a×100/N, wherein,

g is the anastomosis degree;

a is the respiration number with the respiratory rate difference less than 3;

n is the actual breath number.

4. The personalized respiratory movement pattern reconstruction training device of claim 1, wherein:

the guidance signal module generates a guidance signal based on the set training time period, inspiration time, expiration time, breath-hold time.

5. The personalized respiratory movement pattern reconstruction training apparatus of claim 4, wherein:

the training period, inspiration time, expiration time, breath-hold time are set by the operator.

6. The personalized respiratory movement pattern reconstruction training apparatus of claim 4, wherein:

the training time, the inspiration time, the expiration time and the breath-hold time are set according to the breath pattern baseline data obtained by the baseline data module.

7. The personalized respiratory movement pattern reconstruction training device of claim 1, further comprising: a breathing pattern recognition module;

The respiratory pattern recognition module is configured to calculate a chest-abdomen respiratory contribution ratio of the subject in the respiratory exercise training process according to the respiratory signals of the chest and the abdomen of the subject received by the physiological parameter receiving module.

8. The personalized respiratory movement pattern reconstruction training apparatus of claim 5 or 7, wherein:

the guiding signal module outputs corresponding guiding signals according to parameters set by an operator.

9. A personalized respiratory motion pattern reconstruction training method, comprising:

receiving, by a physiological parameter receiving module, a respiratory signal of a subject;

the guidance signal module is configured to output a guidance signal to the subject such that the subject performs a respiratory motion in accordance with the guidance signal;

calculating the coincidence degree between the respiratory signal received by the physiological parameter receiving module and the guiding signal when a user performs respiratory motion under the guiding of the guiding signal by the respiratory coincidence degree calculating module, wherein the coincidence degree comprises at least one of tidal volume curve coincidence degree, chest and abdomen respiratory curve coincidence degree and respiratory rate coincidence degree;

the tidal volume curve fitness is calculated as follows:

1) The breathing waveform is fitted using the following interpolation function:

In [ a, b ]]The n+1 points a.ltoreq.x are given above ₀ <x ₁ <…<x _n B, f (x) is [ a, b ]]The function of the above, the function s (x; a ₀ ,...,a _n ) Fitting is as follows: s (x) _i ；a ₀ ,...,a _n )＝f(x _i ),i＝0,1,…,n；

Wherein s (x) is f (x) is represented by [ a, b]Interpolation function above, if s (x) is related to parameter a ₀ ,a ₁ ,...,a _n Is a linear relationship, namely:

s(x)＝a ₀ s ₀ (x)+a ₁ s ₁ (x)+…+a _n s _n (x)

s (x) is a polynomial interpolation function;

2) Tidal volume curve waveforms are calculated by chest and abdomen respiratory waves: vt=k×rc+m×ab;

3) Setting a curve tolerance range: range= + -0.05 (maxS-minS);

wherein: range is the tolerance Range;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

4) Setting a window as 5s, and calculating the number N of each sampling point of the tidal volume waveform in the 5s within a tolerance range;

5) Calculating the anastomosis degree: g=n×100/a

Wherein: g is the fitness, N is the number of sampling points within a tolerance range of 5s, and A is the number of all sampling points within 5 s;

wherein,

[ a, b ]: intercepting respiratory signals of a-b time periods on chest and abdomen respiratory curves;

f (x): a discrete function over a time period of the chest and abdomen respiration curve [ a, b ];

s (x): an interpolation function of f (x) over [ a, b ];

VT: tidal volume;

m, K: chest and abdomen respiratory signal fitting coefficients;

RC, AB: chest and abdomen respiration signals;

range: tolerance ranges;

maxS, setting the peak value of the respiratory wave;

minus, setting the trough value of the respiratory wave;

G: tidal volume compliance;

n: the number of sampling points within a tolerance range of 5 s;

a: the number of all sampling points in 5 s;

the calculation of the coincidence degree of the chest and abdomen breathing curve is as follows:

1) The respiratory rate and the chest-abdomen respiratory contribution ratio are set to be C, A respectively by fitting the related chest-abdomen respiratory waveforms;

2) The actual chest and abdomen respiration waveform is ch, ab;

3) Setting a chest-abdomen curve tolerance range: crange, arange

Crange＝±0.05(maxC-minC)

Arange＝±0.05(maxA-minA)

4) Setting a window as 5s, and calculating the coincidence degree of the chest and abdomen respiration waveforms in 5s, wherein the coincidence degree is GC and GA respectively

GC＝Nc*100/Ac；

GA＝Na*100/Aa；

Nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

5) Calculating the final fitness: g= (gc+ga)/2;

wherein,

C. a: fitting chest and abdomen respiration waveforms;

ch. ab: actual chest and abdomen respiration waveforms;

crange, arange: chest and abdomen curve tolerance ranges;

maxC, minC: the set crest value and trough value of the chest respiratory wave signal;

maxA, minA: the set peak value and trough value of the abdominal respiration signal;

GC. GA: chest and abdomen respiration waveform coincidence degree;

nc, na: the number of sampling points of chest respiratory signals and abdomen respiratory signals within the tolerance range of 5 s;

ac. Aa:5s of the number of all sampling points of the internal chest respiratory signal and the abdominal respiratory signal;

G: respiratory signal fitness;

the respiration rate fitness is calculated as follows:

the fitness is calculated by g=a×100/N, wherein,

g is the anastomosis degree;

a is the respiration number with the respiratory rate difference less than 3;

n is the actual breath number.

10. The personalized respiratory movement pattern reconstruction training method according to claim 9, further comprising:

obtaining breath pattern baseline data by configuring, by the baseline data module, the breath signals received by the physiological parameter receiving module at resting spontaneous breathing for a predetermined period of time prior to the subject performing the breath pattern training;

the guiding signal module generates guiding signals based on the set training time, inspiration time, expiration time and breath-hold time;

the respiratory pattern recognition module is configured to calculate the chest-abdomen respiratory contribution ratio of the subject in the respiratory exercise training process according to the respiratory signals of the chest and the abdomen of the subject received by the physiological parameter receiving module.