Method and system for breathing training and testing - Patents.com

Abstract

The present disclosure relates to a computer-implemented method for calculating a respiratory training intensity in a data set representing a subject's breaths through separate inhalation and exhalation airways with predefined breathing and exhalation resistances, the method comprising calculating respiratory muscle force versus time for at least one breath of the subject and / or calculating respiratory muscle work for at least one breath of the subject. In one embodiment, the calculation of the respiratory training intensity is a real-time process performed using a continuously received stream of data.

Description

[Technical field]

[0001] The present disclosure relates to a system and computer-implemented method for breathing training and testing. The system is configured to measure a subject's breathing pressure in a mouthpiece, transmit the pressure data to a remote processing device, and continuously process the pressure data for calculation of breathing training intensity and respiratory muscle performance. [Background technology]

[0002] Respiratory training can be defined as a step of performing specific exercises aimed at improving the function of respiratory muscles, i.e., the muscles involved in active inspiration and / or active expiration. Respiratory training encompasses both inhalation and exhalation training and has been shown to have a wide range of beneficial effects. For example, inhalation training has been shown to improve respiratory muscle function in terms of strength, power, endurance, and efficiency, and has been proven to be important for reducing dyspnea during exertion.

[0003] Respiratory exercisers are known in the art and typically comprise a mouthpiece for inhalation and exhalation and a breathing passage with an opening that ensures that the subject experiences a pressure load during breathing, thereby providing a respiratory muscle training stimulus. The opening may be adjustable so that the pressure load can be set according to the subject's breathing strength and the type of training benefit desired.

[0004] In US Pat. No. 4,221,381, an adjustment structure adjusts the size of at least one of the openings therein to control the size of the access opening to the atmosphere. The air resistance is the same for both inhalation and exhalation.

[0005] In U.S. Pat. No. 4,739,987, the inhalation and exhalation resistances are adjusted by the use of openings in each of two partitions that cover the openings in and out of alignment.

[0006] DE 10 2019 001926 discloses a system for measuring and analysing human respiration based on electromyographic (EMG) signals obtained from the user's respiratory musculature during exercise.

[0007] US 2009 / 229611 discloses a system and method for inspiratory muscle strength training in patients receiving artificial respiratory support.

[0008] These prior art devices and systems are associated with several disadvantages and problems that may be overcome by the presently disclosed method and system. In particular, the known devices are limited with respect to the feedback provided to the user, with respect to the control of resistance, with respect to the mechanical implementation of the device, and with respect to the measurement of important exercise-related variables such as pressure. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] U.S. Pat. No. 4,221,381 [Patent Document 2] U.S. Pat. No. 4,739,987 [Patent Document 3] US Patent Application Publication No. 2009 / 229611 Summary of the Invention [Means for solving the problem]

[0010] The inventors have identified that the pressure load, i.e. the training stimulus magnitude produced by a given opening, is seemingly proportional to the rate of airflow through the opening, and therefore, at a given flow rate, a smaller surface area produces a larger pressure load. Thereby, the same pressure load can be produced by openings with different surface areas by varying the rate of airflow. This is a limitation of prior art breathing training units, since changes to airflow rate result in changes in pressure / training load.

[0011] In the face of the shortcomings of the prior art breathing training units, the inventors have realized how the breathing training unit can be configured to obtain more accurate breathing training information and how to provide the subject with said training information, thereby enabling the subject to optimize training stimulation instantaneously and / or during a training session.Furthermore, unlike the parameters for assessing prior art breathing training, such as flow rate and pressure load, as described above, the inventors have also realized that basing the determination of breathing training stimulation on respiratory muscle strength and / or respiratory muscle work rate is much better, since both of these parameters are multidimensional and provide the most accurate quantification of training stimulation.

[0012] The present disclosure therefore provides in a first aspect a computer implemented method for calculation of a breathing exercise intensity in a dataset representing a subject breathing through separate inhalation and exhalation airways having a predefined breathing resistance, the method comprising: calculating respiratory muscle force versus time for at least one breath of the subject; and / or calculating respiratory muscle work for at least one breath of the subject; The present invention relates to a computer-implemented method,

[0013] In a preferred embodiment of the present disclosure, the calculation of the breathing training intensity for an individual subject is a real-time process that is performed using a continuously received stream of data from a pressure sensor or the like located in the mouthpiece of the breathing training unit. The method can be applied to calculate the inspiratory muscle training intensity and / or the expiratory muscle training intensity depending on the desired type of training.

[0014] Real-time calculation of the continuously received data allows for sustained and instantaneous optimization of the training stimuli and thereby the physiological effect of the training session. The data may be a data set including pressure data versus time obtained in association with at least one of the inhalation and exhalation airways. The data set may have been obtained, for example, by a pressure sensor for measuring air pressure in the mouthpiece.

[0015] The training stimulus can be considered as the result of overloading the respiratory muscles in terms of training duration, intensity, and / or frequency, as well as the specificity of the training, e.g., a strength training stimulus or an endurance training stimulus.

[0016] In a preferred embodiment, the disclosed method is configured to calculate the peak respiratory muscle force output of the respiratory muscles. It has been identified that the pressure load that elicits the peak respiratory muscle force output of the respiratory muscles induces the greatest and widest range of adaptation (the most versatile stimulus). It should be noted that each individual has a unique peak respiratory muscle force output, e.g., maximum inspiratory force output and / or expiratory force output, defined by the strength of its respiratory muscles and its ability to generate airflow, specifically, its maximum respiratory pressure (inhalation and exhalation) and maximum respiratory flow rate (inhalation and exhalation), respectively.

[0017] In a further aspect, the present disclosure relates to a breath sensing system arranged / suitable for connection to a breathing training or testing device, typically having an inhalation airway and an exhalation airway. a. An electronic sensor unit, i. at least one pressure sensor for measuring air pressure in the inspiratory and / or expiratory airways of the breathing training and testing device; ii. a processing unit for transmitting the pressure data; and an electronic sensor unit comprising: b. A computer program having instructions that, when executed by a remote processing device, such as a smartphone, cause the processing device to: i. continuously acquiring said pressure data via a receiver on a remote processing device; ii. performing a method for the calculation of breathing exercise intensity in a data set representative of a subject's breathing, as disclosed elsewhere herein; A computer program for causing the Equipped with.

[0018] The breath sensing system is preferably arranged for retrofit / installation on an existing breath training and / or testing device, i.e., a device that typically has an inspiratory airway and an expiratory airway, and the disclosed electronic sensor can be arranged such that inspiratory and / or expiratory pressure data can be obtained for analysis according to the approaches described herein. For example, the breath sensing system can enable breath pressure sensing, calculation of breath training intensity in a data set representative of the subject's breath, and calculation of respiratory muscle force versus time for at least one breath of the subject and / or respiratory muscle work for at least one breath of the subject.

[0019] The electronic sensor unit may be adapted to be positioned, for example, in the inspiratory and / or expiratory airways of the breathing training and testing device. However, in a specific embodiment, the breath sensing system may comprise a tubular connector arranged to be connected to the inspiratory and / or expiratory airways of the breathing training and testing device. The tubular connector is preferably arranged to contain the electronic sensor unit such that the sensor unit measures pressure within the connector portion. The tubular connector is preferably configured to connect to an end of the inspiratory and / or expiratory path. The tubular connector may further comprise a mouthpiece.

[0020] In yet a further aspect, the present disclosure provides a breathing training and testing system for exercising and analyzing the breathing of a subject, the system comprising: A breathing unit comprising: A mouthpiece, at least one inhalation airway having adjustable inhalation airflow resistance; at least one exhalation airway having an adjustable exhalation airflow resistance; A mouthpiece connected to the An electronic sensor unit, at least one pressure sensor for measuring air pressure in the mouthpiece; a processing unit for transmitting the pressure data; an electronic sensor unit comprising: A breathing unit comprising: A computer program comprising instructions that, when executed by a remote processing device, such as a smartphone, cause the processing device to: acquiring said pressure data via a receiver on a remote processing device, preferably continuously acquiring said pressure data; performing a method for the calculation of breathing exercise intensity in a data set representative of a subject's breathing as disclosed elsewhere herein; A computer program that causes The present invention relates to a breathing training and testing system comprising:

[0021] The breathing unit together with the mouthpiece and electronic sensor unit is preferably portable, as also shown in Figures 4-6, thereby functioning as a personal breathing training and testing device that can be used in practice during active training.

[0022] In one embodiment, the electronic sensor unit comprises a storage medium, i.e. in the form of a memory (RAM) or other similar digital storage medium, for at least temporarily storing the pressure data so that the pressure data can be stored on the breathable unit, for example in the event that there is no connection to a remote processing device for a period of time, e.g., 30 minutes or 1-5 hours, e.g., during a training session, when the smartphone is not necessarily carried by the user. When the connection to the remote processing device is re-established, the stored pressure data can be transmitted from the breathable unit to the remote processing device.

[0023] In a preferred embodiment of the present disclosure, the computer program has instructions that, when executed by the remote processing device, cause the processing device to display instantaneous and / or cumulative calculated force and / or work rate data on a screen of the remote processing device.By using the breathing training and testing system disclosed herein, a subject can obtain accurate information related to breathing training intensity in a data set.

[0024] One embodiment of a device suitable for use in addition to the disclosed methods and / or as part of the disclosed respiratory training and testing system is disclosed in "Respiratory device and system for exercising and analyzing respiration of a subject," pending application WO 2018 / 011358, filed July 13, 2017. WO 2018 / 011358 is incorporated herein by reference in its entirety. [Brief description of the drawings]

[0025] [Figure 1] FIG. 1 illustrates an example of a respiratory pressure-flow-force calculation according to an illustrative embodiment of the present disclosure.

[0026] [Diagram 2] FIG. 2 illustrates an example of a subject's maximum breathing pressure versus the subject's lung volume, according to a specific embodiment of the present disclosure.

[0027] [Diagram 3] FIG. 3 shows an overview of a breathing training and testing system according to an illustrative embodiment of the present disclosure.

[0028] [Figure 4] 4A-B show a breathing exercise and testing device having two individually adjustable airflow resistances according to a specific embodiment of the present disclosure.

[0029] [Diagram 5] 5A-D show different views of an embodiment of a breathing exercise and testing device according to some embodiments of the present disclosure.

[0030] [Figure 6] 6A-C show different exterior views of an embodiment of a breathing exercise and testing device according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] definition As used herein, the term "breath" refers to a portion of the breathing cycle consisting of or including inspiration and / or expiration. A "breath" as used herein may thereby consist of, for example, an inspiration, an expiration, an inspiration followed by an expiration, or an expiration followed by an inspiration, potentially combined with a breathing pause between inspiration and expiration.

[0032] As used herein, the term "respiratory muscle training" (RMT) refers to techniques that aim to improve the function of respiratory muscles through specific exercises. RMT is beneficial for a wide variety of subjects, including those suffering from respiratory conditions such as asthma, bronchitis, emphysema, and COPD. However, RMT can also be part of a subject's sports training, such as to improve the endurance and / or strength and / or power of respiratory muscles. The benefits of respiratory muscle training, along with methods and systems for the implementation of RMT, are described in more detail in McConnell, A., Respiratory Muscle Training: Theory and Practice. Elsevier, 2013.

[0033] As used herein, the term "remote processing device" should be broadly interpreted to include not only a physical device, but also, for example, a remote server, a cloud, or a web-based content management system. Note that this is a legitimate user's device, i.e., the server of an online service is not included. Preferably, the remote processing device is remote from the electronic sensor unit. However, the subject may hold the remote processing device, or alternatively, it may be located remotely from the subject, such as when a trainer or medical professional holds the remote processing device. Examples of remote processing devices include those described above, and also mobile devices such as smartphones, tablet computers, smart watches, smart glasses, or wearable devices.

[0034] As used herein, the term "pressure sensor" refers to a device for pressure measurement of a gas or liquid. The pressure sensor may be, for example, a gauge pressure sensor, a differential pressure sensor, a vacuum pressure sensor, or a sealed pressure sensor, or a combination thereof. It is noted that as used herein, a pressure sensor may include multiple sensing units, and the sensor or processing unit is configured to calculate the pressure based on pressure data from the multiple sensing units.

[0035] Detailed Description The present disclosure relates to methods and systems for obtaining accurate breathing training intensity information, i.e., inspiratory training intensity information and / or expiratory training intensity information.

[0036] In a first aspect, the present disclosure relates to a computer-implemented method for the calculation of a breathing training intensity, such as an inspiratory training intensity and / or an expiratory training intensity. The calculation is typically based on a data set representative of a subject breathing through a breathing training unit, the device preferably having separate inhalation and exhalation airways with a predefined breathing resistance.

[0037] In some embodiments of the present disclosure, the computer-implemented method includes calculating respiratory muscle force for at least a portion of a breath of a subject, such as at least one complete breath of the subject. Respiratory muscle force is typically calculated over multiple time points, whereby respiratory muscle force may be calculated over time, such as during at least one breath of the subject or during a training session. Respiratory muscle force is typically calculated based on instantaneous measurements, such as instantaneous flow and instantaneous pressure. In such cases, respiratory muscle force may represent an instantaneous value of force, however, respiratory muscle force may alternatively or additionally be based on average measurements, such as average flow and / or average pressure. In such cases, respiratory muscle force may represent an average pressure over a given time interval and / or number of breaths.

[0038] In some embodiments of the present disclosure, the computer-implemented method includes calculating respiratory muscle work for at least a portion of a single breath of the subject, more preferably for at least one breath of the subject. Respiratory muscle work may also be calculated over other lengths, such as over a predetermined amount of time, number of breaths, or over an entire training session or portion thereof.

[0039] In a preferred embodiment of the present disclosure, the calculation of the subject's breathing training intensity (power and / or work) is a real-time process that is performed using a stream of continuously received data. The data may be obtained, for example, by a pressure sensor located in the mouthpiece of the breathing training unit, which is configured to measure the inspiratory pressure and / or expiratory pressure in the airway of the breathing training unit. The method is thereby applicable to the continuous calculation of the inspiratory muscle training intensity and / or expiratory muscle training intensity, for example, depending on the type of training benefit desired.

[0040] The disclosed method may, in one embodiment, be implemented by use of a breathing training system through which a subject may select whether training of inspiratory muscles, expiratory muscles, or both types of muscles is desired. In response to the selection, the system may obtain relevant pressure and / or flow data, i.e., related to inspiration and / or expiration, and perform calculations based on these values. In general, the obtained data represents the subject's breathing through the inspiration airway and / or expiration airway, preferably each of the airways having a predefined breathing resistance.

[0041] In an embodiment of the present disclosure, the data set includes pressure data versus time obtained in association with at least one of the inhalation airway and the exhalation airway. A pressure sensor may be located in the mouthpiece of the breathing training unit such that the pressure sensor is connected to at least one of the inhalation airway and the exhalation airway. In a further embodiment of the present disclosure, the breathing training system includes a plurality of pressure sensors configured to measure different pressure ranges. The breathing training system is typically configured to derive flow rate by pressure measurement. However, in an embodiment of the present disclosure, the breathing training system includes a pressure sensor and a flow rate sensor. Thus, in an embodiment of the present disclosure, the data set includes flow rate versus time obtained in association with at least one of the inhalation airway and the exhalation airway.

[0042] In some embodiments of the disclosed method, the method relates to calculating a respiratory training intensity. The respiratory training intensity can be determined quantitatively and / or qualitatively in several ways and characterized by several parameter values. For example, the respiratory training intensity can be described by respiratory muscle strength, such as average and / or peak respiratory muscle strength per breath, or by respiratory muscle work, such as respiratory muscle work per breath.

[0043] force In a further embodiment of the present disclosure, the computer-implemented method includes calculating an average respiratory muscle force for at least one breath, a predefined time interval, a training session, and / or a portion thereof. The computer-implemented method may be configured to calculate, for example, an average respiratory muscle force during inspiration, expiration, or a breath (inspiration and / or expiration).

[0044] In a specific embodiment of the present disclosure, the computer-implemented method includes calculating peak respiratory muscle strength for at least one breath, a predefined time interval, a training session, and / or a portion thereof. The computer-implemented method may be configured to calculate peak respiratory muscle strength during, for example, inspiration, expiration, or a breath (inspiration and / or expiration).

[0045] In some embodiments of the present disclosure, the computer-implemented method includes calculating an average peak respiratory muscle force of a plurality of breaths of the subject. The computer-implemented method may be configured to calculate the peak respiratory muscle force for each breath, for example. The computer-implemented method may further be configured to calculate an average based on a plurality of peak respiratory muscle force values ​​obtained from single breaths, for example, during a training session over a predetermined amount of time.

[0046] In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a respiratory rate of change of pressure versus time for at least one breath of the subject, and / or a maximum rate of change of pressure for at least one breath of the subject. The respiratory rate of change of pressure may be an inspiratory rate of change of pressure and / or an expiratory rate of change of pressure. The respiratory rate of change of pressure may provide information related to, for example, how quickly a muscle is able to provide a forcing force (pressure) during a loaded muscular contraction, i.e., this may be considered as a rate of pressure generation.

[0047] MRP The (maximal) pressure-generating capacity of the respiratory muscles depends on the flow rate. Line AB in Figure 1 shows an example of a linear regression between pressure load and capacity to generate airflow. Pressure and flow-generating capacity can be measured, for example, by asking a subject to perform a series of inspirations at maximum inspiratory effort for a range of fixed pressure loads or flow resistances.

[0048] Typically, a linearly decreasing flow rate (e.g., L s -1 A decrease in pressure (measured in cmH2O) is seen with increasing pressure load (e.g., measured in cmH2O). Mathematically and physiologically, the product of pressure and flow is force. Hence, respiratory muscle force of the inspiratory and / or expiratory muscles can be estimated as explained above. Peak respiratory muscle force (FIG. 1C) typically occurs at the midpoint of the pressure-flow relationship. Thus, the optimal flow (FIG. 1D) and pressure (FIG. 1E) for maximum force production are 50% of the values ​​at A and B, respectively.

[0049] Although the dependency between the flow generating capacity and the maximum pressure generating capacity of the respiratory muscles, such as the inspiratory and / or expiratory muscles, can be determined as described above, an alternative way of estimating it is based on the fact that the slope of the maximum pressure generating capacity vs. flow of the inspiratory and / or expiratory muscles is relatively uniform in healthy humans, typically about 17.5 units. Thus, as shown in FIG. 1, the pressure at any point on the line describing the relationship between the maximum flow generating capacity and pressure, i.e., line AB, can be estimated by measuring the flow generated by the user during inspiration and / or expiration at a single pressure load. The subject may use any breathing resistance setting, i.e., a breathing training unit set to any pressure load, etc. For example, a maximum respiratory pressure (MRP), such as a maximum inspiratory pressure or a maximum expiratory pressure, is measured by measuring the flow and respiratory pressure at a specific respiratory pressure setting (pressure load) of the breathing training unit, and then the following, i.e., [ka] where MRP is the maximum (static) respiratory pressure, [ka] and [ka] and R P is the respiratory pressure constant, typically 17.5.

[0050] In certain embodiments of the present disclosure, the respiratory pressure constant is between 10 and 25, such as between 13 and 22, more preferably between 16 and 19, and most preferably between 10 and 25, such as 17.5.

[0051] As a result, the maximum inspiratory pressure (MIP) can be estimated by measuring the flow and inspiratory pressure at the maximum inspiratory effort and at a set pressure load, and then calculating the MIP via the formula set forth above. Similarly, the maximum expiratory pressure (MEP) can be estimated by measuring the flow and expiratory pressure at the maximum expiratory effort and at a set pressure load, and then calculating the MEP via the formula set forth above.

[0052] Thereby, in some embodiments of the present disclosure, the computer-implemented method includes calculating the subject's maximum respiratory pressure by multiplying a respiratory pressure constant and the flow rate obtained at the subject's maximum respiratory effort, such as at a set pressure load, and then adding the pressure value measured at the maximum breath.

[0053] In another embodiment of the present disclosure, a computer-implemented method includes calculating a subject's maximum inspiration pressure by multiplying a respiratory pressure constant and a flow rate obtained at the subject's maximum inspiration effort, such as at a set pressure load, and then adding the pressure value measured at the maximum inspiration.

[0054] In another embodiment of the present disclosure, a computer-implemented method includes calculating a subject's maximum expiratory pressure by multiplying a respiratory pressure constant and a flow rate obtained at the subject's maximum expiratory effort, such as at a set pressure load, and then adding a pressure value measured at the maximum inspiration.

[0055] It should be noted that while a respiration constant of 17.5 is typically accurate for use in calculating respiration values ​​for an average healthy subject, other values ​​of respiration constants may be more suitable depending, for example, on the subject's breathing strength, age, weight, height, sex, physical fitness, and / or any existing medical conditions such as asthma or chronic obstructive pulmonary disease (COPD). However, the respiration constant R P It is particularly preferred that the molecular weight is 10 to 25, more preferably 12.5 to 22.5, even more preferably 15 to 20, still more preferably 16 to 19, still more preferably 17 to 18, and most preferably 17.5.

[0056] In a specific embodiment of the present disclosure, the respiration constant R P is estimated based on one or more respiratory properties of the subject, such as the subject's age, weight, height, sex, physical fitness, and / or existing medical conditions, or any property that may affect the subject's respiratory capacity, including respiratory measurements of the subject, such as respiratory muscle strength versus time for at least one breath and / or respiratory muscle work versus time for at least one breath. The disclosed method may include, for example, estimating the subject's respiratory constant based on a regression model or a machine learning model, which may have been trained based on several training data sets, for example, each training data set including a measured respiratory constant, an individual's respiratory properties, and / or an individual's respiratory measurements, such as respiratory muscle strength versus time for at least one breath and / or respiratory muscle work versus time for at least one breath.

[0057] For example, if the measured pressure and flow at a given resistance setting (set pressure load) of a respiratory training unit are 100 cmH2O and 0.5 l·sec, respectively, -1then the maximum intake pressure is: MIP = (0.5 17.5) + 100 = 109 cmH2O where 17.5 is used as the respiratory pressure constant.

[0058] MRF Similar principles for determining maximum respiratory pressure may also be used to determine maximum respiratory flow (MRF), such as maximum inspiratory flow (MIF) and / or maximum expiratory flow (MEF). Direct measurement of maximum respiratory flow (i.e., at zero pressure load) is typically associated with significant accuracy problems, and instead determining maximum respiratory flow at higher pressure loads generally provides greater accuracy. Maximum respiratory flow is determined by determining the flow and pressure at maximum respiratory effort at a specific pressure load (e.g., respiratory resistance setting of a breathing training unit) and then calculating the maximum respiratory flow using the following formula: [ka] The MRF can be estimated by deriving the MRF through [ka] and [ka] Similarly, maximum inspiratory flow (MIF) and / or maximum expiratory flow (MEF) can be estimated by the same formula by measuring the flow and pressure at maximum inspiratory or expiratory effort, respectively, at a set pressure load.

[0059] In an embodiment of the present disclosure, the computer-implemented method includes calculating the subject's maximum respiratory flow rate by dividing the pressure measured at the subject's maximum respiratory effort at a set pressure load by a respiratory flow constant, and then adding the flow values ​​measured at the maximum breath. Similarly, in a further embodiment of the present disclosure, the computer-implemented method includes calculating the subject's maximum inspiratory flow rate by dividing the pressure measured at the subject's maximum inspiratory effort at a set pressure load by a respiratory flow constant, and then adding the flow values ​​measured at the maximum breath. Furthermore, in another embodiment of the present disclosure, the computer-implemented method includes calculating the subject's maximum expiratory flow rate by dividing the pressure measured at the subject's maximum expiratory effort at a set pressure load by a respiratory flow constant, and then adding the flow values ​​measured at the maximum expiratory effort.

[0060] For example, if the measured pressure and flow rate at a given setting (set pressure load) of the breathing training system are 15 cmH2O and 7.5 l·sec, respectively, -1 then the maximum intake pressure is: PIF=7.5+(15 / 17.5)=8.35l·sec -1 where 17.5 is used as the respiratory pressure constant.

[0061] gradient In some embodiments of the present disclosure, a computer-implemented method includes calculating the slope of a gradient that describes the relationship between a subject's maximum respiratory flow and pressure by measuring the maximum respiratory flow and pressure at two different respiratory resistances (pressure loads) and then calculating the slope.

[0062] The slope of a line describing the maximum pressure generating capacity of the respiratory muscles (e.g., line AB in FIG. 1) can be calculated by measuring the pressure and flow at two separate pressure loads, e.g., two different respiratory resistance settings of a breathing training system, and then using the following formula: [ka] can be determined by deriving the slope through [ka] and [ka] And P F = pressure measured at pressure load F, P A = pressure measured at pressure load A, and F and A are two separate pressure loads, e.g., obtained at two different respiratory resistance settings of a breathing training system. The slope is preferably calculated based on pressure and flow values ​​obtained during the subject's maximal inspiratory or expiratory effort, e.g., the slope can describe either the relationship between pressure and flow during the subject's maximal expiratory effort or the relationship between pressure and flow during the subject's maximal inspiration.

[0063] For example, the breathing training system may be set to a breathing resistance setting A, which corresponds to a first pressure load, at which the pressure and flow are 15 cmH2O and 7.5 l·sec 2 at the subject's maximum inspiratory (or expiratory) effort. -1 The breathing training system may then be set to a breathing resistance setting F, corresponding to a second pressure load, at which pressure and flow are measured to be 100 cmH2O and 0.5 l·sec at the subject's maximum inspiratory (or expiratory) effort. -1 The slope is then given by slope = (100-15) / (7.5-0.5) = 12.14.

[0064] In a preferred embodiment of the present disclosure, the computer-implemented method includes calculating the respiratory muscle force versus time of at least one breath of the subject. The respiratory muscle force versus time may then be used to derive one or more training intensity indicators. For example, the peak respiratory muscle force per breath or any other interval, which is the highest value of the (instantaneous) respiratory muscle force during that time interval. The (instantaneous) respiratory muscle force is the product of the (instantaneous) flow rate and pressure. Furthermore, the average respiratory muscle force may be calculated per breath or any other interval. The average respiratory muscle force may be calculated, for example, as the average value of the (instantaneous) force throughout the entire breath or any other interval. In an embodiment of the present disclosure, the computer-implemented method is configured to calculate the peak respiratory muscle force (PRP) per breath or any other interval, such as a predefined time interval, a breathing training session, or a portion thereof, by calculating the highest (instantaneous) respiratory muscle force value per breath or said other interval. In another embodiment of the present disclosure, the computer-implemented method is configured to calculate the average respiratory muscle strength per breath, or any other interval, such as a predefined time interval, breathing training session, or part thereof, by calculating the average value of (instantaneous) respiratory muscle strength per breath or said other interval.

[0065] In a further embodiment of the present disclosure, the computer-implemented method is configured to calculate a session average of peak respiratory muscle force (PRP) per breath and / or average respiratory muscle work per breath. The session length can be a predefined amount of time, a predefined number of movements, a predefined number of breaths, and / or a time until fatigue is identified.

[0066] In a specific embodiment of the present disclosure, the computer-implemented method includes estimating the peak respiratory muscle strength of the subject by multiplying half the maximum respiratory pressure and half the maximum respiratory flow rate. It has been identified that the theoretical peak respiratory muscle strength of the subject occurs at approximately half the maximum respiratory pressure and approximately half the maximum respiratory flow rate, and thus these values ​​allow a good approximation of the theoretical peak respiratory muscle strength. In a preferred embodiment of the present disclosure, the computer-implemented method is configured to calculate the peak respiratory muscle strength, such as maximum inspiratory force and / or maximum expiratory force, by multiplying the maximum respiratory pressure, such as maximum inspiratory pressure and / or maximum expiratory pressure, respectively, by the maximum respiratory flow rate, such as maximum inspiratory flow rate and / or maximum expiratory flow rate, respectively, by a peak respiratory muscle strength coefficient, the peak respiratory muscle strength coefficient being 0.1 to 0.4, more preferably 0.15 to 0.35, even more preferably 0.2 to 0.3, and most preferably 0.25. The peak respiratory muscle strength is thereby calculated as follows, i.e. PRP = MRF MRP F P (4) where PRP is peak respiratory muscle force, MRF is maximum respiratory flow, MRP is maximum respiratory pressure, and F P is the peak respiratory muscle force index, typically 0.25.

[0067] Subject-specific force targeting In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a subject-specific respiratory muscle force target over at least one breath, a predetermined amount of time, a training session, or a portion thereof. The calculation may be based on respiratory muscle force versus time relative to a target respiratory muscle force for the subject, such as a percentage of peak respiratory muscle force. The subject-specific force target may be a predetermined percentage of peak respiratory muscle force, such as, for example, 50%, 60%, or 80%. A respiratory training system may be configured to display the subject-specific force target to the subject, for example, using the system, along with the (continuously updated) peak respiratory muscle force per breath or the average respiratory muscle force per breath during the training session.

[0068] Object-specific force coefficients In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a subject-specific respiratory muscle force index based on the subject's average peak respiratory muscle force of multiple breaths relative to the subject's peak respiratory muscle force, whereby the subject-specific force index provides an indication of the degree to which the subject is approaching reaching the subject's specific maximum, i.e., peak, respiratory muscle force.

[0069] The subject-specific respiratory muscle force coefficient may be a subject-specific inspiratory force coefficient and / or a subject-specific expiratory force coefficient. For example, the subject-specific inspiratory force coefficient may be calculated based on the subject's average maximum inspiratory force of multiple breaths relative to the subject's maximum inspiratory force. Additionally or alternatively, the subject-specific respiratory muscle force coefficient may be a subject-specific expiratory force coefficient calculated based on the subject's average maximum expiratory force of multiple breaths relative to the subject's maximum expiratory force.

[0070] Workload The maximum pressure generating capacity of the respiratory muscles (inspiratory and expiratory muscles) varies across lung volumes (FIG. 2). At a given lung volume, respiratory pressure (P) is given by the following equation: P = MRP - MRP · (V 2 +2 V) / (V INMAX 2 +2 V INMAX )(5) where MRP is the maximum [static] respiratory pressure (or an inference from the maximum respiratory flow and / or flow measured, e.g., during a loaded respiratory effort), V is the general lung capacity, V INMAX is the maximum inhalation volume (i.e., vital capacity).

[0071] Estimation of the theoretical maximum work capacity The maximum achievable work over a single breath is the integral of the maximum pressure over volume (the area under the curve depicted in Figure 2), W MAX , i.e. W MAX =∫ VC PdV(6) W MAX=MRP·[(2 / 3·V INMAX 2 +V INMAX ) / (V INMAX +2)](7) It can be calculated by:

[0072] MRP = 160 cmH2O, V INMAX = 4.6 L, the maximum achievable work over a breath is W MAX = 453 cmH2O L.

[0073] For example, over 30 breath training sessions, the total maximum theoretical predicted work is: W MAXTOT =30·453=13,590cmH2O·L W MAXTOT = 0.090665 cmH2O L / Joule 13,590 cmH2O L = 1,232 Joules As stated above. NOTE: 0.090665 is the constant that converts cmH2O·L to Joules.

[0074] In typical circumstances, the maximum achievable respiratory muscle work is (theoretical) W MAX One reason for this is that the W MAX The equation for calculating W is based on static inspiratory muscle strength. Pressure generating capacity (dynamic strength) is typically significantly lower. Another reason is that W decreases over the entire training session due to the effects of fatigue. MAX It is typically not possible to work at 100% of the theoretical W. Therefore, in order to set real-time training intensity targets and to retrospectively evaluate training session quality, MAX The coefficient of the physiological maximum work capacity (W MAXphys ) can be used to approximate the W MAX is W over several breaths completed within a given session, e.g., 30. MAXphysIt has been shown to be a good approximation of this ratio, which may be 0.1 to 0.9, more preferably 0.2 to 0.8, even more preferably 0.3 to 0.7, even more preferably 0.4 to 0.6, and most preferably 0.5.

[0075] The maximum physiological work capacity over a given breath is given by: W MAXphys =0.5·MRP·[(2 / 3·V INMAX 2 +V INMAX ) / (V INMAX +2)](8) and the maximum physiological work capacity over a training session can be calculated as follows: W MAXphysTOT =0.5 n MRP [(2 / 3 V INMAX 2 +V INMAX ) / (V INMAX +2)](9) where n is the number of breaths in a training session. As explained elsewhere herein, physiological maximum work capacity is typically calculated over a breath, i.e., inspiration, expiration, or inspiration and expiration, or over multiple breaths, i.e., multiple inspirations, multiple expirations, or multiple inspirations and expirations.

[0076] ARW In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a cumulative respiratory muscle work over at least one breath of the subject. In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a cumulative respiratory muscle work versus time over at least one breath of the subject. It is noted that the cumulative respiratory muscle work can be either a cumulative inspiratory work and / or a cumulative expiratory work. The cumulative respiratory muscle work may include a factor between 0 and 1 for calculating the physiological maximum work capacity, which is typically 0.5.

[0077] Respiratory muscle work per breath (W B) can be calculated by integrating the measured respiratory muscle forces (pressure·flow) over time, i.e., summing the products of pressure and flow every sample, and then multiplying the sum by the sample interval. Alternatively, W B can be calculated as the integral of pressure over volume (area under the curve in FIG. 2). The respiratory muscle work per breath may be the inspiratory work per breath and / or the expiratory work per breath, and the method may further include calculating a cumulative work over the training session based on the work per breath by summing the work per breath of the training session.

[0078] In a specific embodiment of the present disclosure, the computer-implemented method includes calculating the average respiratory muscle work of a subject's multiple breaths. Note that the average respiratory muscle work can be either the average inspiratory work and / or the average expiratory work. As explained elsewhere herein, the average respiratory muscle work per breath can be derived by summing the products of pressure and flow for each sample and then multiplying the sum by the sampling interval. Alternatively, W B can be calculated as the integral of pressure over volume.

[0079] In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a subject-specific workload target for at least one breath based on the subject's maximum respiratory muscle work capacity over one breath. The maximum respiratory muscle work capacity may be based on a physiological respiratory muscle work capacity, such as, for example, the maximum respiratory muscle work capacity multiplied by a factor, typically 0.5. The subject-specific workload target is preferably a subject-specific workload factor of the physiological respiratory muscle work capacity. The workload factor may be, for example, 50%, 70%, or 90%, such that the subject-specific workload target reflects the subject's training intensity target. The subject-specific workload target (T W ) is expressed by the following formula: T W = F × 0.5 × MRP × [(2 / 3 V INMAX 2 +V INMAX ) / (VINMAX +2)](10) where F is the subject-specific workload coefficient and 0.5 is used as the coefficient to calculate the physiological respiratory muscle workload capacity. Depending on the type of training (inspiratory training, expiratory training, or inspiratory and expiratory training), the subject-specific workload target can be based on inspiratory data, expiratory data, or both.

[0080] Subject-specific effort feedback In a specific embodiment of the present disclosure, the computer-implemented method includes calculating a subject-specific workload feedback for at least one breath based on the cumulative respiratory muscle workload versus time for the subject's maximum respiratory muscle workload capacity over at least one breath. The maximum respiratory muscle workload capacity may be, for example, the physiological respiratory muscle workload capacity, i.e., the maximum respiratory muscle workload capacity multiplied by a compensation factor, typically 0.5. The subject-specific workload feedback (F W ) is F W =100×W A / W MAXphysTOT (11), where W A is typically the cumulative respiratory muscle work of the training session, and W MAXphys is the physiological respiratory muscle work capacity of the training session, i.e., physiological respiratory muscle work capacity times the number of breaths of the training session.

[0081] In a specific embodiment of the present disclosure, the step of calculating the subject-specific workload feedback is based on the subject's cumulative respiratory muscle workload for multiple breaths relative to the maximum respiratory muscle workload capacity for the multiple breaths. The subject-specific workload feedback is calculated according to the formula F W =100×W TOT / W MAXphysTOT and may be based on, for example, the work of inspiration, the work of expiration, or a combination thereof, depending on the type of training desired.

[0082] The method may further be configured to categorize the subject-specific workload feedback into intensity ratings, such as low, medium, and high. For example, an F of less than 35 may be Wresults in a rating of "low" and an F of 35-65 W results in a rating of "medium" and an F above 65 W will result in a rating of "High".

[0083] Breathing Detection System In a further aspect, the present disclosure relates to a breath sensing system. The breath sensing system may be configured for retrofitting of a breath training and testing device. The breath sensing system may comprise an electronic sensor unit and / or a software application, i.e., a computer program (product). In an embodiment of the present disclosure, the electronic sensor comprises at least one pressure sensor and a processing unit. The breath sensing system may comprise a single pressure sensor or multiple pressure sensors with different measurement ranges and / or resolutions. At least the pressure sensor is preferably adapted to measure air pressure in the inspiratory and / or expiratory airways of the breath training and testing device. Thus, the size of the pressure sensor is preferably small enough that the pressure sensor can be accommodated by the inspiratory and / or expiratory airways of the breath training and testing device without obstructing the airflow. The electronic sensor unit may further comprise a processing unit for transmitting pressure data, such as for transmitting the pressure data wirelessly.

[0084] In a preferred embodiment of the present disclosure, the electronic sensor unit is removably mounted on the housing of the breathing unit. Preferably, the mounting connection between the breathing unit and the electronic sensor unit is airtight. The removable electronic sensor unit may, for example, comprise a means for snap-fitting into the main unit of the breathing unit. Thereby, the removable electronic sensor unit may comprise one part of the snap connection, such as a male part, and the main unit may comprise another part of the snap connection, such as a female part.

[0085] In a preferred embodiment of the present disclosure, the breath sensing system comprises a software application executable on a remote processing device such as a smartphone, i.e. similar to a computer program (product) having instructions executable by a remote processing device such as a smartphone. The software application / computer program is preferably configured for continuously acquiring pressure data via a receiver on the remote processing device and / or for performing methods for calculation of breathing exercise intensity in a data set representative of the subject's breath, as disclosed elsewhere herein.

[0086] The breath sensing system is preferably arranged for breath training and device adaptation. For example, the breath sensing system may enable breath pressure sensing, calculation of breath training intensity in a data set representative of the subject's breath, and calculation of respiratory muscle force versus time for at least one breath of the subject and / or respiratory muscle work for at least one breath of the subject.

[0087] The electronic sensor unit may be adapted to be positioned, for example, in the inspiratory and / or expiratory airways of the breathing training and testing device. However, in a specific embodiment, the breath sensing system may comprise a tubular connector arranged to be connected to the inspiratory and / or expiratory airways of the breathing training and testing device. The tubular connector is preferably arranged to contain the electronic sensor unit such that the sensor unit measures pressure within the connector portion. The tubular connector is preferably configured to connect to an end of the inspiratory and / or expiratory path. The tubular connector may further comprise a mouthpiece.

[0088] Respiratory Training and Testing System In yet a further aspect, the present disclosure relates to a breathing training and testing system for exercising and analyzing a subject's breathing muscles. The breathing muscle training and testing system typically comprises a breathing unit through which the subject can breathe, such as performing inhalation, exhalation, or both. The breathing unit may comprise a mouthpiece, an electronic sensor, and / or a software application. It is preferred that the mouthpiece is connected to at least one airway, more preferably at least one inhalation (inhalation) airway and exhalation (exhalation). It is further preferred that the airway has adjustable airflow resistance, such as at least one inhalation airway with adjustable inhalation airflow resistance and at least one exhalation airway with adjustable exhalation airflow resistance, i.e., therefore, the inhalation airflow resistance can be adjusted independently from the exhalation airway resistance, and vice versa. The electronic sensor unit may comprise at least one pressure sensor for measuring air pressure, and the sensor unit may be configured to measure air pressure, for example, in the mouthpiece, and additionally or alternatively, the electronic sensor may be configured to measure air pressure in an airway, such as the inhalation airway and / or the exhalation airway.

[0089] FIG. 1 shows the general concept of the disclosed breathing training and testing system for exercising and analyzing a subject's breathing. As shown, the system may comprise a breathing unit (2), preferably with individually adjustable airflow resistance on the inhaled and exhaled air. The breathing unit preferably holds an electronic sensor unit in a mouthpiece that measures exercise session data such as air pressure and / or time and / or duration and / or airflow resistance settings. The electronic sensor unit may, in one embodiment, store the measured data locally. The unit (2) may also perform some local calculations, including a data set representing the subject's breathing, such as through separate inhalation and exhalation airways with predefined breathing resistances. The data and / or calculations may include flow, pressure, force, work, respiratory volume deviation, and respiratory muscle strength. The same calculations may alternatively or additionally be performed on a remote processing device (3), such as a mobile phone. The system is preferably configured to provide respiratory motion feedback to the subject (1) by displaying text or graphics on a display of the remote processing device (3), by tactile indicators or audio of the remote processing device, by its speaker, etc. Motion data storage, data analysis, session recommendations, and settings can be located both on the system itself, such as the breath unit or remote processing device, and / or on the connected platform (4).

[0090] 4A-B disclose an embodiment of an example of a breathing unit with a mouthpiece 5 that creates an airtight connection to the mouth and allows breathing through the device. The breathing unit of FIG. 4 is clearly portable, as can be seen from the size comparison of FIG. 3 between the smartphone 3, the breathing unit 2 and the user's head 1. The breathing unit comprises a valve housing 6 with two one-way valves 8, 9 that control the airflow so that the inhaled air passes through one adjustable resistance 8 and the exhaled air passes through another adjustable resistance 9, as also shown by the arrows in FIG. 4A. The two adjustable resistance interfaces 8, 9 may include predefined settings that correspond to strict airway purification. The user may adjust these settings and read the set values ​​on the device. An electronic unit 7, which may include a pressure gauge connected to the inside of the valve housing 6, is mounted on the device. FIG. 4B shows the situation in which the electronic unit 7 is removed from the valve housing 7. Thereby, the valve housing can be thoroughly cleaned. This also allows the use of different valve housings with the same electronic unit 7.

[0091] In a preferred embodiment of the present disclosure, the breathing training and testing system includes a processing unit configured to transmit pressure data. The pressure data may be acquired, for example, by at least one electronic sensor located in the mouthpiece and / or at least one airway, as disclosed elsewhere herein. The processing unit may be connected to a wireless network, for example, via Bluetooth, Wi-Fi, TMor mobile broadband. In a specific embodiment of the present disclosure, the breathing training and testing system comprises a software application executable on a remote processing device, such as a smartphone. The software application may be configured to continuously acquire pressure data via a receiver on the remote processing device and execute a computer-implemented method for calculation of breathing exercise intensity in a dataset, such as a dataset representing a subject's breath through separate inhalation and exhalation airways with a predefined breathing resistance.

[0092] In specific embodiments of the present disclosure, the software application is configured to display instantaneous and / or cumulative calculated force and / or work data on the screen of the remote processing device. The software application may additionally or alternatively be configured to display any type of indicator of respiratory training intensity, subject-specific respiratory capacity, subject-specific target level, subject-specific feedback based on respiratory pressure and / or respiratory muscle work and / or force, as disclosed elsewhere herein. For example, the software application may be configured to display subject-specific workload feedback, subject-specific respiratory muscle force target, average respiratory muscle work, cumulative respiratory muscle work, average peak respiratory muscle force, calculated (measured or theoretical) peak respiratory muscle force, calculated (measured or theoretical) maximum respiratory flow, calculated (measured or theoretical) maximum respiratory pressure, and / or respiratory rate of change of pressure, which parameters may be either real-time, averaged, or cumulative values, and further may be based on measurements of or values ​​calculated to reflect one or more inspirations, one or more expirations, and / or one or more inspirations and expirations.

[0093] In a further embodiment of the present disclosure, the remote processing device comprises a processing unit and / or an antenna for communicating with a remote server, such as by electrical communication. The software application is preferably configured for performing the transmission of the breathing training data to the remote server. The breathing training information preferably includes calculated parameters of the completed breathing training session, e.g. related to respiratory muscle strength and / or respiratory muscle work. The breathing training information preferably includes average respiratory muscle strength, peak respiratory muscle strength, maximum rate of change of pressure, average peak respiratory muscle strength, subject-specific respiratory muscle force target, maximum respiratory pressure, maximum respiratory flow rate, subject-specific force coefficient, cumulative respiratory muscle work, average respiratory muscle work, subject-specific work feedback, and / or subject-specific work target.

[0094] The remote server is preferably configured to arrange the received breathing exercise information from the multiple subjects in a leaderboard. The remote server may be further configured to calculate a score for each received breathing exercise information based on a combination of parameters of the breathing exercise information.

[0095] In a preferred embodiment of the present disclosure, the software application is configured to read the leaderboard and associated data, such as a score provided for each breathing exercise information by the remote server and / or one or more parameters of the breathing exercise information.

[0096] In a further embodiment of the present disclosure, the software application is configured to suggest a specific training routine based on the subject's respiratory characteristics, such as age, weight, height, sex, physical fitness, and / or any characteristics that may affect the subject's breathing capacity, including the subject's existing medical condition, such as asthma or chronic obstructive pulmonary disease, or the subject's respiratory measurements, such as respiratory muscle strength versus time for at least one breath and / or respiratory muscle work versus time for at least one breath. The method of the present disclosure may include predicting a training routine that provides the best training stimulus, for example, based on a regression model or machine learning model, which may have been trained based on several training data sets, each training data set including measured respiratory constants, individual respiratory characteristics, and / or individual respiratory measurements.

[0097] Another embodiment of a breathing device with two individually / independently controllable resistance units is shown in Fig. 5. Fig. 5 shows only exterior views from different viewing angles, Fig. 5A is a perspective view, Fig. 5B is a side view looking directly into one of the resistance units 8, Fig. 5C is a view looking into the mouthpiece 18 and the mouthpiece airway 1, Fig. 5D is a top view where the removable electronic unit 13 can be seen, and Fig. 5D is a front view looking directly into the removable electronic unit 13. The removable electronic unit 13 is attached to the device housing 26 using a snap-fit ​​arrangement so that the electronic unit 13 is securely attached to the housing 26 during use but can be removed for cleaning purposes.

[0098] A breathing device according to another embodiment of the present disclosure is shown in Fig. 6. Fig. 6A is a top view in which the removable electronic unit 13 can be seen. Fig. 6B is a side view and Fig. 6A is a perspective view looking directly at one of the resistance units 8. The breathing device of Figs. 5 and 6 is also explicitly portable, since the mouthpiece 18 fits into the human mouth.

[0099] item 1. A computer-implemented method for calculation of breathing exercise intensity in a dataset representing a subject breathing through an inhalation airway and / or an exhalation airway having a predefined breathing resistance, comprising: calculating respiratory muscle force versus time for at least one breath of the subject; and / or calculating respiratory muscle work for at least one breath of the subject; 4. A computer-implemented method comprising: 2. The method of any of the preceding items, wherein the dataset represents a subject's breath through an inhalation airway and / or an exhalation airway having a predefined respiratory resistance. 3. The method according to any of the preceding items, wherein the calculation of breathing exercise intensity is a real-time process performed using a continuously received stream of data. 4. The method of any of the preceding items, wherein the dataset includes pressure data versus time obtained associated with at least one of the inhalation and exhalation airways. 5. The method of any of the preceding items, comprising the step of calculating the average respiratory muscle force of at least one breath of the subject. 6. The method of any of the preceding items, comprising the step of calculating peak respiratory muscle force for at least one breath of the subject. 7. A method according to any of the preceding items comprising the step of calculating the respiratory rate of change of pressure versus time for at least one breath of the subject and / or the maximum rate of change of pressure for at least one breath of the subject. 8. The method of any preceding item comprising the step of calculating the subject's average peak respiratory muscle force over multiple breaths. 9. A method according to any of the preceding items, comprising calculating a subject-specific respiratory muscle strength target over at least one breath based on respiratory muscle strength versus time relative to a target respiratory muscle strength for the subject, such as a percentage of peak respiratory muscle strength. 10. A method according to any of the preceding items, comprising the step of calculating the subject's maximum respiratory pressure by multiplying a respiratory pressure constant and the flow rate obtained at the subject's maximum respiratory rate, and then adding the pressure value measured at the maximum respiratory rate. 11. A method according to any of the preceding items, comprising the step of calculating the subject's maximum respiratory flow by dividing the pressure measured at the subject's maximum breathing by a respiratory flow constant, and then adding the flow values ​​measured at the maximum breathing. 12. The method of any of the preceding items, comprising calculating the slope of a gradient that describes the relationship between a subject's maximum respiratory flow and pressure by measuring the maximum respiratory flow and pressure at two different respiratory resistances and then calculating the slope. 13. A method according to any preceding item comprising estimating the subject's peak respiratory muscle strength by multiplying half the peak respiratory muscle strength by half the maximum respiratory flow. 14. A method according to any preceding item, comprising the step of calculating a subject-specific force coefficient based on the subject's peak respiratory muscle force relative to the subject's average peak respiratory muscle force over multiple breaths. 15. The method of any of the preceding items, comprising the step of calculating cumulative respiratory muscle work versus time over at least one breath of the subject. 16. A method according to any preceding item comprising the step of calculating average respiratory muscle work over multiple breaths of the subject. 17. A method according to any of the preceding items, comprising calculating subject-specific workload feedback for at least one breath based on cumulative respiratory muscle work versus time relative to the subject's maximum respiratory muscle work capacity over one breath. 18. A method according to any preceding item, comprising calculating subject-specific workload feedback based on the subject's cumulative respiratory muscle work of multiple breaths relative to the maximum respiratory muscle work capacity for said multiple breaths. 19. A system for calculation of breathing exercise intensity in a dataset representing a subject's breath, the system comprising a non-transient computer-readable storage device for storing instructions which, when executed by a processor, implement a method for calculation of breathing exercise intensity in a dataset representing a subject's breath according to any of items 1-18. 20. A mobile device comprising a processor and a memory and adapted to implement a method for calculation of breathing exercise intensity in a dataset representative of a subject's breath according to any of items 1-18. 21. A computer program having instructions that, when executed by a computing device or system, cause the computing device or system to calculate a breathing exercise intensity in a dataset representative of a subject's breath according to any of items 1-18. 22. A breath sensing system arranged to modify a breathing training or testing device, comprising: a. An electronic sensor unit, i. at least one pressure sensor for measuring air pressure in the inspiratory and / or expiratory airways of the breathing training and testing device; ii. A processing unit for transmitting the pressure data; an electronic sensor unit comprising: b. A computer program having instructions that, when executed by a remote processing device, such as a smartphone, cause the processing device to: i. continuously acquiring said pressure data via a receiver on a remote processing device; ii. carrying out a method according to any of the preceding paragraphs; A computer program that causes A sensing system comprising: 23. A respiratory sensing system as described in item 22, wherein the system further comprises a tubular connector arranged for connection to an end of the inspiratory airway and / or expiratory airway, and the pressure sensor is contained by the tubular connector. 24. A respiratory sensing system as described in any one of items 22-23, wherein the computer program has instructions which, when executed by the remote processing device, cause the processing device to display instantaneous and / or cumulative calculated force and / or work volume data on a screen of the remote processing device. 25. A breathing training and testing system for exercising and analyzing the breathing of a subject, comprising: A breathing unit comprising: A mouthpiece, at least one inhalation airway having adjustable inhalation airflow resistance; at least one exhalation airway having adjustable exhalation airflow resistance; A mouthpiece connected to the An electronic sensor unit, at least one pressure sensor for measuring air pressure in the mouthpiece; A processing unit for transmitting the pressure data. An electronic sensor unit comprising: A breathing unit comprising: A computer program comprising instructions that, when executed by a remote processing device, such as a smartphone, cause the processing device to: continuously acquiring said pressure data via a receiver on a remote processing device; carrying out a method according to any of the preceding items; A computer program that performs A breathing training and testing system comprising: 26. A respiratory training and testing system as described in item 25, wherein the computer program has instructions which, when executed by the remote processing device, cause the processing device to display instantaneous and / or cumulative calculated force and / or work volume data on a screen of the remote processing device.

Claims

1. 1. A computer-implemented method for calculation of a breathing exercise intensity in a dataset comprising pressure data obtained in association with at least one of an inhalation airway and an exhalation airway of a breathing exercise or testing device, the dataset representing a subject's breathing through the airway having a predefined breathing resistance, the method comprising: calculating respiratory muscle force versus time for at least one breath of the subject; and / or calculating respiratory muscle work for at least one breath of the subject; and Calculating the peak respiratory muscle strength of at least one breath of the subject; and / or calculating the average peak respiratory muscle force over multiple breaths of the subject Including, A method wherein the calculation of the breathing exercise intensity is a real-time process performed using a continuously received stream of data.

2. The method of claim 1, comprising the step of calculating the respiratory rate of change of pressure versus time for at least one breath of the subject and / or the maximum rate of change of pressure for at least one breath of the subject.

3. A method according to any of the preceding claims, comprising a step of calculating a subject-specific respiratory muscle strength target over at least one breath based on the respiratory muscle strength versus time relative to a target respiratory muscle strength for the subject, such as a percentage of peak respiratory muscle strength.

4. A method according to any of the preceding claims, comprising the step of calculating the subject's maximum respiratory pressure by multiplying a respiratory pressure constant and a flow rate obtained at the subject's maximum respiratory rate, and then adding the pressure value measured at the maximum respiratory rate.

5. A method according to any of the preceding claims, comprising the step of calculating the subject's maximum respiratory flow by dividing the pressure measured at the subject's maximum breathing by a respiratory flow constant and then adding the flow value measured at the subject's maximum breathing.

6. A method according to any of the preceding claims, comprising the step of measuring maximum respiratory flow and the pressure at two different respiratory resistances and then calculating the slope of a gradient describing the relationship between the subject's maximum respiratory flow and the pressure by calculating the gradient.

7. A method according to any of the preceding claims, comprising the step of estimating the subject's peak respiratory muscle strength by multiplying half the peak respiratory muscle strength by half the maximum respiratory flow.

8. A method according to any of the preceding claims, comprising a step of calculating a subject-specific force coefficient based on the average peak respiratory muscle strength of the subject for multiple breaths relative to the subject's peak respiratory muscle strength.

9. A method according to any of the preceding claims, wherein the method includes a step of calculating cumulative respiratory muscle work versus time over at least one breath of the subject, and / or a step of calculating average respiratory muscle work over multiple breaths of the subject.

10. A method according to any of the preceding claims, comprising the step of calculating subject-specific workload feedback for at least one breath based on the cumulative respiratory muscle work versus time relative to the subject's maximum respiratory muscle work capacity over a single breath, and / or the step of calculating subject-specific workload feedback based on the cumulative respiratory muscle work of the subject's multiple breaths relative to the subject's maximum respiratory muscle work capacity for the multiple breaths.

11. 1. A breath sensing system for a breath training or testing device having an inspiratory airway and / or an expiratory airway, the sensing system comprising: An electronic sensor unit, comprising: at least one pressure sensor for measuring air pressure in the inspiratory airway and / or the expiratory airway of the breathing training and testing device; A processing unit for transmitting the pressure data. an electronic sensor unit comprising: A computer program comprising instructions that, when executed by a remote processing device, such as a smartphone (3), cause the processing device to: i. continuously acquiring said pressure data via a receiver on said remote processing device; ii. carrying out a method according to any preceding claim; A computer program that causes A respiratory sensing system comprising:

12. 11. The respiratory sensing system of claim 10, wherein the computer program has instructions which, when executed by the remote processing device, cause the processing device to display instantaneous and / or cumulative calculated force and / or work rate data on a screen of the remote processing device.

13. 1. A breathing training and testing system for exercising and analyzing the breathing of a subject, the breathing training and testing system comprising: A breathing unit, comprising: A mouthpiece, comprising: at least one inhalation airway having adjustable inhalation airflow resistance; at least one exhalation airway having adjustable exhalation airflow resistance; A mouthpiece connected to the An electronic sensor unit, comprising: at least one pressure sensor for measuring air pressure within the mouthpiece; A processing unit for transmitting the pressure data. An electronic sensor unit comprising: A breathing unit comprising: A computer program having instructions that, when executed by a remote processing device, such as a smartphone, cause the processing device to: continuously acquiring the pressure data via a receiver on the remote processing device; Carrying out the method according to any one of claims 1 to 9; A computer program that causes A breathing training and testing system comprising:

14. 14. The breathing exercise and testing system of claim 13, wherein the computer program has instructions which, when executed by the remote processing device, cause the processing device to display instantaneous and / or cumulative calculated force and / or work volume data on a screen of the remote processing device.

15. A system for calculation of respiratory exercise intensity in a dataset representing a subject's breath, the system comprising a non-transient computer-readable storage device for storing instructions which, when executed by a processor, perform a method for calculation of respiratory exercise intensity in a dataset representing a subject's breath in accordance with any of claims 1 to 9.

16. A computer program having instructions which, when executed by a computing device or system, cause the computing device or system to calculate respiratory training intensity in a dataset representing a subject's breath in accordance with any of claims 1 to 9.

Images