Testing system to diagnose neuromuscular deconditioning and pathologic conditions

Abstract

A testing system on an exercise machine includes sensors including a flow measurement device, a gas analyzer device, a force or torque measurement device and an isokinetic control device. The sensors, upon activation of the exercise machine and a force being applied by an entity onto a contacting surface of the exercise machine, capture sensor data indicative of a pulmonary oxygen uptake VT)2, pulmonary ventilation, heart rate and the force or torque. The testing system includes sensor processing circuitry that obtains the captured sensor data and processes the captured sensor data and a computing system comprising one or more processors.

Description

TESTING SYSTEM TO DIAGNOSE NEUROMUSCULAR DECONDITIONING AND PATHOLOGIC CONDITIONSGOVERNMENT LICENSE RIGHTS

[0001] This invention was made with government support under R01HL166850 awarded by the National Institutes of Health. The government has certain rights in the invention.CROSS REFERENCE TO RELATED APPLICATION

[0002] This application claims the benefit under 35 U.S.C. 119(e) of U.S. Provisional Application No. 63 / 582,156, filed September 12, 2023, the content of which is hereby incorporated in its entirety.TECHNICAL FIELD

[0003] This disclosure relates to exercise testing in order to identify abnormalities in neuromuscular performance and differentiate between neuromuscular deconditioning and disease conditions, which provides improved diagnostic and therapeutic outcomes for treatment of neuromuscular deconditioning and / or pathologic conditions.BACKGROUND

[0004] When an individual undergoes exercise testing, variables such as those related to the cardiovascular and pulmonary systems are measured.SUMMARY

[0005] Various embodiments of the present disclosure include a testing system associated with an exercise machine, the testing system configured to diagnose neuromuscular deconditioning or disease of an entity. The testing system includes sensors comprising a flow measurement device, a gas analyzer device, a force measurement device and an isokinetic control device. The sensors are configured to, upon activation of the exercise machine and a force being applied by the entity onto a contacting surface of the exercise machine, capture sensor data indicative of a pulmonary oxygen uptake VO2, pulmonary ventilation, and the isokinetic force or torque. The testing system includes sensor processing circuitry configured to obtain the captured sensor data and to process the captured sensor data. The testing system includes a computing system comprising one ormore processors configured to: obtain the processed sensor data; determine variables indicative of deconditioning or disease associated with the entity based on the processed sensor data, the variables comprising a peak voluntary isokinetic power Piso, (e.g., a baseline PiS0), an aerobic power index (API), a fatigue index (FI), and a power reserve (PR); and perform a diagnosis of deconditioning or disease based on the determined variables.

[0006] In some embodiments, the baseline Pisois determined when the entity is in an unexercised state.

[0007] In some embodiments, the one or more processors are further configured to obtain an end ramp power in hyperbolic control mode and end-exercise PiS0in isokinetic control mode when the entity is in an intolerance state, wherein the intolerance state is based on a point in time at which the entity is unable to continue exercising, where pulmonary oxygen uptake, VO2 and ventilation have reached peak values. The end-exercise Pisomay be distinct from the baseline Pjso.

[0008] In some embodiments, the obtaining of the API and the FI is based on the baseline Piso and the end ramp power.

[0009] In some embodiments, the API is obtained based on a ratio of the end ramp power to the baseline Piso.

[0010] In some embodiments, the FI is obtained based on a difference between the end ramp power and the baseline P;soand normalized by the end ramp power.

[0011] In some embodiments, the PR is obtained based on the difference between the end ramp power and the end exercise PiSO.

[0012] In some embodiments, the one or more processors are further configured to implement a therapeutic treatment in response to the diagnosis.

[0013] In some embodiments, the therapeutic treatment comprises selectively implementing a surgery based on the diagnosis.

[0014] In some embodiments, the therapeutic treatment comprises selectively providing a drug treatment at a determined dosage based on the diagnosis.

[0015] In some embodiments, the one or more processors are further configured to perform selectively obtaining additional sensor data based on a diagnosis and a certainty level of the diagnosis.

[0016] Various embodiments of the present disclosure include a method of diagnosing neuromuscular deconditioning and / or disease of an entity, the method associated with an exercise machine. The method includes, upon activation of the exercise machine and a force or torque being applied by the entity onto a contacting surface of the exercise machine, capturing, from sensors, sensor data, the sensor data indicative of a pulmonary oxygen uptake VO2. pulmonary ventilation and the force. The method includes obtaining, using sensor processing circuitry, the captured sensor data; processing, using the sensor processing circuitry, the obtained sensor data; obtaining, using a computing system comprising one or more processors, the processed sensor data; determining, using the computing system, variables indicative of deconditioning or disease associated with the entity based on the processed sensor data, the variables comprising a peak voluntary isokinetic power Piso, an aerobic power index (API), a fatigue index (FI), and a power reserve (PR); and performing a diagnosis of deconditioning or disease based on the determined variables.

[0017] In some embodiments, the obtaining of the API and the FI is based on the baseline Piso and the end ramp power.

[0018] In some embodiments, the PR is obtained based on the difference between the end ramp power and the end exercise Piso.

[0019] In some embodiments, the method further comprises obtaining an end ramp power when the entity is in an intolerance state, wherein the entity is unable to continue exercising, where pulmonary oxygen uptake, VO2 and pulmonary ventilation have reached peak values.

[0020] In some embodiments, the obtaining of the API and the FI is based on the baseline Piso and the end ramp power.

[0021] In some embodiments, the API is obtained based on a ratio of the end ramp power to the baseline Pjso.

[0022] In some embodiments, the FI is obtained based on a difference between the end ramp power and the baseline Pisoand normalized by the end ramp power.

[0023] In some embodiments, the PR is obtained based on the difference between the end ramp power and the end exercise Piso.

[0024] In some embodiments, the method includes implementing a therapeutic treatment in response to the diagnosis.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 is a diagram illustrating a testing system and a corresponding testing method implemented by the testing system.

[0026] FIG. 2 is a pictorial diagram illustrating variables obtained by the testing system.

[0027] FIG. 3 illustrates testing results according to the aforementioned testing system and the testing method.

[0028] FIG. 4 illustrates testing results according to the aforementioned testing system and the testing method.

[0029] FIG. 5 illustrates testing results according to the aforementioned testing system and the testing method.

[0030] FIG. 6 illustrates testing results according to the aforementioned testing system and the testing method.

[0031] FIG. 7 illustrates testing results of predicted work rate in relation to power output for different subsets of young subjects.

[0032] FIG. 8 illustrates testing results of predicted work rate in relation to power output for different subsets of the COPD subjects.

[0033] FIG. 9 illustrates testing results of predicted work rate in relation to power output for different subsets of the CHF subjects.

[0034] FIG. 10 illustrates testing results of predicted work rate in relation to power output for different subsets of the COPD subjects

[0035] FIG. 11 illustrates a block diagram of an example computer system in which any of the embodiments described herein may be implemented.

[0036] FIGS. 12A and 12B illustrate example GUIs to display any results.DETAILED DESCRIPTION

[0037] A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology, in particular, to diagnosis and therapeutic treatment of neuromuscular deconditioning or a disease condition using a testing system. The testing system is configured to determine neuromuscular performance in health and various disease conditions, and therefore distinguishing among cardiovascular, pulmonary, and / or neuromuscular abnormalities in such conditions. Neuromuscular deconditioning refers to decreases in muscular strength and / or power, and / or onset of muscular fatigue, attributed to a deficiency in exercise capacity that normally occurs with disuse or inactivity. Neuromuscular deconditioning is often present in combination with, but may not be caused by, chronic disease. The testing system overcomes previous limitations including an inability to distinguish between neuromuscular deconditioning and various disease conditions that limit exercise. The testing system provides a technical benefit of more accurately and efficiently diagnosing whether an entity has neuromuscular deconditioning together or separately from a disease condition. As a result, more tailored and effective treatments are implemented depending on the accurate diagnosis.

[0038] Moreover, the testing system is an objective, non-invasive measure of integrated functions of the pulmonary, cardiovascular, and neuromuscular systems. The testing system evaluates submaximal and peak exercise responses, which may inform on causes of exerciseintolerance, dyspnea, fatigue, disease prognosis and / or progression, pre- and post-surgical risk stratification, or exercise training prescription. The testing system is reproducible and well tolerated by different entities such as elderly, and those with heart failure (e.g., chronic heart failure (CHF)), heart transplant or pulmonary diseases (e.g., chronic obstructive pulmonary disease (COPD)).

[0039] The testing system is associated with an exercise machine configured to, when activated and implemented on an entity (e.g., human), induce a change in an exercise state of the entity. The exercise state of the entity may pertain to a degree of exercise and / or exertion during the exercise, which may include different exercise control modes of the exercise machine (e.g., an isokinetic control modes separated by a hyperbolic control mode). Exertion may refer to an application of force, torque or power during exercise. In some embodiments, the exercise machine may induce the entity to change from an unexercised, or resting, state to an exercised state. An unexercised state may refer to the entity not having undergone an exercise protocol within a threshold previous period of time (e.g., within the last six hours, or 24 hours). An exercise protocol may refer to exercise satisfying at least a threshold degree of muscular and / or cardiovascular exertion.

[0040] In some embodiments, different exercise states may be associated with the different exercise modes of the exercise machine. Various exertion parameters or variables (hereinafter “variables”) are determined and processed by the testing system during the different modes and / or at different exercise states. In some embodiments, a peak voluntary isokinetic power (baseline PiSO) may be determined when the entity is at an unexercised state, which corresponds to an onset of a first isokinetic control mode occurring before the hyperbolic control mode. An end ramp power may be determined when the entity is at an intolerance state in the hyperbolic control mode, and a peak voluntary isokinetic power (end exercise Piso) which corresponds to an onset of a second isokinetic control mode following the hyperbolic control mode. In some embodiments, the intolerance state may refer to a limit of tolerance or a fatigued state. In some embodiments, the intolerance state may be based on a point in time at which heart rate, pulmonary oxygen uptake (VO?) or pulmonary ventilation have reached peak values. From the Piso measurements and the end ramp power, further variables may be determined. These variables may include baseline Piso, an aerobic power index (API) (e.g., an index of tolerance to enduranceexercise), a fatigue index (FI), and a power reserve (PR). The baseline PiSOmay be compared with the entity (over time) or with nomograms of normal values. The API is obtained from a ratio of the end ramp power to baseline Piso. The FI is obtained based on a difference between the end ramp power and the baseline PiSO. The difference is further divided by the end ramp power. The PR is a degree to which the entity is able to acutely, or instantaneously, increase the peak voluntary isokinetic power (end exercise P;so) when the entity is in the intolerance state.

[0041] An advantage of the API is that it is normalized across entities of different ages and different sexes. In other words, two different representative populations of different ages exhibit no statistically significant differences in their APIs. Additionally, two different representative populations of different sexes also exhibit no statistically significant differences in their APIs. Effectively, the API provides real-time normalization of exertional variables without additional data modification or computation. This results in more efficient and effective diagnosis and treatment of neuromuscular deconditioning regardless of demographic or anthropometric characteristics of the entity.

[0042] Furthermore, the testing system has been implemented or tested in different populations, such as a chronic obstructive pulmonary disease (COPD) population, a heart failure population, and populations of strength trained, and endurance trained athletes.

[0043] The foregoing figures further illustrate these concepts.

[0044] FIG. 1 is a diagram illustrating a testing system 100 and a corresponding testing method 150 implemented by the testing system. In FIG. 1, the testing system 100 is associated with an exercise machine, such as a cycle ergometer. When activated and implemented on an entity, the exercise machine may induce a change in an exercise state of the entity. When activated, a contacting surface of the exercise machine, such as a pedal of a cycle ergometer, may be programmed to move in a rotational or translational manner. Depending on a mode of the exercise machine, the contacting surface may move at a fixed speed or a variable speed, such as an increasing speed. The exercise machine may move at a fixed speed or cadence during a first isokinetic control mode and a second isokinetic control mode, and may move at a variable speed during a hyperbolic control mode. The hyperbolic control mode may implement ramp- incremental exercise. In some embodiments, in a cycle ergometer, the exercise machine mayrotate the cranks at between 50 and 150 revolutions per minute, or any subrange thereof, during the first isokinetic control mode and / or the second isokinetic control mode. In some embodiments, the exercise machine may rotate the cranks at between 70 and 80 revolutions per minute during the first isokinetic control mode and / or the second isokinetic control mode. A part of the entity, such as a foot, may continuously contact the contacting surface of the exercise machine while the contacting surface of the exercise machine is moving. Sensor data and / or signals associated with the entity and with the exercise machine may be captured and processed.

[0045] The testing system 100 includes sensors 102, which may include any or all of a flow measurement device 104, a gas analyzer device 106, and a force measurement device 108. The sensors 102 may be calibrated. For example, the flow measurement device 104 may be calibrated using a precalibrated syringe to simulate an expected physiologic range of ventilatory flow rates that occur from an unexercised state to an exercised state. The gas analyzer device 106 may be calibrated using two-point calibration, such as using two precision-analyzed gas mixtures. The flow measurement device 104 may capture and output signals indicative of volume of gas flow from the mouth and / or nose of the entity. The flow measurement device 104 may include any of a flow transducer, a pneumotachograph, a mass flow sensor, a pitot tube flow meter, or a turbine volume transducer. The gas analyzer device may capture and output signals indicative of gas concentration, such as an oxygen (O2) and / or a carbon dioxide (CO2) concentration of the gas flow. The gas analyzer device may include a mass spectrometer. In some embodiments, signals outputted from the flow measurement device 104 and the force measurement device 106 may be used to obtain variables such as the pulmonary oxygen uptake () or pulmonary ventilation.

[0046] The force measurement device 108 may be associated with the exercise machine. The force measurement device 108 may capture and output signals indicative of a force or a torque applied to the contacting surface at various points in time. The force measurement device 108 may include one or more force transducers, such as strain gauges within a crank assembly. The force measurement device 108 may capture signals indicative of the force or torque applied at an onset of the first isokinetic control mode and the force or torque applied at an onset of the second isokinetic control mode, following instantaneous switching from the hyperbolic control mode. The force measurement device 108 may capture signals over a given time duration, such as between one and ten seconds. The force measurement device 108 may capture signals at highfrequency, such as every two degrees of angular rotation, over a period of between one and ten revolutions.

[0047] The signals obtained by the sensors 102 may be routed to signal processing circuitry 112. In some embodiments, the signal processing circuitry 112 may include a converter 114, such as an analog to digital converter (ADC) to convert any analog signals into digital signals. The signal processing circuitry 112 may further include one or more filters 116 which may filter out any of the signals deemed to be outliers or signals that fail to satisfy some criteria such as a stability or variation criteria. For example, the filters 116 may filter out certain cycles in which a waveform representing the applied force or torque over time fails to conform to a given shape.

[0048] The processed signals may be routed to a computing system 122, which includes one or more hardware processors 124 configured to obtain variables from the processed signals. The variables include a peak voluntary isokinetic power (baseline P;so) when the entity is at an unexercised state, which corresponds to an onset of the first isokinetic control mode. The baseline Pjsomay be obtained by averaging the torque applied across a number of revolutions, such as between two and ten revolutions, and multiplying the averaged torque by the velocity of the contacting surface of the exercise machine. Other variables include an aerobic power index (API), a fatigue index (FI), and a power reserve (PR). The API and FI are obtained based on the baseline PiS0, and an end ramp power. The end ramp power is obtained when the entity is at an intolerance state, which corresponds to the power prior to the onset of the second isokinetic control mode following the hyperbolic control mode. The PR is obtained based on the end ramp power and end-exercise PiSO, for example, a difference between the end ramp power and the endexercise Piso. In some embodiments, the intolerance state may refer to a limit of tolerance or a fatigued state. In some embodiments, the intolerance state may be based on a point in time at which pulmonary oxygen uptake, VO2 or pulmonary ventilation is at a peak. The API is obtained from a ratio of the end ramp power to baseline Piso. The API describes an ability of the aerobic system to support the available isokinetic power (e.g., the baseline PiS0) during the ramp- incremental test. The FI is obtained based on a difference between the end ramp power and baseline P;so. The difference is further divided by the end ramp power. The FI describes reduction in PiSOper unit (c.g., watt) of ramp-incremental work rate. The PR is a degree to which the entity is able to acutely, or instantaneously, increase the end ramp power when the entity is inthe intolerance state., The PR describes the ability to instantaneously produce PiS0in excess of the power required for the exercise task at end exercise, providing a diagnosis of whether or not the neuromuscular system is limiting to exercise tolerance (versus the pulmonary or cardiovascular systems).

[0049] In some embodiments, a low baseline Piso, a high API, and / or a high FI indicate a high likelihood of neuromuscular deconditioning. In some embodiments, a high PR may indicate that another factor besides neuromuscular deconditioning is limiting exercise capacity. This factor may be cardiovascular or pulmonary in origin. In some embodiments, a combination of a low baseline Pisoand a high PR may indicate neuromuscular deconditioning.

[0050] The computing system 122 also includes one or more datastores 126 such as databases and / or caches configured to store and / or transmit data relating to any of the variables. In some embodiments, the computing system 122 may communicate with one or more other computing systems which include databases, for example, transmitting information regarding the variables for implementing one or more therapeutic treatments. This communication among different computing systems, or database systems, provides specialization among each database system, and more efficient delegation or allocation of computing resources (e.g., hard drive capacity and / or memory) among each database system. For example, one database system may include specialized processors that are configured to determine the aforementioned variables while a different database system may include specialized processors that are configured to implement one or more therapeutic treatments. In some examples, the datastores 126 may have a hierarchical tier structure, in which data fragments are stored in a certain tier depending on an importance level or frequency of access, modification, and / or utilization of that data fragment. For example, the computing system 122 may store or write a data fragment that is more frequently accessed, modified, or utilized in a higher tier and store a data fragment that is less frequently accessed, modified, or utilized in a lower tier. A data fragment may be transported from a lower to higher tier if that data fragment is accessed, modified, and / or utilized more frequently. A data fragment may be transported from a higher to a lower tier if that data fragment is accessed, modified, and / or utilized more frequently.

[0051] A corresponding method 150 includes a step 152 of obtaining sensor signals from the sensors 102, a step 162 of processing the sensor signals using signal processing circuitry 112, a step 172 of outputting variables 156 by the computing system 122, and a step 182 of implementing therapeutic treatments based on the outputted variables. In some embodiments, the step 182 may include diagnosing the entity based on the outputted variables. In some embodiments, the diagnosis may be performed by a different computing system following the transmission of the variables to the different computing system.

[0052] The diagnosis may indicate whether the entity has neuromuscular deconditioning or whether the entity has a disease such as a cardiovascular or pulmonary disease, a probability, and / or a degree of severity of the deconditioning or the disease, based at least in part on the obtained variables. For example, if the entity has a lower than normal baseline PiSO, that may indicate or suggest lack of neuromuscular conditioning, as exhibited by weak muscles. If the entity has a lower than normal API, that may indicate or suggest aerobic deconditioning. If the FI is high than normal, that may suggest aerobic deconditioning which increases fatigability.

[0053] In some embodiments, depending on the diagnosis and / or a certainty level of the diagnosis, the testing system 100 may be triggered to perform additional testing to obtain additional sensor data and / or additional variables in an effort to provide a more accurate diagnosis. The therapeutic treatments may include prescribing certain drug treatments in dosages depending upon the diagnosis and / or the severity of the diagnosis. The drug treatments may include, without limitation, ACE inhibitors, angiotensin-II antagonists, anti-arrhythmic medicines, anticoagulants, anti-platelet medicines, beta-blockers, calcium-channel blockers, cholesterol lowering medicines, digoxin, diuretics, anabolic drugs, pulmonary medications (short or long acting anti-muscarinic or beta-agonists) and / or nitrates. In some embodiments, the prescribed dosages of drug treatments may vary based on the severity and / or a confidence level of the diagnosis. For example, the prescribed dosages may include different dosage ranges depending on the severity level of the diagnosis. The prescribed dosages may include a first dosage range if the severity level of the diagnosis is within a low severity range, a second dosage range if the severity level of the diagnosis is within a medium severity range, and a third dosage range if the severity level of the diagnosis is within a high severity range. As an illustrative example, the first dosage range may be, for example, between 0.25 and 5 mg daily of ACEinhibitors for a low severity range, between 1 mg and 20 mg daily for a medium severity range, and / or between 5 mg and 50 mg daily for a high severity range. Dosage ranges may increase over time in some therapeutic treatments. Other dosage ranges may apply for other drugs.

[0054] The therapeutic treatments may include a surgery (e.g., a catheterization) or other procedure. In some scenarios, if the diagnosis includes neuromuscular deconditioning, the therapeutic treatments may include implementing an exercise training program, rather than a drug treatment or a surgery, if the diagnosis is likely to be neuromuscular deconditioning (e.g., has at least a threshold probability of being partially attributed to neuromuscular deconditioning). In some embodiments, the therapeutic treatments may include a combination of any of the aforementioned, such as a combination of an exercise training program and a drug treatment, if the diagnosis is both neuromuscular deconditioning and a disease condition. An exercise training program may be programmed to cause exertion of at least a threshold amount of power output over at least a threshold duration of time. The power output induced may be manifested as different modes, such as isokinetic and / or hyperbolic control modes.

[0055] Using the testing system 100, predictive models may be generated. These predictive models may describe normal reference values for different classifications of subjects. Threshold values of neuromuscular performance, including sensitivity and specificity, may be obtained to accurately diagnose neuromuscular deconditioning and disease. Other variables that influence exercise tolerance may be identified, such as peak VO2, lactate threshold, diastolic dysfunction, vascular reactivity, leg lean mass, leg strength and power, muscle oxidative capacity, and muscle biopsy morphology.

[0056] Using the testing system 100, the discriminative capability of variables associated with deconditioning may be determined by measuring sensitivity of the variables in response to exercise training in deconditioned subjects and specificity of the variables to discriminate between peripheral and central hemodynamic exercise limitations in patients with heart failure and preserved ejection fraction. The testing system will improve clinical decision-making and severity stratification across a wide range of chronic disease states where deconditioning is a common feature.

[0057] FIG. 2 is a diagram illustrating the obtained variables. A peak voluntary isokinetic power 202, represented as baseline Piso, measures peak power output when the entity is in an unexercised state. The peak voluntary isokinetic power 202 may be obtained as an average power across an average of a number of revolutions or cycles at a constant speed (e.g., 3 crank revolutions). Following the measurement of baseline Pjso, ramp incremental exercise during hyperbolic control mode is implemented. At an intolerance state in hyperbolic control mode, an end ramp power 204 is obtained. A second peak voluntary isokinetic power at end exercise is then measured in, represented as end exercise Piso208. Using the peak voluntary isokinetic power at baseline 202, the end ramp power 204, peak voluntary isokinetic power at end exercise Piso 208, other variables including the API, FI, and PR are obtained.

[0058] FIG. 3 illustrates testing results 300 according to the aforementioned testing system 100 and the method 150, demonstrating a proof of concept. In FIG. 3, results of corresponding variables across two representative populations including male and female subjects are tested. These variables include peak VGi 302, baseline Piso304 and API 306. API has been demonstrated to be normalized across male and female populations, meaning that API is an effective variable that does not need to be further normalized based on gender. Thus, API can be effectively used as part of diagnosis of neuromuscular deconditioning or disease regardless of gender. In FIG. 3 and other subsequent figures, although baseline Piso304 is referenced, end exercise PiS0may additionally or alternatively be measured.

[0059] FIG. 4 illustrates testing results 400 according to the aforementioned testing system 100 and the method 150. In FIG. 4, results of corresponding variables across two representative populations including young and old subjects are tested. These variables include peak VO2402, baseline P;so404 and API 406. API has been demonstrated to be normalized across young and old populations, meaning that API is an effective variable that does not need to be further normalized based on age. Thus, API can be effectively used as part of diagnosis of neuromuscular deconditioning or disease regardless of age.

[0060] FIG. 5 illustrates testing results 500 according to the aforementioned testing system 100 and the method 150. In FIG. 5, results of corresponding variables across two representative populations including lean and obese subjects are tested. These variables include percentage ofpredicted peak VO? 502, baseline PiS0504, API 506, FI 508, and PR 510. Using the testing system 100, impaired aerobic function that was previously unidentified according to ACCP / ATS criteria is now identified.

[0061] FIG. 6 illustrates testing results 600 according to the aforementioned testing system 100 and the method 150. In FIG. 6, results of corresponding variables across different populations, including young subjects, old subjects, subjects affected by chronic heart failure (CHF), and subjects affected by chronic obstructive pulmonary disease (COPD). These variables include percentage of baseline PiS0602, API 604, FI 606, and PR 608.

[0062] FIG. 7 illustrates testing results of predicted work rate in relation to power output for different subsets of young subjects, according to the baseline PiS0602 of FIG. 6.

[0063] FIG. 8 illustrates testing results of predicted work rate in relation to power output for different subsets of the COPD subjects, according to the API 604 of FIG. 6.

[0064] FIG. 9 illustrates testing results of predicted work rate in relation to power output for different subsets of the CHF subjects, according to the FI 606 of FIG. 6.

[0065] FIG. 10 illustrates testing results of predicted work rate in relation to power output for different subsets of the COPD subjects, according to the PR 608 of FIG. 6.HARDWARE IMPLEMENTATION

[0066] The techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include circuitry or digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, server computer systems, portable computer systems, handheld devices, networking devices orany other device or combination of devices that incorporate hard-wired and / or program logic to implement the techniques.

[0067] Computing device(s) are generally controlled and coordinated by operating system software. Operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, I / O services, and provide a user interface functionality, such as a graphical user interface (“GUI”), among other things.

[0068] In some embodiments, the GUI may be associated with the computing system 122. The GUI may display any or all of the aforementioned variables, such as baseline Piso, end exercise Piso, and / or other variables including the API, FI, and PR. In some embodiments, the GUI may display the diagram in FIG. 2 or a similar or condensed representation of the diagram in FIG. 2. For example, the GUI may be part of different computing devices such as a mobile phone, a laptop, a tablet, or any other computer or mobile device. Depending on a size (e.g., a screen size) of the GUI, the computing system 122 may determine a level of granularity of the display. For example, on a larger screen, the computing system 122 may display more details while on a smaller screen, the computing system 122 may selectively display fewer details within a condensed representation. In one example, on a larger screen, the computing system 122 may display a pictorial representation similar or same as the diagram of FIG. 2, as illustrated in an example GUI 1200 of FIG. 12A. However, on a smaller screen, the computing system 122 may display a condensed representation which removes a portion or all of the pictorial aspects in FIG. 2. For example, the computing system 122 may display only a list of variables without pictures, for example, in a list or table format, as illustrated in an example GUI 1250 of FIG. 12B. In this manner, the computing system 122 may adaptively display the most relevant or utilized data on different computing devices which have different screen sizes and / or resource (e.g., hard drive, memory) availability.

[0069] In some embodiments, the computing system 122 may be configured to display, on the UI, a menu listing or otherwise depicting one or more applications or modules (hereinafter “modules”), and configured to display, on the screen, a module summary that is accessible directly from the menu. The application summary displays a limited list of data that is to be expanded in additional detail, within the one or more modules. The data may be selectable tolaunch the respective module, and in the respective module, the data can be seen within the respective module in greater detail. The module summary may be displayed while the one or more modules are in an unlaunched state. For example, different modules may correspond to different variables (e.g., baseline PiSO, end exercise PiSO, API, FI, and PR). Any of the variables may be selectable, and trigger launching of a module that displays more detailed information and / or analysis of the variable. The application summary may include a listing of the variables, a summary of measured results of the variables, and / or a representation similar to FIG. 2, as shown in the GUI of FIGS. 12A and 12B. Each module may display more detailed information or analysis of each variable. For example, a first module may display more detailed analysis of baseline Pisoincluding evaluation or comparison of baseline Pisoto historical or other benchmark values of baseline Piso. Although not illustrated in FIGS. 12A and 12B, diagnoses and / or therapeutic treatments may also be shown in the GUIs and / or be shown in a module that may be launched.

[0070] FIG. 11 is a block diagram that illustrates a computer system 1100 upon which any of the embodiments described herein may be implemented. In some examples, the computer system 1100 may include a cloud-based or remote computing system. For example, the computer system 1100 may include a cluster of machines orchestrated as a parallel processing infrastructure. The computer system 1100 includes a bus 1102 or other communication mechanism for communicating information, one or more hardware processors 1104 coupled with bus 1102 for processing information. Hardware processor(s) 1104 may be, for example, one or more general purpose microprocessors.

[0071] The computer system 1100 also includes a main memory 1106, such as a random access memory (RAM), cache and / or other dynamic storage devices, coupled to bus 1102 for storing information and instructions to be executed by processor 1104. Main memory 1106 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1104. Such instructions, when stored in storage media accessible to processor 1104, render computer system 1100 into a special-purpose machine that is customized to perform the operations specified in the instructions.

[0072] The computer system 1100 further includes a read only memory (ROM) 1108 or other static storage device coupled to bus 1102 for storing static information and instructions for processor 1104. A storage device 1110, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled to bus 1102 for storing information and instructions.

[0073] The computer system 1100 may be coupled via bus 1102 to a display 1112, such as a cathode ray tube (CRT) or LCD display (or touch screen), for displaying information to a computer user. An input device 1114, including alphanumeric and other keys, is coupled to bus 1102 for communicating information and command selections to processor 1104. Another type of user input device is cursor control 1116, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 1104 and for controlling cursor movement on display 1112. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor.

[0074] The computing system 1100 may include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the computing device(s). This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

[0075] In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or fromthemselves, and / or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and may be originally stored in a compressed or installable format that requires installation, decompression or decryption prior to execution). Such software code may be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and / or may be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into submodules despite their physical organization or storage.

[0076] The computer system 1100 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and / or program logic which in combination with the computer system causes or programs computer system 1100 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 1100 in response to processor(s) 1104 executing one or more sequences of one or more instructions contained in main memory 1106. Such instructions may be read into main memory 1106 from another storage medium, such as storage device 1110. Execution of the sequences of instructions contained in main memory 1106 causes processor(s) 1104 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0077] The term “non-transitory media,” and similar terms, as used herein refers to any media that store data and / or instructions that cause a machine to operate in a specific fashion. Such non- transitory media may comprise non-volatile media and / or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 1110. Volatile media includes dynamic memory, such as main memory 1106. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, and networked versions of the same.

[0078] Non-transitory media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between non-transitory media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1102. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0079] Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 1104 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1100 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data earned in the infra-red signal and appropriate circuitry can place the data on bus 1 102. Bus 1 102 carries the data to main memory 1106, from which processor 1 104 retrieves and executes the instructions. The instructions received by main memory 1106 may retrieves and executes the instructions. The instructions received by main memory 1106 may optionally be stored on storage device 1110 either before or after execution by processor 1104.

[0080] The computer system 1100 also includes a communication interface 1118 coupled to bus 1102. Communication interface 1118 provides a two-way data communication coupling to one or more network links that are connected to one or more local networks. For example, communication interface 1118 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 1118 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicated with a WAN). Wireless links may also be implemented. In any such implementation, communication interface 1118 sends and receiveselectrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0081] A network link typically provides data communication through one or more networks to other data devices. For example, a network link may provide a connection through local network to a host computer or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet”. Local network and Internet both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link and through communication interface 1118, which carry the digital data to and from computer system 1100, are example forms of transmission media.

[0082] The computer system 1100 can send messages and receive data, including program code, through the network(s), network link and communication interface 1118. In the Internet example, a server might transmit a requested code for an application program through the Internet, the ISP, the local network and the communication interface 1118.

[0083] The received code may be executed by processor 1104 as it is received, and / or stored in storage device 1110, or other non-volatile storage for later execution.

[0084] Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The processes and algorithms may be implemented partially or wholly in application- specific circuitry.

[0085] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented asseparate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0086] Although an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or concept if more than one is, in fact, disclosed.

[0087] The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

[0088] As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, and data stores are somewhat arbitrary, and particular operations arc illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

[0089] Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include,certain features, elements and / or steps. Thus, such conditional language is not generally intended to imply that features, elements and / or steps arc in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and / or steps are included or are to be performed in any particular embodiment.

[0090] Unless the context requires otherwise, throughout the present specification and claims, the word “comprise” and variations thereof, such as, “comprises” and “comprising” are to be construed in an open, inclusive sense, that is as “including, but not limited to.” Recitation of numeric ranges of values throughout the specification is intended to serve as a shorthand notation of referring individually to each separate value falling within the range inclusive of the values defining the range, and each separate value is incorporated in the specification as it were individually recited herein. References to “approximately” may be construed to encompass values within a certain range of the specified value, such as within 25 percent, 10 percent, 5 percent, 1 percent, 0.5 percent, 0.25 percent, 0.1 percent, or any other applicable value. In other embodiments, “approximately” may refer to a value or entity being within a design tolerance to achieve an objective or result.

[0091] Additionally, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. The phrases “at least one of,” “at least one selected from the group of,” or “at least one selected from the group consisting of,” and the like are to be interpreted in the disjunctive (e.g., not to be interpreted as at least one of A and at least one of B).

[0092] Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

Claims

CLAIMS:1 . A testing system associated with an exercise machine, the testing system configured to diagnose neuromuscular deconditioning or disease of an entity, the testing system comprising: sensors comprising a flow measurement device, a gas analyzer device, and a force measurement device, the sensors configured to, upon activation of the exercise machine and a force or torque being applied by the entity onto a contacting surface of the exercise machine, capture sensor data indicative of a pulmonary oxygen uptake VO2 and the force or torque; sensor processing circuitry configured to obtain the captured sensor data and to process the captured sensor data; and a computing system comprising one or more processors configured to: obtain the processed sensor data; determine variables indicative of deconditioning or disease associated with the entity based on the processed sensor data, the variables comprising a peak voluntary isokinetic power PiSo, an aerobic power index (API), a fatigue index (FI), and a power reserve (PR); and perform a diagnosis of deconditioning or disease based on the determined variables.

2. The testing system according to claim 1, wherein the baseline P;sois determined when the entity is in an unexercised state.

3. The testing system according to claim 1, wherein the one or more processors are further configured to obtain an end ramp power in hyperbolic control mode and end-exercise Pisoin isokinetic control mode when the entity is in an intolerance state, wherein the intolerance state is based on a point in time at which the entity is unable to continue exercising, where pulmonary oxygen uptake, VO2, pulmonary ventilation or heart rate has reached a peak.

4. The testing system according to claim 3, wherein the obtaining of the API and the FI is based on the Pisoand the end ramp power.

5. The testing system according to claim 4, wherein the API is obtained based on a ratio of the end ramp power to Piso.

6. The testing system according to claim 4, wherein the FI is obtained based on a difference between the end ramp power and Pisoand normalized by the end ramp power.

7. The testing system according to claim 1, wherein the PR is obtained based on the difference between the end ramp power and the end exercise Piso.

8. The testing system according to claim 1, one or more processors are configured to implement a therapeutic treatment in response to the diagnosis.

9. The testing system according to claim 8, wherein the therapeutic treatment comprises selectively implementing a surgery based on the diagnosis.

10. The testing system according to claim 8, wherein the therapeutic treatment comprises selectively providing a drug treatment at a determined dosage based on the diagnosis.

11. The testing system according to claim 1, wherein the one or more processors are further configured to perform selectively obtaining additional sensor data based on a diagnosis and a certainty level of the diagnosis.

12. A method of diagnosing neuromuscular deconditioning or disease of an entity, the method associated with an exercise machine and comprising: upon activation of the exercise machine and a force or torque being applied by the entity onto a contacting surface of the exercise machine, capturing, from sensors, sensor data, the sensor data indicative of a pulmonary oxygen uptake VO2 and the force or torque: obtaining, using sensor processing circuitry, the captured sensor data; processing, using the sensor processing circuitry, the obtained sensor data; obtaining, using a computing system comprising one or more processors, the processed sensor data;determining, using the computing system, variables indicative of deconditioning or disease associated with the entity based on the processed sensor data, the variables comprising a peak voluntary isokinetic power P;so, an aerobic power index (API), a fatigue index (FI), and a power reserve (PR); and perform a diagnosis of deconditioning or disease based on the determined variables.

13. The method of claim 12, wherein the obtaining of the API and the FI is based on the PiSo and the end ramp power.

14. The testing system according to claim 12, wherein the PR is obtained based on the difference between the end ramp power and the end exercise Piso.

15. The method of claim 12, further comprising obtaining an end ramp power when the entity is in an intolerance state, wherein the entity is unable to continue exercising, where pulmonary oxygen uptake, V7Oi has stopped increasing.

16. The method of claim 15, wherein the obtaining of the API and the FI is based on the Piso and the end ramp power.

17. The method of claim 16, wherein the API is obtained based on a ratio of the end ramp power to Piso.

18. The method of claim 16, wherein the FI is obtained based on a difference between the end ramp power and Pisoand normalized by the end ramp power.

19. The testing system according to claim 16, wherein the PR is obtained based on the difference between the end ramp power and the end exercise Piso.

20. The method of claim 12, further comprising implementing a therapeutic treatment in response to the diagnosis.

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