Methods and systems for reducing the likelihood of repetitive stress damage to soft tissue

Abstract

The present application relates to methods and systems for reducing the likelihood of repetitive stress damage to soft tissue. Techniques for reducing repetitive stress damage to soft tissue in performing a treatment are disclosed. These techniques include: obtaining at least one repetitive stress data set associated with the soft tissue and the treatment; accessing information characterizing a first damage state and second information characterizing a second damage state, wherein the first information quantifies the number of repetitions that the soft tissue transitions out of the first damage state at a given stress, and wherein the second information quantifies the number of repetitions that the soft tissue transitions out of the second damage state at a given stress; predicting conditions sufficient to damage the soft tissue; determining at least one guideline for reducing the risk of repetitive stress damage to the soft tissue material based at least on the prediction; and implementing at least one guideline in the treatment.

Description

[0001] Related Applications

[0002] This application claims priority to and the benefit of U.S. Utility Patent Application No. 17 / 110,953, filed on December 3, 2020, entitled “Soft Tissue Material Cumulative Damage Model for Reducing Repetitive Stress Injuries in Performing a Process,” which claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 944,799, filed on December 6, 2019, entitled “Systems and Methods for Developing a Tendon Material Cumulative Damage Model,” the disclosures of which are fully incorporated herein by application. Technical Field

[0003] The subject matter described herein relates generally to the field of materials science and its application in ergonomics. More specifically, the subject matter disclosed herein relates to repetitive stress damage to soft tissue materials and guidelines for minimizing the risk of sustaining such damage. Background Art

[0004] In particular, overuse injuries of the shoulder (including the supraspinatus tendon) are some of the most significant ergonomic injuries. Therefore, there is a need to understand the dynamics of the workplace or other activities that contribute to and may cause, for example, stress on the tendon to address the tasks or repetitions of tasks that contribute to the injury.

[0005] To build this understanding, information is drawn from multiple research fields, including medical devices, surgical technology, medicine, engineering, materials science, and ergonomics. Furthermore, these research fields do not currently intersect in a way that models and guidelines for repetitive stress damage to materials can be easily created (or have been developed). This is evidenced by the body of research and its focus. For example, medical, therapeutic, and pharmacological research is devoted to (or focused on) individuals after an injury has occurred: surgical procedures, physical therapy protocols, and treatments that enhance recovery. Damage detection focuses on screening for risk after a patient self-reports an injury (rather than before an injury). While traditional ergonomic practices aim to prevent injury, this has only been done, or so far, at a macro level using epidemiological methods: estimates of when a person will self-report an impairment based on discomfort level or pain are created based on estimated work exposures, guidelines are created below that threshold, and most impairments are defined by broad body regions (whole shoulder, knee, or back) rather than individual sites. Engineering publications from the 1990s onwards have shown that ex vivo tendon materials experience strength reductions after repetitive stress, consistent with principles of structural fatigue. These work efforts do not report on the utility of this information in creating models or in preventing or predicting injury. They neither explore the significant structural and performance differences between tendons and non-biological structures, nor do they identify (or develop) engineered materials that can be used as substitutes for further testing, replacing tendons that are difficult to obtain and subject to change and degradation once removed from living tissue. A new approach is needed that links multidisciplinary research together, fills in gaps, and exploits evolving technologies in multiple fields. Significant advances in computer technology have made the creation of material models more attractive. Computational material models can simulate the fatigue behavior of existing or potential new materials. Ultrasonic probing can estimate the material acoustic properties of biomaterials within biological subjects, which can be correlated to certain mechanical properties (such as elastic modulus). Motion tracking technology can be used to observe people at work and simulate these movements within a digital body. These technologies represent an opportunity to develop a model that can generate guidelines for preventing soft tissue damage in the workplace. The model can also take into account personal characteristics to create a personalized model. Working models can also be used in reverse: to inform the design requirements for building artificial or replacement tendons, and how these tendons perform throughout the motion process. Summary of the invention

[0006] This disclosure includes examples according to the following clauses.

[0007] Item 1. A method for reducing the likelihood of repetitive stress damage to soft tissue while performing a treatment, the method comprising: obtaining at least one repetitive stress data set associated with the soft tissue and the treatment; accessing at least first information characterizing a first damage regime and second information characterizing a second damage regime, wherein the first information quantifies the number of repetitions for the soft tissue to transition out of the first damage regime at a given stress, and wherein the second information quantifies the number of repetitions for the soft tissue to transition out of the second damage regime at a given stress; predicting conditions sufficient to damage the soft tissue based at least on the first information, the second information, and the repetitive stress data set; determining at least one guideline for reducing the risk of repetitive stress damage to soft tissue materials based at least on the prediction; and implementing at least one guideline in the treatment.

[0008] Clause 2. The method of clause 1, wherein the first damage state and the second damage state each comprise one of the following: an undamaged state, a sub-rupture damage state, or a tear extension state.

[0009] Clause 3. A method according to any one of clauses 1 or 2, wherein the method further comprises: accessing third information characterizing a third damage state, wherein the third information quantifies the number of repetitions of the soft tissue transitioning out of the third damage state under a given stress; wherein the first damage state comprises an undamaged state, the second damage state comprises a sub-rupture damage state, and the third damage state comprises a tear extension state.

[0010] Clause 4. A method according to any one of clauses 1, 2 or 3, wherein the repetitive stress data set includes at least one of a number of repetitions or a duration of at least one task of the treatment, and a force on a cross-sectional area of ​​the soft tissue.

[0011] Clause 5. The method of any one of clauses 1 to 4, wherein obtaining the at least one repetitive stress data set associated with the soft tissue and the treatment comprises estimating at least one stress distribution in the soft tissue.

[0012] Clause 6. A method according to any one of clauses 1 to 5, wherein the at least one guideline includes a limitation on at least one of: the posture of the soft tissue, the number of repetitions of a given movement of the soft tissue, the force applied to the soft tissue, the duration of maintaining a given posture of the soft tissue, the duration of repetitions of a given movement of the soft tissue, or the duration of a given force applied to the soft tissue.

[0013] Clause 7. A method according to any one of clauses 1 to 6, wherein accessing at least first information characterizing a first damage state and second information characterizing a second damage state comprises obtaining at least one of the following items: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling (MDM) data, or publication data.

[0014] Clause 8. A method according to any one of clauses 1 to 7, wherein the soft tissue comprises the teres minor tendon, the infraspinatus tendon, the supraspinatus tendon, the subscapularis tendon, the deltoid tendon, the biceps tendon, the triceps tendon, the brachioradialis tendon, the supinator tendon, the radial flexor carpi tendon, the ulnar flexor carpi tendon, the radial extensor carpi tendon, the radial extensor carpi brevis tendon, the iliopsoas tendon, the obturator internus tendon, the adductor longus tendon, the adductor brevis tendon, the adductor maximus tendon, the gluteus maximus tendon, the gluteus medius tendon, the quadriceps tendon, the patellar tendon, the hamstring tendon, the sartorius tendon, the gastrocnemius tendon, the Achilles tendon, the soleus tendon, the tibialis Anterior tendon of the tibialis, peroneus longus tendon, flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, opposition hallucis tendon, flexor hallucis longus tendon, extensor tendon, abductor hallucis tendon, flexor hallucis longus tendon, flexor digitorum brevis tendon, masseter tendon, abductor hallucis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, eye muscles tendon, levator palpebrae tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenic capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transverse abdominis tendon, latissimus dorsi tendon or erector spinae tendon.

[0015] Clause 9. The method of any one of clauses 1 to 8, further comprising personalizing at least one guideline for the subject by applying one or more demographic variables of the subject to the at least one guideline.

[0016] Clause 10. The method according to any one of clauses 1 to 9, further comprising: obtaining one or more usage data sets of the soft tissue of the subject; and estimating damage to the soft tissue of the subject by comparing the usage data sets with the at least one guideline.

[0017] Item 11. A computer system for reducing repetitive stress damage to soft tissue in performing a treatment, the system comprising at least one electronic processor that executes instructions to perform operations, the operations comprising: obtaining at least one repetitive stress data set associated with the soft tissue and the treatment; accessing at least first information characterizing a first injury state and second information characterizing a second injury state, wherein the first information quantifies the number of repetitions for the soft tissue to transition out of the first injury state under a given stress, and wherein the second information quantifies the number of repetitions for the soft tissue to transition out of the second injury state under a given stress; predicting conditions sufficient to damage the soft tissue based at least on the first information, the second information, and the repetitive stress data set; and determining at least one guideline for reducing the risk of repetitive stress damage to soft tissue materials based at least on the prediction; thereby implementing the at least one guideline in the treatment.

[0018] Clause 12. The system of clause 11, wherein the first damage state and the second damage state each comprise one of: an undamaged state, a sub-rupture damage state, or a tear-extension state.

[0019] Clause 13. A system according to clause 11 or 12, wherein the operation further comprises: accessing third information characterizing a third damage state, wherein the third information quantifies the number of repetitions of the soft tissue transitioning out of the third damage state under a given stress; wherein the first damage state comprises a no damage state, the second damage state comprises a sub-rupture damage state, and the third damage state comprises a tear extension state.

[0020] Clause 14. A system according to any one of clauses 11, 12 or 13, wherein the repetitive stress data set includes at least one of a number of repetitions or a duration of at least one task of the process, and a force on a cross-sectional area of ​​the soft tissue.

[0021] Clause 15. The system of any one of Clauses 11 to 14, wherein obtaining the at least one repetitive stress data set associated with the soft tissue and the treatment comprises estimating at least one stress distribution in the soft tissue.

[0022] Clause 16. A system according to any one of clauses 11 to 15, wherein the at least one guideline includes a limitation on at least one of: the posture of the soft tissue, the number of repetitions of a given movement of the soft tissue, the force applied to the soft tissue, the duration of maintaining a given posture of the soft tissue, the duration of repetitions of a given movement of the soft tissue, or the duration of a given force applied to the soft tissue.

[0023] Clause 17. A system according to any one of clauses 11 to 16, wherein accessing at least first information characterizing a first injury state and second information characterizing a second injury state comprises obtaining at least one of the following: ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling (MDM) data, or publication data.

[0024] Clause 18. A system according to any one of clauses 11 to 17, wherein the soft tissue comprises the teres minor tendon, the infraspinatus tendon, the supraspinatus tendon, the subscapularis tendon, the deltoid tendon, the biceps tendon, the triceps tendon, the brachioradialis tendon, the supinator tendon, the radial flexor carpi tendon, the ulnar flexor carpi tendon, the radial extensor carpi tendon, the radial extensor carpi brevis tendon, the iliopsoas tendon, the obturator internus tendon, the adductor longus tendon, the adductor brevis tendon, the adductor magnus tendon, the gluteus maximus tendon, the gluteus medius tendon, the quadriceps tendon, the patellar tendon, the hamstring tendon, the sartorius tendon, the gastrocnemius tendon, the Achilles tendon, and the soleus tendon. , tibialis anterior tendon, peroneus longus tendon, flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, hallucis contortus tendon, flexor hallucis longus tendon, extensor tendon, abductor hallucis tendon, flexor hallucis longus tendon, flexor digitorum brevis tendon, masseter tendon, abductor hallucis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, eye muscles tendon, levator palpebrae tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenic capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transverse abdominis tendon, latissimus dorsi tendon or erector spinae tendon.

[0025] Clause 19. The system of any one of clauses 11 to 18, wherein the operations further comprise personalizing at least one guideline for the subject by applying one or more demographic variables of the subject to the at least one guideline.

[0026] Clause 20. A system according to any one of clauses 11 to 19, wherein the operation further comprises: obtaining one or more usage data sets of the subject's soft tissue; and estimating damage to the subject's soft tissue by comparing the usage data sets with at least one guideline.

[0027] The present disclosure relates to systems and related methods for generating tendon injury models. Generally, these systems and related methods relate to generating guidelines for reducing or minimizing repetitive damage to tendon materials.

[0028] In one aspect, the present disclosure provides a method for generating a tendon injury model (e.g., a tendon injury accumulation model, etc.). The method includes generating one or more SN curves for one or more physical states of at least one tendon from one or more SN curve data sets, wherein a given SN curve includes a graph of the magnitude of stress applied to the tendon versus the number of repetitions of tendon failure. The method also includes generating one or more repetitive stress data sets describing the tendon, and combining the SN curve or data derived therefrom with the repetitive stress data set to predict damage to the tendon under one or more conditions (e.g., a physical or tangible representation or information of tendon damage under a set of given conditions), thereby generating a tendon injury model. However, it should be noted that the physical mechanism by which damage accumulates in biomaterials (such as collagen-based tendons) is very different from the physical mechanism of traditional crystal structure materials (SN type behavior models are based on this mechanism). The common model of the latter is damage accumulation caused by dislocation accumulation at sliding obstacles in (usually) metal grains or crystals. As the number of cycles increases, dislocations are generated at discontinuities in the microstructure of the structural material and gradually accumulate onto obstacles, and even if the macro stress is far below the material yield strength, continued cyclic loading will eventually cause small cracks to appear at the accumulation location. This will then manifest as a material defect and eventually develop into a crack. Although prior art methods have attempted to model fatigue within soft tissues, such attempts will produce inaccurate results because they treat soft tissues as having the fatigue properties of metals. However, despite the observation of SN type behavior, the fatigue and crack mechanisms of metals have no real analogue in collagen-based tendons subjected to similar cyclic loading. This is because there are no dislocations in the tendon that adapt to local plastic deformation. Instead, the tendon adapts to cyclic loading through local stretching and kinking of collagen fibers, which can be considered a form of microscopic damage. This damage can be repaired by the body through the reorganization of collagen, but it is time-based and undergoes a healing process in the body. Thus, an effective model of damage accumulation in tendon structures includes: a) a representation of the accumulation of microscopic damage prior to a detectable defect (or tear); b) a representation of the healing process that counteracts said microscopic damage at competing rates; c) a representation of the connection and extension of microscopic damage to produce a detectable macroscopic defect in the tendon, usually represented by a tear or crack in the collagen structure, which may also be accompanied by pain; d) a representation of the extension and growth of this tear or crack over time (da / dn) if continuous cyclic loading is applied above a critical threshold stress intensity Kc; and e) a representation of the total load cycles leading to catastrophic separation or effective failure of the tendon carrying the service load. The latter e) is a common model for SN fatigue type data.The model components for each of the above processes will identify four (4) states of damage accumulation: 1) no damage; 2) accumulation of microscopic damage (sub-rupture); 3) accumulation of damage in the form of growing tears or cracks, cell matrix damage, or other biological damage; and 4) catastrophic failure or separation of the tendon structure. These model components can be represented mathematically and integrated into the collective model. This collective model is also validated by inspection or interrogation methods that can identify the presence and extent of advancing microscopic damage and tears.

[0029] On the other hand, the present disclosure provides a method for generating a guideline for avoiding repetitive stress of tendon materials and / or accumulation of tendon damage. The method includes generating one or more SN curves for one or more physical states of at least one tendon from one or more SN curve data sets, wherein a given SN curve includes a graph of the stress magnitude applied to the tendon for the number of repetitions in order to achieve the transition of the damage state of the tendon. The method also includes generating one or more repetitive stress data sets describing the tendon, and combining the SN curve or data derived therefrom with the repetitive stress data set to predict damage to the tendon under one or more conditions to generate a tendon injury model. In addition, the method also includes generating at least one guideline for repetitive stress of the tendon material of the tendon from the tendon injury model, wherein the guideline includes the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, the duration of a given force applied to the tendon, and a combination thereof, thereby generating a tendon injury model, which is used to obtain at least one guideline.

[0030] The method of the present disclosure includes multiple aspects. In some aspects, for example, the method includes combining multiple SN curves of physical states to produce at least one combined SN curve. In some aspects, the method includes applying at least one cumulative damage model when combining the SN curve or the data derived therefrom with a repetitive stress data set to predict damage to the tendon under one or more conditions. In some aspects, the method includes obtaining an SN curve data set using one or more data sources including medical diagnostic technology, such as ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive test data, cadaver materials, animal materials, polymer substitute materials, molecular dynamics modeling (MDM) data, publication data and combinations thereof.

[0031] In some aspects, the tendon comprises the supraspinatus tendon. In some aspects, the tendon comprises the teres minor tendon, the infraspinatus tendon, the supraspinatus tendon, the subscapularis tendon, the deltoid tendon, the biceps tendon, the triceps tendon, the brachioradialis tendon, the supinator tendon, the radial flexor carpi tendon, the ulnar flexor carpi tendon, the radial extensor carpi tendon, the radial extensor carpi brevis tendon, the iliopsoas tendon, the obturator internus tendon, the adductor longus tendon, the adductor brevis tendon, the adductor maximus tendon, the gluteus maximus tendon, the gluteus medius tendon, the quadriceps tendon, the patellar tendon, the hamstring tendon, the sartorius tendon, the gastrocnemius tendon, the Achilles tendon, the soleus tendon, the tibialis anterior tendon, the peroneus longus tendon, the toe The present invention relates to the present invention to the present invention. The present invention relates to the present invention ...

[0032] In some aspects, the method includes combining the SN curve or data derived therefrom and the repetitive stress data set with the healing data to predict damage to the tendon under one or more conditions. In some aspects, the present disclosure provides a method for predicting tendon damage in a subject, the method comprising: obtaining one or more usage data sets of at least one tendon of the subject, and comparing the usage data sets with the guide of repetitive stress of the tendon material generated by the method, thereby predicting tendon damage in the subject. In some aspects, the physical state includes a non-damaged state, a sub-rupture damage state, a crack initiation state, a fracture state or curve, and combinations thereof. In some aspects, one or more steps are at least partially implemented by a computer.

[0033] In some aspects, the method includes generating at least one guideline for repetitive stress of tendon material of a tendon from a tendon injury model, wherein the guideline includes the posture and / or position of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof. In some of these aspects, the guideline for repetitive stress of tendon material includes one or more recommended use / rest cycles of the tendon under one or more sets of use conditions. In some of these aspects, the method includes verifying the guideline for repetitive stress of tendon material. In some of these aspects, the method includes personalizing the guideline for a given subject by applying one or more demographic variables of the subject to the guideline. In some of these aspects, the method includes using task information when generating the guideline. In some of these aspects, the task information includes tool weight and / or force vector.

[0034] In some aspects, the method includes estimating at least one stress distribution in the tendon to generate a repetitive stress data set. In some of these aspects, the method includes estimating the stress distribution in the tendon using at least one dimension of the tendon. In some of these aspects, the dimension includes at least one cross-sectional area of ​​the tendon. In some of these aspects, the method includes estimating the stress distribution in the tendon using at least one cyclic curve, the cyclic curve including a graph of at least one force applied to the tendon versus the number of repetitions of the tendon failure. In some of these aspects, the force is determined in one or more postures of the tendon. In some of these aspects, the method includes determining the force using estimation and / or modeling techniques, such as finite element modeling (FEM) and / or electromyography (EMG). In some of these aspects, the method includes using task information when determining the force applied in multiple postures of the tendon. In some aspects, the task information includes tool weight and / or force vector.

[0035] In other aspects, the present disclosure provides a system comprising a controller that includes or is capable of accessing a computer-readable medium that includes non-transitory computer-executable instructions that, when executed by at least one electronic processor, at least perform: generating one or more SN curves of one or more physical states of at least one tendon from one or more SN curve data sets, wherein a given SN curve includes a graph of the magnitude of stress applied to the tendon versus the number of repetitions of tendon failure, and combining the SN curve or data derived therefrom with one or more repetitive stress data sets to generate a tendon injury model.

[0036] On the other hand, the present disclosure provides a computer-readable medium comprising non-transitory computer-executable instructions which, when executed by at least one electronic processor, perform at least: generating one or more SN curves for one or more physical states of at least one tendon from one or more SN curve data sets, wherein a given SN curve comprises a graph of the magnitude of stress applied to the tendon versus the number of repetitions of tendon failure, and combining the SN curve or data derived therefrom with one or more repetitive stress data sets to generate a tendon injury model.

[0037] In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: combining multiple SN curves for physical states to generate at least one combined SN curve. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: when combining the SN curve or data derived therefrom with a repetitive stress data set, applying at least one cumulative damage model to predict damage to the tendon under one or more conditions. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: obtaining an SN curve data set using one or more data sources including medical diagnostic technology, such as ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive test data, cadaveric materials, animal materials, polymer substitute materials, molecular dynamics modeling (MDM) data, publication data, and combinations thereof. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: combining the SN curve or data derived therefrom, and the repetitive stress data set with healing data to predict damage to the tendon under one or more conditions. In certain aspects, the instructions of the system or computer-readable medium disclosed herein further perform at least: obtaining one or more usage data sets for at least one tendon of a subject, and comparing the usage data sets to guidelines for repetitive stress of tendon materials to predict tendon injury in the subject.

[0038] In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: generating at least one guideline for repetitive stress of tendon material for tendon from a tendon injury model, wherein the guideline includes the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: verifying the guideline for repetitive stress of tendon material. In some of these aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: personalizing the guideline for a given subject by applying one or more demographic variables of the subject to the guideline. In some of these aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: using task information when generating the guideline. In some of these aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: estimating at least one stress distribution in the tendon to generate a repetitive stress data set. In some of these aspects, the instructions of the system or computer readable medium disclosed herein further perform at least: estimating a stress distribution in the tendon using at least one dimension of the tendon. In some of these aspects, the instructions of the system or computer readable medium disclosed herein further perform at least: estimating a stress distribution in the tendon using at least one cyclic curve including a graph of at least one force applied to the tendon versus a number of repetitions of tendon failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or other aspects and advantages will become more apparent and more easily understood from the following detailed description of examples in conjunction with the accompanying drawings, in which:

[0040] Figure 1 is a flow chart schematically illustrating exemplary method steps according to some aspects disclosed herein;

[0041] Figure 2 is a schematic diagram showing exemplary method steps according to some aspects disclosed herein;

[0042] Figure 3 is a schematic diagram showing exemplary method steps according to some aspects disclosed herein;

[0043] Figure 4 is a flow chart illustrating a method of providing at least one guideline to reduce risk of repetitive stress damage to a tendon according to various examples; and

[0044] Figure 5 is a schematic diagram of an exemplary system suitable for use with certain aspects disclosed herein.

[0045] Detailed description

[0046] Exemplary aspects will now be described more fully with reference to the accompanying drawings. However, examples of the present disclosure may be embodied in many different forms and should not be construed as limited to the examples set forth herein. Rather, these examples are provided to make the present disclosure thorough and complete and to fully convey its scope to those skilled in the art. In the accompanying drawings, some details may be simplified and / or drawn to facilitate understanding rather than to maintain strict structural accuracy, details, and / or proportions.

[0047] It should be understood that when an element is referred to as being "on another component," "associated with another component," "connected to another component," "electrically connected to another component," or "coupled to another component," it can be directly on, associated with, connected to, electrically connected to, or coupled to another component, or there can be intervening components. In contrast, when a component is referred to as being "directly on another component," "directly connected to another component," "directly electrically connected to another component," or "directly coupled to another component," there are no intervening components. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0048] It should be understood that although the terms first, second, etc. can be used to describe various elements, components and / or directions in this article, these elements, components and / or directions should not be limited by these terms. These terms are only used to distinguish one element, component and / or direction from another element, component and / or direction. For example, a first element, component or direction can be referred to as a second element, component or direction without departing from the teaching of the example.

[0049] For ease of description, spatially relative terms such as "below," "beneath," "below," "above," "upper," etc. may be used herein to describe the relationship of one component and / or feature to another component and / or feature, or other components and / or features, as shown in the figures. It should be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0050] As used herein, a given "component" and a corresponding "component connector" refers to at least two components that are structured or otherwise operable to engage, operably connect, or otherwise associate with each other. In some aspects, a component is structured or otherwise operable to engage, operably connect, or otherwise associate with multiple component connectors. In some aspects, a component connector is structured or otherwise operable to engage, operably connect, or otherwise associate with multiple components.

[0051] As used herein, "subject" refers to an animal, such as a mammalian species (e.g., human) or an avian (e.g., bird) species or a non-mammalian species (e.g., fish, molluscs, reptiles, amphibians, etc.). More specifically, the subject can be a vertebrate, e.g., a mammal, such as a mouse, a primate, an ape, or a human. Animals include farm animals (e.g., production cattle, dairy cows, poultry, horses, pigs, etc.), sports animals, and companion animals (e.g., pets or support animals). The subject can be a healthy individual, an individual suffering from or suspected of having a disease or disease predisposition, or an individual in need of treatment or suspected of needing treatment.

[0052] The terms used herein are only for the purpose of describing specific examples and are not intended to limit the examples. As used herein, the singular forms "a", "an" and "the" are intended to also include the plural forms, unless the context clearly indicates otherwise. It will be further understood that when used in this specification, the terms "include" and / or "comprise" specify the presence of the features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0053] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the examples belong. It will be further understood that terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0054] I. Introduction

[0055] The present disclosure relates to methods, systems, computer-readable media, and related models for determining and predicting tendon usage based in certain aspects on material science principles such as forces (e.g., in different postures), stress distributions, stress data, and material (tendon) performance parameters (e.g., healthy state, healing, cumulative damage, and being injured). The present disclosure identifies useful correlations between material science principles and tendons. In some applications, predictive models are used to solve task-related ergonomic problems to inform and establish guidelines to prevent or otherwise reduce the possibility of injury. Although much of the present disclosure focuses on shoulder injuries and the supraspinatus tendon, the methods and related aspects disclosed herein may also be applied to essentially any soft tissue (such as intervertebral (vertebral) discs of interest, ligaments, tendons, and tendon systems) and in bionic applications (such as, in the design of artificial tendons).

[0056] As a further introduction, using the shoulder to perform work in the workplace is a challenge in extreme work environments. Shoulder injuries also frequently occur outside of the workplace, such as at home and while participating in sports or other human activities. Current guidelines do not provide clear acceptable limits for shoulder-based work activities in the workplace and do not consider the interaction of posture, force, and repetition, nor the interaction of posture, force, repetition, duration, and vibration. Practitioners who cannot simply eliminate shoulder movement face multiple degrees of unknown risk. Therefore, robust threshold limit guidelines for shoulder joint demands are needed to address the complex nature of upper extremity work, including the interaction of force, repetition and posture and work / rest cycles. These guidelines are very helpful to industrial engineers and ergonomics specialists, but also to physicians, physical therapists, sports medicine practitioners, and athletic trainers, etc.

[0057] i. Occupational and non-occupational shoulder diseases are significant

[0058] It has been well documented that shoulder disorders are common and frequent in extreme workplaces such as heavy manufacturing, welding, fishing, meat processing, heavy machining, auto repair, and painting. According to the U.S. Bureau of Labor Statistics in 2013, cumulative trauma to the shoulder accounts for 15% of all workplace musculoskeletal injuries, second only to lower back and general back injuries; however, shoulder injuries tend to be more severe and result in more time lost. A typical shoulder injury results in 21 days of lost work, twice as many as other musculoskeletal injuries at 11 days, and three times as many as back injuries at 7 days of lost work. Every day an employee loses has a profound impact not only on the employee and his or her family, but also on production schedules, product quality, and colleagues who perform extra work when team members are absent and injured.

[0059] ii. Shoulder function

[0060] The shoulder mechanism allows for the placement, function, and control of the hand, which is the most useful part of the human body for manual labor or tool-intensive assembly. The hand (and therefore the arm and shoulder) moves to weld, paint, drill, cut, gut a fish, or handle materials. The tool, hand, and shoulder system are often located overhead or in hard-to-reach places. The shoulder complex must support the weight of the arm, any tools used for the job, and the forces applied. Most manual work involving tools involves tasks that are repeated many times during the job.

[0061] A neutral shoulder posture is when the upper arm is straight and hanging beside the body. Every time the shoulder moves from its neutral position, tension is placed on the tendon, creating stress within the tendon. Every time the shoulder moves from a neutral position, forces and repetitions are created, subjecting the tendon to cumulative fatigue damage.

[0062] iii. Insufficient risk guidance

[0063] Studies linking shoulder pain or rotator cuff injury to workplace factors have identified overhead work (defined as the elbow above the shoulder), applied forces, repetitive motion, and body load as important contributors. However, the quality of these studies has been mixed, with evidence inconsistently demonstrating a significant dose-response relationship, although two studies since then have linked arm elevation above 90° to the specific outcome of tendon damage detected on MRI or impingement surgery. One review specifically looked at the evidence for workplace factors in outcomes of rotator cuff tendinopathy and found a lack of research.

[0064] Even for studies with specific outcomes, the exposure assessments in these research papers are too coarse and the outcome categorization is still not specific enough to establish strong quantitative associations between "dose and response" or "exposure and risk." Therefore, currently published guidelines for shoulder demand impairment risk are not adequate for use in occupational ergonomic injury prevention programs. These guidelines simply recommend reducing or eliminating overhead or extended shoulder postures; they do not specify to what extent they need to be reduced to significantly affect injury rates.

[0065] While it is not feasible to eliminate repetitive, awkward or strenuous shoulder use in some industries, it can be improved. In the absence of available risk thresholds, the question remains as to what extent these risk factors should be reduced to prevent impairment. The issue is further complicated by the fact that this type of work may involve many repetitive motions (painting) and / or forces (drilling) and / or loads (welding). Any useful guidance should also include risk factor interactions and work / rest cycles.

[0066] iv. A new approach

[0067] Given that causality cannot be established using epidemiological data alone, an alternative approach is needed. The model and related aspects disclosed herein fill many gaps that cannot be filled by traditional epidemiological studies, but instead use fatigue models of cumulative damage to predict and prevent certain aspects of impairment.

[0068] The term "fatigue" is used throughout this disclosure in its mechanical sense - structural degradation caused by repetitive forces - rather than physiological fatigue, which is the inability to perform activities due to reduced muscle contraction force. The term "combined force" is used to represent the tension on the tendon caused by the weight of the tool, the posture of the body part, and the forces applied by the worker to the tool. More generally, "combined force" refers to the combination of forces acting on the soft tissue during the operation, including posture (e.g., shoulder position), vibration, tool weight, force vectors applied to the hand, arm weight, etc. Tension is associated with the shoulder, but other soft tissues (such as intervertebral discs) may experience compressive forces. Other types of soft tissues may experience both compressive and tensile forces.

[0069] v. Tendon subrupture

[0070] When people engage in physical activity, the effects of repeated stress on soft tissues can cause small cracks, often called microtrauma or subruptures. Subruptures themselves are not harmful to the body because the body will repair itself and become stronger if there is an adequate recovery period; this is a potential benefit of exercise. If the recovery period is insufficient, the tendon can become damaged and eventually damage can occur. The adequate recovery period for use (or overuse) of a tendon has previously been undetermined, unknown, or undefined.

[0071] Robust guidelines are needed that provide clear, acceptable limits and rest periods for shoulder-based work activities that account for the interaction between posture, force, duration, vibration, and repetition.

[0072] The model disclosed herein for the fatigue and repair phase of the supraspinatus tendon is beneficial for heavy industry, especially where overhead work, repetitions and forces are present, as well as for sports. Tendons behave like materials with predictable fatigue failures at given stress levels and cycles, but they are also capable of self-repair.

[0073] The methods presented herein utilize principles from materials science and medicine, for example, to propose models that can be used to set exposure limits and create useful work / rest cycles to predict and prevent overuse injuries of the shoulder, leading to a significant change in current approaches to mitigate and reduce shoulder injuries.

[0074] In some respects, the models disclosed herein do not include all aspects of tendon behavior. The prevalence of occupational shoulder injuries exceeds the limited research currently being conducted, and there are numerous opportunities for collaborative research between diverse groups such as aerospace engineers, fatigue specialists, orthopedic surgeons, and industrial ergonomics experts, with potentially enormous benefits to worker health.

[0075] The models disclosed herein can be used, for example, to redesign work practices that exceed reasonable tendon strain or stress thresholds, create work-rest cycles based on collagen damage and repair rates, identify individuals for whom the model is inadequate, and implement strength training to improve tendon material properties.

[0076] Note that the disclosed technology is not limited to tendons. As described herein, the examples are presented by way of illustration and not limitation with reference to shoulder tendons. However, the examples are not limited thereto. Other types of soft tissues (such as intervertebral (spinal) discs and ligaments) can be analyzed and impairment-modified using the examples disclosed herein.

[0077] II. Exemplary Methods

[0078] The present disclosure provides various methods for generating tendon injury models and guidelines for generating repetitive stresses in tendon materials. Certain aspects of these methods are described in Figures 1 to 3 Schematically shown in . As shown, method 100 includes generating one or more SN curves for one or more physical states of at least one tendon from one or more SN curve data sets (steps 102, 204). A given SN curve typically includes a graph of the magnitude of stress applied to the tendon versus the number of repetitions of tendon failure. The model component of each of the above processes will identify four (4) states of damage accumulation: 1) no damage; 2) accumulation of microscopic damage (sub-rupture); 3) accumulation of damage in the form of growing tears or cracks, cell matrix damage or other biological damage; 4) a state of catastrophic failure or separation of the tendon structure. Method 100 also includes generating one or more repetitive stress data sets describing the tendon (steps 104, 216). Method 100 also includes combining the SN curve (222) or data derived therefrom with the repetitive stress data set (216) to predict damage to the tendon under one or more conditions to generate a tendon injury model (steps 106, 216) (e.g., a physical or tangible representation or information of tendon damage under given conditions). In some aspects, the method 100 also typically includes generating at least one guideline for repetitive stress of tendon material based on the tendon injury model (steps 108, 224). As used herein, the term "guideline" refers to a set of recommended evidence-based maximum acceptable limits in terms of force (e.g., vibration, tool weight, force vector applied to the hand, arm weight, etc.), posture, position, frequency, duration and / or recovery, intended to protect human tissue materials from the risk of injury due to tendon injury during human activities (such as manufacturing or other processing). In some aspects, the guideline includes the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof. As also shown in the figure, the method also includes evaluating the role of a given muscle (302) and the injury of a given tendon (304) in some aspects.

[0079] The method of the present disclosure includes multiple aspects. In some aspects, for example, the method includes combining multiple SN curves for a physical state to produce a combined SN curve (222). In certain aspects, the method includes applying a cumulative damage model to predict damage to a tendon under one or more conditions when combining an SN curve (204) or data derived therefrom with a repetitive stress dataset (216). In some aspects, the method includes obtaining an SN curve dataset using one or more data sources including medical diagnostic technology, such as ultrasound data, computerized axial tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive test data, cadaveric material, animal material, polymer substitute material, molecular dynamics modeling (MDM) data, publication data, and combinations thereof (202, 204, 206).

[0080] Essentially any tendon can be evaluated as part of the methods disclosed herein. Some exemplary tendons that may be used include the teres minor tendon, the infraspinatus tendon, the supraspinatus tendon, the subscapularis tendon, the deltoid tendon, the biceps tendon, the triceps tendon, the brachioradialis tendon, the supinator tendon, the radial flexor carpi tendon, the ulnar flexor carpi tendon, the radial extensor carpi tendon, the radial extensor carpi brevis tendon, the iliopsoas tendon, the obturator internus tendon, the adductor longus tendon, the adductor brevis tendon, the adductor maximus tendon, the gluteus maximus tendon, the gluteus medius tendon, the quadriceps tendon, the patellar tendon, the hamstring tendon, the sartorius tendon, the gastrocnemius tendon, the Achilles tendon, the soleus tendon, the tibialis anterior tendon, the peroneus longus tendon In some aspects, tendon comprises mammal tendon. In some aspects, mammal tendon comprises people's tendon.

[0081] In some aspects, the method includes combining the SN curve or data derived therefrom and the repetitive stress data set with the healing data to predict damage to the tendon under one or more conditions (212). In some aspects, the present disclosure provides a method for predicting tendon damage in a subject, the method comprising: obtaining one or more usage data sets of at least one tendon of the subject, and comparing the usage data sets with the guide of repetitive stress of the tendon material generated by the method, thereby predicting tendon damage in the subject. In some aspects, the physical state includes a non-damaged state, a sub-rupture damage state, a crack initiation state, a fracture state or curve, and combinations thereof. In some aspects, one or more steps are at least partially implemented by a computer. Systems and related computer-readable media are further described herein.

[0082] In some aspects, the method includes generating at least one guideline (224) for repetitive stress of tendon material for a tendon from a tendon injury model (214), wherein the guideline includes a posture of the tendon, a number of repetitions of a given movement of the tendon, a force applied to the tendon, a duration of maintaining a given posture of the tendon, a duration of repetitions of a given movement of the tendon, a duration of a given force applied to the tendon, and combinations thereof. In some of these aspects, the guideline for repetitive stress of tendon material includes one or more recommended use / rest cycles for the tendon under one or more sets of use conditions. Typically, these methods include validating the guideline for repetitive stress of tendon material. In some of these aspects, the method includes personalizing the guideline (226) for a given subject by applying one or more demographic variables of the subject to the guideline. In some of these aspects, the method includes using task information (228) when generating the guideline. In some of these aspects, the task information includes tool weight and / or force vector.

[0083] In some aspects, the method includes estimating at least one stress distribution in the tendon to generate a repetitive stress data set (218). In some of these aspects, the method includes estimating the stress distribution in the tendon using at least one dimension of the tendon (220). In some of these aspects, the dimension includes at least one cross-sectional area of ​​the tendon. In some of these aspects, the method includes estimating the stress distribution in the tendon using at least one cyclic curve, the cyclic curve including a graph of at least one force applied to the tendon and the number of repetitions of the tendon failure (210). In some of these aspects, the force is determined in one or more postures of the tendon (208). In some of these aspects, the method includes determining the force using estimation and / or modeling techniques, such as finite element modeling (FEM) and / or electromyography (EMG). In some of these aspects, the method includes using task information when determining the force applied in the posture of the tendon. In some aspects, the task information includes tool weight and / or force vector.

[0084] In some aspects, processing of combined SN curves involves cumulative damage modeling and healing around multiple curves. To illustrate, an example may include a repetitive stress dataset (e.g., the dataset may correspond to exposure that a subject may accumulate while performing a task) that includes:

[0085] Task A Force: 150N Repetitions: 50

[0086] Task B Force: 100N Repetitions: 500

[0087] Task C Force: 50N Repeat: 5000

[0088] In some aspects, the number of repetitions is derived from observation or sampling of the task. The stress figure is derived from a digital model of the subject's hand / arm position while performing the task plus any loads the subject was subjected to in some aspects. The output will be force (N). Stress is force divided by the cross-sectional area of ​​the tendon (e.g., SST geometric model), in this case 50mm 2 This will create Task A Stress = 3MPa, Task B Stress = 2MPa, and Task C Stress = 1MPa. In this example, some information is also provided about the SN curve for where no damage occurs and the damage-free state ends (and sub-rupture begins) at 1000 cycles at 4MPa (this is a point on the curve).

[0089] All of these numbers are combined to determine the amount of damage to the tendon under consideration. In some aspects, the Miner's Rule is used as part of this process. In the Miner's Rule, if it exceeds 1, it means that the SN curve selected for comparison has been exceeded. In this case, since the no damage state was selected, exceeding 1 means that the no damage state no longer applies, but one of the other states applies.

[0090] These tasks, combined with the no damage state limit, yield (3MPa*50+2MPA*500+1MPa*5000) / (4MPa*1000)=1.53. This means that the no damage state is exceeded because it is >1. Furthermore, if a given guideline specifies that no damage is expected at all, then that guideline is exceeded. Therefore, if >1, then the next state needs to be calculated (e.g., sub-rupture), and the next state may need to be calculated, and so on, until the fracture state. In some aspects, healing data is also applied if multiple tasks are combined with rest periods in between (e.g., percentage reduction in exposure, moving to a lower point on the curve, etc.).

[0091] In this illustration, the calculations presented above provide a point solution (only one number and one answer) for one iteration. For better model fidelity, this iteration can be run multiple times, sampling from, for example, the distribution of SST geometry, the numerical model of the forces on the tendons, and optionally some variation in the repetitions. For example, this could be a model run multiple times with slightly different probability inputs, similar to a Monte Carlo simulation. This provides a risk output with some bounds or bands (e.g., 95% confidence intervals, etc.). Optionally, other variables (such as, demographic data) are also added to the model.

[0092] Figure 4 4 is a flow chart illustrating a method 400 for reducing (e.g., eliminating) repetitive stress damage to soft tissue in performing a treatment according to various examples. The method 400 can utilize material science properties of such soft tissue to determine guidelines that, when implemented as presently described with reference to the method 400, reduce the likelihood of damage to those performing the treatment. For example, the method 400 can be implemented in part using the system 500, as described below with reference to Figure 5 The method 400 also includes additional computer actions that provide improvements in the field of industrial hygiene. Such actions include, for example, obtaining a repetitive stress data set associated with the soft tissue and the treatment (e.g., a description of the force and number of repetitions per area of ​​the soft tissue) and implementing guidelines to reduce the likelihood of repetitive stress injuries to individuals during the treatment.

[0093] For example, method 400 can be used to improve repetitive stress injuries of workers who perform a portion of a manufacturing process. Each worker can have one or more tasks that form a portion of a manufacturing process, for example, on an assembly line. Each worker's tasks can be modified by one or more guidelines generated by method 400. Alternatively, method 400 can be used, for example, to improve repetitive stress injuries of athletes who perform a training program. For example, an athlete can perform one or more exercises that form a portion of a training program. The exercises can be modified by one or more guidelines generated by method 400. In general, method 500 can be used to improve repetitive stress injuries in any type of process, including repetitive motion of a person, without limitation to manufacturing or sports processes.

[0094] Method 400 can be used to improve damage to any of a variety of soft tissues. According to some examples, method 400 can be used to improve damage to a tendon or tendon complex. Examples of the above tendons and tendon complexes are shown in FIG. Figures 1 to 3 Given above. Alternatively, method 400 can be used to improve damage to connective tissue or musculoskeletal soft tissue. In general, non-limiting examples of soft tissues in which examples can be implemented include tendons, tendon complexes, intervertebral (spinal) discs, and ligaments.

[0095] At 402, method 400 obtains at least one repetitive stress data set associated with a soft tissue and a treatment. The repetitive stress data set can be in the form of a computer file, such as in a tab-delimited or comma-separated value (CSV) format. As non-limiting examples, the repetitive stress data set can be obtained by reading from a persistent electronic memory by a computer, or by entering the data into a file format and storing it in a computer.

[0096] According to some examples, each repetitive stress data set can represent the exposure that a subject may accumulate in a process of completing a task that forms part of the entire process. The exposure can be in the form of multiple repetitions and the force applied to the soft tissue at each repetition. For example, the force can be expressed in Newtons (N). Alternatively, each repetitive stress data set can be in the form of the number of repetitions and the stress on the soft tissue at each repetition. The stress can be a composite stress, including stresses caused by posture (e.g., shoulder position), vibration, tool weight, force vectors applied to the hand, arm weight, etc. For example, the stress can be expressed in megapascals (MPa).

[0097] Each repetitive stress data set may also include a description of the motion of each repetition, for example, in narrative form. Multiple repetitive stress data sets may account for multiple types of motion. Method 400 may be performed by acquiring repetitive stress data sets in a computer-readable form (e.g., through user input) and providing them to a computer program that performs actions such as blocks 402, 404, 406, and at least a portion of 408.

[0098] At 404, method 400 accesses information characterizing at least two damage states. The information can be in the form of a computer file, for example, as value pairs in a tab-delimited or comma-separated value (CSV) format. As non-limiting examples, the information can be accessed by reading the information from a permanent electronic memory by a computer, or by receiving and storing the information in a computer.

[0099] According to various examples, method 400 may access first information characterizing a first injury state and second information characterizing a second injury state. According to various examples, method 400 may further access third information characterizing a third injury state. Each information may quantify the number of repetitions of the soft tissue transitioning out of a corresponding injury state under a given stress. For example, the first information may quantify the number of repetitions of the soft tissue transitioning out of a first injury state per given stress; the second information may quantify the number of repetitions of the soft tissue transitioning out of a second injury state per given stress, and for example including a third injury state, the third information may quantify the number of repetitions of the soft tissue transitioning out of a third injury state per given stress.

[0100] Each information may be in the form of a curve that quantifies the number of repetitions of the soft tissue transitioning out of a corresponding damaged state under a given stress, for example, with stress as an independent variable and the number of repetitions as a dependent variable. For example, according to various examples, as described herein, each information may be in the form of an SN curve. When stored in a computer, such information may be in the form of a set of ordered pairs (S, R), where S represents stress and R represents the number of repetitions of transitioning out of a corresponding state.

[0101] According to various examples, the first damage state can be a non-damage state, the second damage state can be a sub-rupture damage state, and for example including its example, the third damage state can be a tear extension state. (Note that any combination of two or more damage states can be used according to various examples, not limited to those explicitly described at present.) The non-damage state can represent a situation where microscopic damage (e.g., sub-ruptures) occur in soft tissue at substantially the same rate as they heal. Transitioning out of the non-damage state can represent a situation where the rate at which sub-ruptures accumulate is faster than the healing rate of the corresponding soft tissue. The sub-rupture state can represent a situation where microscopic damage (e.g., sub-rupture damage) accumulates but has not yet formed a macroscopic tear. Transitioning out of the sub-rupture state can represent a situation where a macroscopic tear has been formed. The tear extension state can represent a situation where a tear has formed and is propagating through the soft tissue. Transitioning out of the tear extension state can represent a situation where the soft tissue has completely ruptured.

[0102] Note that combining examples of either or both of the no-injury state and the sub-rupture state can predict damage to the subject's soft tissue before the subject is aware that damage has occurred. For example, soft tissue in these states may be damaged but will not cause pain or discomfort to the subject.

[0103] At 406, method 400 predicts conditions sufficient to damage the soft tissue based at least on the information characterizing the damage state and the repetitive stress data set. The damage can be any of the following: the soft tissue is accumulating microscopic damage at a faster rate than the soft tissue is healing (e.g., transitioning out of a first state), the soft tissue is experiencing macroscopic tearing (e.g., transitioning out of a second state), or the soft tissue is experiencing complete rupture (e.g., transitioning out of a third state).

[0104] Prediction can utilize the material science characteristics of soft tissue to determine such conditions. According to some examples, prediction can be performed as follows. First, if not in terms of stress, the force data in the repetitive stress data set can be converted to unit stress. For example, the repetitive stress data set can be the force acting on the soft tissue at each repetition. By dividing this force by the cross-sectional area of ​​the soft tissue, the repetitive stress data set is converted to unit stress for each repetition (and / or each repetition). To this end, the system can store soft tissue cross-sectional area data. The soft tissue cross-sectional area data can include the average cross-sectional area of ​​various soft tissue types, the specific cross-sectional area of ​​each demographic combination (e.g., physiological sex, age, social gender), or a combination of these data. Secondly, the repetitive stress data set is compared with the information characterizing the injury state. For example, for a repetitive stress data set representing R repetitions under stress level S, the stress level S can be considered as an independent variable in the information representing the current injury state of the soft tissue, and the corresponding dependent variable R' in terms of the number of repetitions for transitioning out of the injury state can be identified. Third, the number of repetitions R' of the identified dependent variable is compared with the number of repetitions R listed in the repetitive stress data set. If the former is greater than the latter, the soft tissue is predicted to maintain its current damage state, and therefore no additional damage is predicted. However, if the former is less than or equal to the latter, the soft tissue is predicted to transition out of the corresponding damage state. In this case, the soft tissue is predicted to be damaged. Therefore, when the number of repetitions R specified in the repetitive stress data set for stress level S reaches or exceeds the number of repetitions R' corresponding to S according to the information characterizing the current damage state to transition out of the current damage state, the soft tissue is predicted to be damaged.

[0105] This process can be extended to include multiple repetitive stress data sets. For example, the products of the number of repetitions and the stress level from the various repetitive group data sets can be summed. This sum can be compared to the product of the stress level and the number of repetitions in the information characterizing the current damage state. If the sum is greater, the soft tissue is predicted to be damaged. Otherwise, the soft tissue is predicted to remain in the current damage state. Note that as mentioned above, Figures 1 to 3 The miner's rules described in the example described can be used for this comparison.

[0106] At 408, method 400 determines at least one guideline for reducing the risk of repetitive stress damage to the soft tissue material based at least on the prediction. Typically, the guideline can reduce the number of repetitions and / or the amount of stress corresponding to the actions in the one or more repetitive stress data sets. Such parameters can be reduced until the calculations described above with reference to block 406 predict no damage. The parameters so reduced can form all or part of the guideline.

[0107] In general, the guideline can reduce stress on the soft tissue by reducing the force on the soft tissue in any of a number of ways. The force on the soft tissue can be a resultant force on the soft tissue that is a result of posture (or position), weight (e.g., weight of an arm and / or holding an object such as a tool), an applied force vector (e.g., pushing with a hand), vibration (from a hand-held vibrating object, such as a hand-held tool), etc. According to some examples, the force is reduced by limiting any of the above parameters.

[0108] The force on the soft tissue may alternatively or additionally be reduced, thereby constraining the position and / or posture of the subject's body or a part thereof, thereby affecting the position and / or posture of the soft tissue. According to various examples, the guideline may include a constraint on at least one of the position of the soft tissue and / or the posture of the soft tissue.

[0109] Here, "position" can refer to a quantitative characterization of a subject's body or a part thereof. For example, a position can be defined using units such as length, angle, or xyz coordinates. For example, a tendon position can be defined with coordinates (0cm, 5cm, 1cm), where the origin (0cm, 0cm, 0cm) is the location where the tendon attaches to the humerus, and the coordinates correspond to the following positions in the following planes: x = sagittal, y = transverse, z = coronal. According to various examples, the position of a subject (or a part of a subject's body) can be determined by attaching a motion tracking system to the subject.

[0110] "Posture" may refer to a qualitative characterization of a subject's body or a part thereof. Postures may be defined in terms of the relative positions of identified landmarks. For example, a particular posture known as "overhead work" may be defined as a situation where a subject's elbow is above the subject's shoulder. Generally speaking, a posture may define a body position in a qualitative manner so that it may be observed and compared to another observation or position. According to various examples, the position of a subject (or a part of a subject's body) may be determined by an ergonomics expert or industrial engineer through observational studies.

[0111] Alternatively or additionally, the force on the soft tissue may be reduced, thereby limiting the duration of a motion, position, or posture. That is, the guideline may limit any one or a combination of: the duration of maintaining a given posture of a soft tissue, the duration of maintaining a given position of a soft tissue, the duration of repetitions of a given motion of a soft tissue, and / or the duration of a given force applied to a soft tissue.

[0112] According to some examples, the guidelines may include an imposed rest period. Such an example may utilize a representation of a healing process that counteracts micro or macro damage. The rest period may represent that such a healing process has sufficient time to counteract any accumulated damage.

[0113] The guidance may be output in any of a variety of forms. According to some examples, the guidance is output in narrative form using a pre-generated narrative template. For example, if calculations indicate that the number of repetitions should be reduced from 1000 to 725, the guidance may fill in these numbers into a template that reads in part: "The number of repetitions of action X should be reduced from Y to Z," where X is replaced with the action description, Y is replaced with 1000, and Z is replaced with 725. The formatted guidance may be output by display on a computer monitor, by email, or any other technique for providing information to an individual or process.

[0114] At 410, method 400 implements the guideline in a process. To this end, method 400 may include providing the guideline to workers on an assembly line, for example, where the process is a manufacturing process. The workers may then change their tasks accordingly. For example, where the process is a sports training process, the guideline may be provided to a coach who may change the athlete's training program accordingly. Additionally, the guideline may be used to design a production system, a product, a job task, a training program, and the like.

[0115] III. Exemplary Systems and Computer-Readable Media

[0116] The present disclosure also provides various systems and computer program products or machine-readable media. In some aspects, for example, the methods described herein are optionally performed or facilitated at least in part using systems, distributed computing hardware and applications (e.g., cloud computing services), electronic communication networks, communication interfaces, computer program products, machine-readable media, electronic storage media, software (e.g., machine executable code or logic instructions), and / or the like. For illustration, Figure 5A schematic diagram of an exemplary system suitable for use with at least multiple aspects of implementing the method disclosed in the present application is provided. As shown, the system 500 includes at least one controller or computer, such as a server 502 (e.g., a search engine server), which includes a processor 504 and a memory storage device or memory 506, and one or more other communication devices 514 (e.g., a client computer terminal, a phone, a tablet computer, a laptop computer, other mobile devices, etc.), which are located away from the server 502 and communicate with the server 502 through an electronic communication network 512 (such as, the Internet or other interconnected networks). The communication device 514 generally includes an electronic display (e.g., an Internet-enabled computer, etc.) that communicates with, for example, the server 502 computer through the network 512, wherein the electronic display includes a user interface (e.g., a graphical user interface (GUI), a network-based user interface, etc.) for displaying results when implementing the method described herein. In some aspects, the communication network also includes the physical transmission of data from one location to another, for example, using a hard drive, a thumb drive, or other data storage mechanism. The system 500 also includes a program product 508 stored on a computer or machine-readable medium, such as one or more of various types of memory (such as memory 506 of server 502), which can be read by server 502 for execution, for example, by one or more other communication devices (e.g., computer 514 (schematically shown as a desktop or personal computer). In some aspects, the system 500 optionally also includes at least one database server, such as server 510 associated with an online website, which stores data searchable directly or through server 502 (e.g., control samples or comparison results data, indexed customized therapies, etc.). The system 500 optionally also includes one or more other servers located remotely from the server 502, each server optionally associated with one or more database servers 510 located remotely or locally to each other server. The other servers can beneficially provide services to geographically remote users and enhance geographically distributed operations.

[0117] As will be appreciated by those skilled in the art, the memory 506 of the server 502 may optionally include volatile and / or non-volatile memory, including, for example, RAM, ROM, and magnetic or optical disks, etc. It will also be appreciated by those skilled in the art that, although shown as a single server, the illustrated configuration of the server 502 is given only as an example, and other types of servers or computers configured according to various other methods or architectures may also be used. Figure 5The server 502 schematically shown in the figure represents a server or server cluster or server group and is not limited to any single physical server. The server site can be deployed as a server group or server cluster managed by a server hosting provider. The number of servers and their architecture and configuration can be increased based on the use, demand and capacity requirements of the system 500. As also understood by those of ordinary skill in the art, in these aspects, the computer 514 can be, for example, a laptop computer, a desktop, a tablet computer, a personal digital assistant (PDA), a mobile phone, a server or other type of computer. As known and understood by those of ordinary skill in the art, the network 512 can include the Internet, an intranet, a telecommunications network, an extranet or the World Wide Web of multiple computers / servers, which communicate with one or more other computers through a communication network and / or a part of a local area network or other regional network.

[0118] As further understood by those of ordinary skill in the art, the exemplary program product or program product 508 may optionally be in the form of microcode, a program, a cloud computing format, a routine, and / or a symbolic language that provides one or more sets of ordered operations that control hardware functions and direct their operation. According to exemplary aspects, the program product 508 also need not reside entirely in volatile memory, but may be selectively loaded as needed according to various methods known and understood by those of ordinary skill in the art.

[0119] As further understood by one of ordinary skill in the art, the term "computer-readable medium" or "machine-readable medium" refers to any medium that participates in providing instructions to a processor for execution. For illustration, the term "computer-readable medium" or "machine-readable medium" includes distribution media, cloud computing formats, intermediate storage media, execution memory of a computer, and any other medium or device capable of storing program products 508 that implement the functions or processes of various aspects of the present disclosure (e.g., for reading by a computer). "Computer-readable medium" or "machine-readable medium" can take a variety of forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks. Volatile media include dynamic memory, such as the main memory of a given system. Transmission media include coaxial cables, copper wires, and optical fibers, including wires that make up a bus. Transmission media can also take the form of sound waves or light waves, such as those generated during radio wave and infrared data communications. Exemplary forms of computer readable media include floppy disks, diskettes, hard disks, magnetic tapes, flash drives or any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with patterns of holes, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves, or any other medium from which a computer can read.

[0120] Program product 508 is optionally copied from a computer readable medium to a hard disk or similar intermediate storage medium. When program product 508 or a portion thereof is to be run, it is optionally loaded from their distribution medium, their intermediate storage medium, etc. into the execution memory of one or more computers, configuring the computer to act according to the functions or methods of various aspects. All of these operations are well known to those of ordinary skill in the art of computer systems, for example.

[0121] To further illustrate, in certain aspects, the present application provides a system including one or more processors and one or more memory components in communication with the processor. The memory component typically includes one or more instructions that, when executed, cause the processor to provide information that causes at least one tendon injury model or component thereof, at least one guide for repetitive stress of tendon material or component thereof, etc. to be displayed (e.g., via computer 514, etc.), and / or receive information from other system components and / or from a system user (e.g., via computer 514, etc.).

[0122] In some aspects, the program product 508 includes non-transitory computer executable instructions that, when executed by the electronic processor 504, perform at least: generating one or more SN curves for one or more physical states of at least one tendon from one or more SN curve data sets, wherein a given SN curve comprises a graph of the magnitude of stress applied to the tendon versus the number of repetitions for tendon failure, and combining the SN curve or data derived therefrom with one or more repetitive stress data sets to generate a tendon injury model.

[0123] The system 500 also typically includes additional system components that are configured to perform various aspects of the methods described herein. In some of these aspects, one or more of these additional system components are located remotely from the server 502 and communicate with the server 502 via the electronic communication network 512, while in other aspects, one or more of these additional system components are located locally and communicate with the server 502 (i.e., without the electronic communication network 512) or communicate directly with, for example, a computer 514.

[0124] Although the foregoing disclosure has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent to those of ordinary skill in the art after reading this disclosure that various changes may be made in form and detail without departing from the true scope of the disclosure and may be implemented within the scope of the appended claims. For example, all methods, systems and / or components or other aspects thereof may be used in various combinations. All patents, patent applications, websites, other publications or documents cited herein are incorporated herein by reference in their entirety for all purposes, as if each individual item was specifically and individually indicated to be incorporated by reference.

Claims

1. A method (100, 400) of reducing the likelihood of repetitive stress damage to soft tissue in performing a manufacturing process or a sports training process, the method (100, 400) comprising: obtaining at least one repetitive stress data set associated with the soft tissue and the process (process 104, 216) (block 402); accessing at least first information characterizing a first damage state and second information characterizing a second damage state, wherein the first information quantifies a number of repetitions that the soft tissue transitions out of the first damage state at a first given stress, and wherein the second information quantifies a number of repetitions that the soft tissue transitions out of the second damage state at a second given stress (block 404), wherein the first damage state comprises a no damage state, and wherein the second damage state comprises a sub-rupture damage state; predicting conditions sufficient to injure the soft tissue based at least on the first information, the second information, and the at least one repetitive stress data set (steps 106, 216) (block 406), wherein the predicting includes: predicting conditions sufficient to transition from the first injury state to the second injury state; Based at least on the prediction, determining at least one guideline for reducing the risk of repetitive stress damage to the soft tissue material (steps 108, 224) (block 408), wherein the determining includes: determining at least one guideline for reducing the risk of transitioning from the first injury state to the second injury state; and implementing the at least one guideline (224) in the process (block 410), wherein the implementing includes: modifying a task of a worker on an assembly line in the manufacturing process or a training program of an athlete in the athletic training process according to the at least one guideline, wherein the at least one repetitive stress data set comprises at least one of a number of repetitions and a duration of at least one task of the treatment and a force on a cross section of the soft tissue, Wherein, the at least one guideline includes limitations on at least one of the following items: the posture of the soft tissue, the number of repetitions of a given movement of the soft tissue, the force applied to the soft tissue, the duration of maintaining a given posture of the soft tissue, the duration of repetitions of a given movement of the soft tissue, and the duration of a given force applied to the soft tissue.

2. The method according to claim 1, further comprising: accessing third information characterizing a third injury state, wherein the third information quantifies a number of repetitions of the soft tissue transitioning out of the third injury state under a third given stress; Wherein, the third damage state includes a tearing and extension state.

3. The method according to claim 1, wherein: Obtaining the at least one repetitive stress data set associated with the soft tissue and the process includes estimating at least one stress distribution in the soft tissue.

4. The method according to claim 1, wherein: Accessing at least first information characterizing a first injury state and second information characterizing a second injury state includes obtaining at least one of the following items: ultrasound data, computerized axial tomography CAT scan data, magnetic resonance imaging MRI scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling MDM data, and publication data.

5. The method according to claim 1, wherein: The soft tissues include the teres minor tendon, infraspinatus tendon, supraspinatus tendon, subscapularis tendon, deltoid tendon, biceps tendon, triceps tendon, brachioradialis tendon, supinator tendon, radial flexor carpi tendon, ulnar flexor carpi tendon, radial extensor carpi tendon, radial extensor carpi brevis tendon, iliopsoas tendon, obturator internus tendon, adductor longus tendon, adductor brevis tendon, adductor maximus tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps tendon, patellar tendon, hamstring tendon, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, Flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, opposition tendon, flexor pollicis longus tendon, extensor tendon, abductor pollicis tendon, flexor pollicis longus tendon, flexor digitorum brevis tendon, masseter tendon, abductor pollicis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, ocular tendon, levator palpebrae tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenic capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transverse abdominis tendon, latissimus dorsi tendon or erector spinae tendon.

6. The method according to claim 1, further comprising: The at least one guideline for the subject is personalized by applying one or more demographic variables of the subject to the at least one guideline.

7. The method according to claim 1, further comprising: obtaining one or more usage data sets of the soft tissue of the subject; as well as Damage to the soft tissue of the subject is estimated by comparing the one or more usage data sets to the at least one guideline.

8. A computer system (500) for reducing repetitive stress damage to soft tissue in performing a manufacturing process or a sports training process, the computer system comprising at least one electronic processor that executes instructions to perform operations including: obtaining at least one repetitive stress data set associated with the soft tissue and the process (process 104, 216) (block 402); At least first information characterizing a first impairment state and second information characterizing a second impairment state are accessed, wherein: The first information quantifies a number of repetitions of the soft tissue transitioning out of the first damage state at a first given stress, and wherein the second information quantifies a number of repetitions of the soft tissue transitioning out of the second damage state at a second given stress (block 404), wherein the first damage state comprises a no damage state, and the second damage state comprises a sub-rupture damage state; predicting conditions sufficient to injure the soft tissue based at least on the first information, the second information, and the at least one repetitive stress data set (block 406), wherein the predicting includes: predicting conditions sufficient to transition from the first injury state to the second injury state; and Based at least on the prediction, determining at least one guideline for reducing a risk of repetitive stress damage to a soft tissue material (224) (block 408), wherein the determining includes: determining at least one guideline for reducing a risk of transitioning from the first injury state to the second injury state; thereby implementing the at least one guideline in the process, wherein the implementing comprises: modifying a task of a worker on an assembly line in the manufacturing process or a training program of an athlete in the sports training process according to the at least one guideline, wherein the at least one repetitive stress data set comprises at least one of a number of repetitions and a duration of at least one task of the treatment and a force on a cross section of the soft tissue, Wherein, the at least one guideline includes limitations on at least one of the following items: the posture of the soft tissue, the number of repetitions of a given movement of the soft tissue, the force applied to the soft tissue, the duration of maintaining a given posture of the soft tissue, the duration of repetitions of a given movement of the soft tissue, and the duration of a given force applied to the soft tissue.

9. The computer system according to claim 8, wherein: The operations also include: accessing third information characterizing a third injury state, wherein the third information quantifies a number of repetitions of the soft tissue transitioning out of the third injury state under a third given stress; Wherein, the third damage state includes a tearing and extension state.

10. The computer system according to claim 8, wherein: Obtaining the at least one repetitive stress data set associated with the soft tissue and the process includes estimating at least one stress distribution in the soft tissue.

11. The computer system according to claim 8, wherein: Accessing at least first information characterizing a first injury state and second information characterizing a second injury state includes obtaining at least one of the following items: ultrasound data, computerized axial tomography CAT scan data, magnetic resonance imaging MRI scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling MDM data, and publication data.

12. The computer system according to claim 8, wherein: The soft tissues include the teres minor tendon, infraspinatus tendon, supraspinatus tendon, subscapularis tendon, deltoid tendon, biceps tendon, triceps tendon, brachioradialis tendon, supinator tendon, radial flexor carpi tendon, ulnar flexor carpi tendon, radial extensor carpi tendon, radial extensor carpi brevis tendon, iliopsoas tendon, obturator internus tendon, adductor longus tendon, adductor brevis tendon, adductor maximus tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps tendon, patellar tendon, hamstring tendon, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, Flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, opposition tendon, flexor pollicis longus tendon, extensor tendon, abductor pollicis tendon, flexor pollicis longus tendon, flexor digitorum brevis tendon, masseter tendon, abductor pollicis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, ocular tendon, levator palpebrae tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenic capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transverse abdominis tendon, latissimus dorsi tendon or erector spinae tendon.

13. The computer system according to claim 8, wherein: The operations also include personalizing the at least one guideline for the subject by applying one or more demographic variables of the subject to the at least one guideline.

14. The computer system according to claim 8, wherein: The operations also include: obtaining one or more usage data sets of the soft tissue of the subject; and Damage to the soft tissue of the subject is estimated by comparing the one or more usage data sets to the at least one guideline.

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