Combining multiple ergonomic risk factors in a single predictive finite element model

Abstract

A method of modeling soft tissue includes receiving one or more images showing anatomical geometry of a first subject. The anatomical geometry includes soft tissue. The method also includes measuring a plurality of parameters of the anatomical geometry of the first subject using one or more sensors attached to the first subject. The method also includes receiving a first set of material properties of the soft tissue of the first subject, a second subject, or both. The method also includes identifying a second set of material properties of the soft tissue characterizing the first subject performing a task. The method also includes determining a strain on the soft tissue, a stress on the soft tissue, or both based at least in part on the one or more images, the parameters, the first set of material properties, and the second set of material properties.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 180,353, filed April 27, 2021, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The topics described in this article generally relate to the field of materials science and its applications in ergonomics. More specifically, the topics disclosed herein concern repetitive stress damage to soft tissue materials and guidelines for minimizing the risk of suffering such damage. Background Technology

[0004] Overuse injuries, particularly to the shoulder (including the supraspinatus tendon), are among the most prominent ergonomic injuries. Therefore, understanding the dynamics of workplace or other activities that contribute to and may lead to stress, for example, on tendons, is crucial to addressing tasks or repetitive tasks that contribute to the injury. To build this understanding, information has been drawn from multiple research fields, including medical devices, surgical techniques, medicine, engineering, materials science, and ergonomics. However, these research fields currently do not intersect in ways that allow for the easy creation (or development) of models and guidelines for repetitive stress injuries to materials. This is evidenced by the research subjects and their focuses. For example, medical, therapeutic, and pharmacological research focuses on (or concentrates on) the individual after the injury has occurred: surgery, physical therapy programs, and treatments to accelerate recovery. Injury detection focuses on screening risk after patients self-report injury (rather than before). A model that can be used to generate guidelines for reducing or improving soft tissue injuries caused by workplace or other occupational and recreational activities remains needed. Summary of the Invention

[0005] This disclosure relates to systems and related methods for generating tendon injury models. Typically, these systems and related methods involve generating guidelines for mitigating or minimizing repetitive damage to tendon materials.

[0006] According to various examples, a method for reducing the likelihood of repetitive stress damage to soft tissue during the execution of a treatment is disclosed. The method includes: obtaining at least one repetitive stress dataset associated with the soft tissue and the treatment; accessing at least first information characterizing a first damage state (regime) and second information characterizing a second damage state, wherein the first information quantifies the number of times the soft tissue transitions to the first damage state under a given stress, and wherein the second information quantifies the number of times the soft tissue transitions to the second damage state under a given stress; predicting conditions sufficient to damage the soft tissue, at least based on the first information, the second information, and the repetitive stress dataset; determining at least one guideline for reducing the risk of repetitive stress damage to the soft tissue material, at least based on the prediction; and implementing the at least one guideline in the treatment.

[0007] Various optional features of the above examples include the following. The first damage state and the second damage state may each include one of the following: a no-damage state, a sub-rupture damage state, or a tear-extension state. The method may include: accessing third information characterizing a third damage state, wherein the third information quantifies the number of repetitions of the soft tissue transitioning to the third damage state under a given stress; wherein the first damage state includes a no-damage state, the second damage state includes a sub-rupture damage state, and the third damage state includes a tear-extension state. The repetitive stress dataset may include at least one of the number of repetitions or the duration of at least one task of the treatment, and forces in a cross-sectional region of the soft tissue. Obtaining at least one repetitive stress dataset associated with the soft tissue and the treatment may include estimating at least one stress distribution in the soft tissue. At least one guideline may include restrictions on at least one of the following: 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. Access to at least the first information characterizing the first state of injury and the second information characterizing the second state of injury may include obtaining at least one of the following: ultrasound data, computerized axial computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer alternative material data, molecular dynamics modeling (MDM) data, or publication data. Soft tissues can include the tendons of the teres minor, infraspinatus, supraspinatus, subscapularis, deltoid, biceps brachii, triceps brachii, brachioradialis, supinator, flexor carpi radialis, flexor carpi ulnaris, extensor carpi radialis, extensor carpi radialis brevis, iliopsoas, obturator internus, adductor longus, adductor brevis, adductor major, gluteus maximus, gluteus medius, quadriceps femoris, patellar tendon, hamstring tendons, sartorius, gastrocnemius, Achilles tendon, soleus, tibialis anterior, and peroneus longus. The tendons of the following muscles are included: flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, Opus pollicis tendon, flexor pollicis longus tendon, extensor tendon, abductor pollicis lateralis tendon, flexor pollicis longus tendon, flexor digitorum brevis tendon, block tendon, abductor pollicis longus tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, oculomotor tendon, levator palpebrae superioris tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenius capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transversus abdominis tendon, latissimus dorsi tendon, or erector spinae tendon. The method may include personalizing at least one guideline for the subject by applying one or more personal demographic variables of the subject to at least one guideline. The method may include: obtaining one or more usage datasets of the subject's soft tissues; and estimating soft tissue damage to the subject by comparing the usage datasets with at least one guideline.

[0008] According to various examples, a computer system for reducing repetitive stress damage to soft tissue during the execution of a process is disclosed. The system includes at least one electronic processor that executes instructions to perform operations including: obtaining at least one repetitive stress dataset related to the soft tissue and the process; accessing at least first information characterizing a first damage state and second information characterizing a second damage state, wherein the first information quantifies the number of times the soft tissue transitions to the first damage state under a given stress, and wherein the second information quantifies the number of times the soft tissue transitions to the second damage state under a given stress; predicting conditions sufficient to damage the soft tissue, at least based on the first information, the second information, and the repetitive stress dataset; and determining at least one guideline for reducing the risk of repetitive stress damage to the soft tissue material, at least based on the prediction; thereby implementing at least one guideline in the process.

[0009] Various optional features of the above examples include the following. The first damage state and the second damage state may each include one of the following: a no-damage state, a sub-rupture damage state, or a tear-extension state. The operation may further include: accessing third information characterizing a third damage state, wherein the third information quantifies the number of repetitions of the soft tissue transitioning to the third damage state under a given stress; wherein the first damage state includes a no-damage state, the second damage state includes a sub-rupture damage state, and the third damage state includes a tear-extension state. The repetitive stress dataset may include at least one of the number of repetitions or the duration of at least one task of the process, and forces in a cross-sectional region of the soft tissue. Obtaining at least one repetitive stress dataset related to the soft tissue and the process may include: estimating at least one stress distribution in the soft tissue. At least one guideline may include restrictions on at least one of the following: 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. Access to at least the first information characterizing the first state of injury and the second information characterizing the second state of injury may include obtaining at least one of the following: ultrasound data, computerized axial computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer alternative material data, molecular dynamics modeling (MDM) data, or publication data. Soft tissues can include the tendons of the teres minor, infraspinatus, supraspinatus, subscapularis, deltoid, biceps brachii, triceps brachii, brachioradialis, supinator, flexor carpi radialis, flexor carpi ulnaris, extensor carpi radialis, extensor carpi radialis brevis, iliopsoas, obturator internus, adductor longus, adductor brevis, adductor major, gluteus maximus, gluteus medius, quadriceps femoris, patellar tendon, hamstring tendons, sartorius, gastrocnemius, Achilles tendon, soleus, tibialis anterior, and peroneus longus. Tendons of the flexor digitorum longus, interosseous tendons, flexor digitorum profundus, abductor digiti minimi, opponens pollicis tendon, flexor pollicis longus tendon, extensor tendons, abductor pollicis lateralis tendon, flexor pollicis longus tendon, flexor digitorum brevis tendon, block tendons, abductor pollicis longus tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, oculomotor tendon, levator palpebrae superioris tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenius capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transversus abdominis tendon, latissimus dorsi tendon, or erector spinae tendon. Soft tissue is not limited to tendons. Soft tissue can also, or alternatively, include ligaments, intervertebral discs, muscles, skin, etc. For example, in the case of a lower back injury, both intervertebral discs and tendons are formed of soft tissue (e.g., type II collagen versus type I / III collagen). Therefore, the model input can be updated and different anatomical structures can be imported to extend the operation to other types of soft tissues (such as intervertebral discs).Bone characteristics can also be incorporated into the definition of the shoulder region. Consequently, cracks can be studied in other parts of the body. Analysis of fibrous muscle tissue in the primary load direction can also follow a similar approach to tendons. The procedure may also include personalizing at least one guideline for the subject by applying one or more individual demographic variables to at least one guideline. The procedure may also include: obtaining one or more usage datasets of the subject's soft tissues; and estimating soft tissue damage to the subject by comparing the usage datasets with at least one guideline.

[0010] In one aspect, this disclosure provides a method for generating tendon injury models (e.g., tendon injury accumulation models, etc.). The method includes generating one or more SN curves from one or more SN curve datasets for one or more physical states of at least one tendon, 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 datasets describing the tendon, and combining the SN curves or data derived therefrom with the repetitive stress datasets to predict tendon injury under one or more conditions (e.g., a physical or tangible representation or information of tendon injury under a given set of conditions), thereby generating a tendon injury model. However, it should be noted that the physical mechanisms by which damage accumulates in biological materials (such as collagen-based tendons) are very different from the physical mechanisms of conventional crystalline materials (based on which SN-type behavioral models are based). The latter is commonly modeled as damage accumulation through dislocation buildup at (typically) metallic grains or sliding barriers in crystals. With increasing cyclic loading, dislocations are generated at discontinuities in the microstructure of the structural material and gradually accumulate on the barrier. Even when the macroscopic stress is far below the material's yield strength, sustained cyclic loading eventually leads to small cracks at the accumulation sites. This then manifests as material defects and eventually develops into cracks. Although existing methods have attempted to model fatigue within soft tissues, such attempts produce inaccurate results because they treat soft tissues as having the fatigue characteristics of metals. However, despite the observation of SN-type behavior, the fatigue and cracking mechanisms of metals have no true analogues in collagen-based tendons subjected to similar cyclic loading. This is because there are no dislocations in tendons that adapt to localized plastic deformation. Instead, tendons adapt to cyclic loading through localized stretching and kinking of collagen fibers, which can be considered a form of microscopic damage. This damage can be repaired by the body through collagen remodeling, but it is time-dependent and occurs through an internal healing process. Therefore, effective models of damage accumulation in tendon structures include: a) a representation of microscopic damage accumulation 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 resulting in a detectable macroscopic defect in the tendon, typically represented as a tear or crack in the collagen structure, which may also occur concurrently with pain; d) a representation of the extension and growth of the 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 bearing the service load. The latter e) is a commonly used model for SN fatigue 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 progressive tears or cracks, cellular matrix damage, or other biological damage; and 4) catastrophic failure or separation of tendon structures. These model components can be represented mathematically and integrated into a collective model. This collective model is also validated through examination or inquiry methods that identify the presence and extent of advancing microscopic damage and tears.

[0011] On the other hand, this disclosure provides a method for generating guidelines for avoiding repetitive stress on tendon material and / or the accumulation of tendon damage. The method includes generating one or more SN curves from one or more SN curve datasets for one or more physical states of at least one tendon, wherein a given SN curve includes a graph of the magnitude of stress applied to the tendon as a function of the number of repetitions to achieve the transition to an injury state of the tendon. The method also includes generating one or more repetitive stress datasets describing the tendon and combining the SN curves or data derived therefrom with the repetitive stress datasets to predict tendon damage under one or more conditions to generate a tendon injury model. Furthermore, the method includes generating at least one guideline for repetitive stress on the tendon material from the tendon injury model, wherein the guideline includes tendon posture, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining the given posture of the tendon, the duration of repetitions of the given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof, thereby generating a tendon injury model for obtaining at least one guideline.

[0012] The method disclosed herein includes several aspects. In some aspects, for example, the method includes combining multiple SN curves of physical states to generate at least one combined SN curve. In some aspects, the method includes applying at least one cumulative damage model to predict tendon injury under one or more conditions when combining the SN curves or data derived therefrom with a repeatable stress dataset. In some aspects, the method includes obtaining an SN curve dataset using one or more data sources, including medical diagnostic techniques, such as ultrasound data, computerized axial computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material, animal material, polymer substitute material, molecular dynamics modeling (MDM) data, publication data, and combinations thereof.

[0013] In some respects, tendons include the supraspinatus tendon. In other respects, tendons include the teres minor tendon, infraspinatus tendon, supraspinatus tendon, subscapularis tendon, deltoid tendon, biceps brachii tendon, triceps brachii tendon, brachioradialis tendon, supinator tendon, flexor carpi radialis tendon, flexor carpi ulnaris tendon, extensor carpi radialis tendon, extensor carpi radialis brevis tendon, iliopsoas tendon, obturator internus tendon, adductor longus tendon, adductor brevis tendon, adductor major tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps femoris tendon, patellar tendon, hamstring tendon, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, and finger tendon. Tendons of the following muscles: flexor digitorum longus tendons, interosseous tendons, flexor digitorum profundus tendons, abductor digiti minimi tendons, Opus pollicis tendons, flexor pollicis longus tendons, extensor tendons, abductor pollicis lateralis tendons, flexor pollicis longus tendons, flexor digitorum brevis tendons, block tendons, abductor pollicis longus tendons, flexor digiti minimi tendons, oculomotor tendons, levator palpebrae superioris tendons, masseter tendons, temporalis tendons, trapezius tendons, sternocleidomastoid tendons, semispinalis capitis tendons, splenius capitis tendons, mylohyoid tendons, thyrohyoid tendons, sternohyoid tendons, rectus abdominis tendons, external oblique tendons, transversus abdominis tendons, latissimus dorsi tendons, or erector spinae tendons, and combinations thereof. In some respects, tendons include mammalian tendons. In some of these respects, mammalian tendons include human tendons. In other respects, tendons include non-mammal tendons.

[0014] In some aspects, the method includes combining SN curves or data derived therefrom, along with repetitive stress datasets, with healing data to predict tendon injury under one or more conditions. In some aspects, this disclosure provides a method for predicting tendon injury in a subject, the method comprising: obtaining one or more usage datasets of at least one tendon of the subject, and comparing the usage datasets with a guide to repetitive stress on tendon materials generated by the method, thereby predicting tendon injury in the subject. In some aspects, physical states include no-damage states, sub-rupture damage states, crack initiation states, fracture states, or curves, and combinations thereof. In some aspects, one or more steps are at least partially performed by a computer.

[0015] In some aspects, the method includes generating at least one guideline for repetitive stress of tendon material from a tendon injury model, wherein the guideline includes tendon posture and / or position, number of repetitions of a given movement of the tendon, force applied to the tendon, duration of holding the tendon in a given posture, duration of repetitions of a given movement of the tendon, duration of a given force applied to the tendon, and combinations thereof. In some aspects 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 aspects of these aspects, the method includes validating the guideline for repetitive stress of tendon material. In some aspects of these aspects, the method includes personalizing the guideline for a given subject by applying one or more personal demographic variables of the subject to the guideline. In some aspects of these aspects, the method includes using task information when generating the guideline. In some aspects of these aspects, the task information includes tool weight and / or force vector.

[0016] In some aspects, the method includes estimating at least one stress distribution in the tendon to generate a repetitive stress dataset. In some aspects, the method includes using at least one dimension of the tendon to estimate the stress distribution in the tendon. In some aspects, the dimension includes at least one cross-sectional area of ​​the tendon. In some aspects, the method includes using at least one cyclic curve to estimate the stress distribution in the tendon, the cyclic curve comprising a graph of at least one force applied to the tendon versus the number of repetitions of tendon failure. In some aspects, the force is determined in one or more postures of the tendon. In some aspects, the method includes using estimation and / or modeling techniques to determine the force, such as finite element modeling (FEM) and / or electromyography (EMG). In some aspects, the method includes using task information when determining the forces applied in multiple postures of the tendon. In some aspects, the task information includes tool weight and / or force vectors.

[0017] In other respects, this disclosure provides a system including a controller that includes or is capable of accessing a computer-readable medium including non-transitory computer-executable instructions that, when executed by at least one electronic processor, at least: generate one or more SN curves for one or more physical states of at least one tendon from one or more SN curve datasets, 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 combine the SN curves or the data derived therefrom with one or more repetitive stress datasets to generate a tendon injury model.

[0018] In another aspect, this disclosure provides a computer-readable medium comprising non-transitory computer-executable instructions that, when executed by at least one electronic processor, at least: generate one or more SN curves from one or more SN curve datasets for one or more physical states of at least one tendon, 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 combine the SN curves or data derived therefrom with one or more repetitive stress datasets to generate a tendon injury model.

[0019] In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: combining multiple SN curves for a physical state to produce at least one combined SN curve. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: applying at least one cumulative damage model to predict tendon injury under one or more conditions when combining SN curves or data derived therefrom with a repetitive stress dataset. In some aspects, the instructions of the system or computer-readable medium disclosed herein also at least perform: obtaining an SN curve dataset using one or more data sources, including medical diagnostic techniques, such as ultrasound data, computerized axial computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material, animal material, polymer substitute material, 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 SN curves or data derived therefrom, and a repetitive stress dataset with healing data, to predict tendon injury under one or more conditions. In some respects, the instructions of the system or computer-readable medium disclosed herein also perform at least the following: obtaining one or more usage datasets for at least one tendon of a subject, and comparing the usage datasets with guidelines for repetitive stress on tendon materials to predict tendon injury in the subject.

[0020] In some aspects, the instructions of the system or computer-readable medium disclosed herein further perform at least the following: generating at least one guideline for repetitive stress of tendon material from a tendon injury model, wherein the guideline includes tendon posture, number of repetitions of a given movement of the tendon, force applied to the tendon, duration of holding the given posture of the tendon, duration of repetition of the given movement of the tendon, 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 further perform at least the following: validating the guideline for repetitive stress of tendon material. In some aspects of these aspects, the instructions of the system or computer-readable medium disclosed herein further perform at least the following: personalizing the guideline for a given subject by applying one or more personal demographic variables of the subject to the guideline. In some aspects of these aspects, the instructions of the system or computer-readable medium disclosed herein further perform at least the following: using task information when generating the guideline. In some aspects of these aspects, the instructions of the system or computer-readable medium disclosed herein further perform at least the following: estimating at least one stress distribution in the tendon to generate a repetitive stress dataset. In some of these aspects, the instructions of the system or computer-readable medium disclosed herein also perform at least the following: estimating the 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 also perform at least the following: estimating the stress distribution in the tendon using at least one cyclic curve, comprising a graph of at least one force applied to the tendon versus the number of repetitions of tendon failure.

[0021] According to various examples, a method for modeling soft tissue is disclosed. The method includes receiving one or more images showing the anatomical geometry of a first subject. The anatomical geometry includes soft tissue. The method further includes measuring multiple parameters of the anatomical geometry of the first subject using one or more sensors attached to the first subject. The method further includes receiving a first set of material properties of the soft tissue of the first subject, a second subject, or both. The method further includes identifying a second set of material properties characterizing the soft tissue of the first subject performing a task. The second set of material properties differs from the first set of material properties. The method further includes determining strain, stress, or both on the soft tissue, at least in part based on one or more images, parameters, the first set of material properties, the second set of material properties, or a combination thereof.

[0022] In another implementation, the method includes receiving one or more images illustrating the anatomical geometry of a subject. The images may be or include magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, or a combination thereof. The anatomical geometry includes soft tissue. The subject is a living mammal. The method further includes measuring multiple parameters of the subject's anatomical geometry. The parameters are measured using one or more sensors attached to the subject. The parameters are measured while the subject is performing a task. The parameters include forceful exertion, posture, repetition, duration, vibration, or a combination thereof. The method further includes receiving a first set of material properties of the subject's soft tissue. The first set of material properties includes in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. The method further includes identifying a second set of material properties of the soft tissue during the characterization task. The second set of material properties differs from the first set. The second set of material properties includes isotropic properties of the soft tissue, anisotropic properties of the soft tissue, nonlinear behavior of the soft tissue, estimated damage state of the soft tissue, or a combination thereof. The method further includes: running a finite element model based at least in part on one or more images, parameters, a first set of material properties, a second set of material properties, or a combination thereof. The method further includes: determining strain, stress, or both on the soft tissue based at least in part on the running of the finite element model. The method further includes: generating a model describing the soft tissue based at least in part on the determined strain, determined stress, or both.

[0023] A system for characterizing the behavior of soft tissue in mammals is also disclosed. The system includes multiple sensors configured to attach to a human subject. The sensors are configured to measure multiple parameters while the human subject performs a repetitive task. Parameters include force, posture, repetition, duration, vibration, or combinations thereof. The system also includes a computing system configured to perform operations. The operations include receiving one or more images illustrating the anatomical geometry of the human subject. The images may be or include magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, or combinations thereof. The anatomical geometry includes soft tissue. The operations also include receiving parameters from the sensors. The operations further include receiving a first set of material properties of the soft tissue of the human subject. The first set of material properties includes in-plane linear modulus, out-of-plane linear modulus, Poisson's ratio, or combinations thereof. The operations also include identifying a second set of material properties characterizing the soft tissue during the repetitive task. The second set of material properties differs from the first set. The second set of material properties includes isotropic properties of the soft tissue, anisotropic properties of the soft tissue, nonlinear behavior of the soft tissue, estimated damage state of the soft tissue, or combinations thereof. The operation further includes: running a finite element model based at least in part on one or more images, parameters, a first set of material properties, a second set of material properties, or a combination thereof. The operation further includes: predicting strain modes, stress modes, vibration modes, and / or failure modes of the soft tissue based at least in part on the running of the finite element model. The operation further includes: generating a model describing the soft tissue based at least in part on strain modes, stress modes, vibration modes, failure modes, or a combination thereof. Attached Figure Description

[0024] The above and / or other aspects and advantages will become more apparent and easier to understand from the following detailed description of the examples taken in conjunction with the accompanying drawings, wherein:

[0025] Figure 1 This is a flowchart schematically illustrating exemplary method steps according to some aspects disclosed herein;

[0026] Figure 2 This is a schematic diagram illustrating exemplary method steps according to some aspects disclosed herein;

[0027] Figure 3 This is a schematic diagram illustrating exemplary method steps according to some aspects disclosed herein;

[0028] Figure 4 It is a flowchart illustrating methods for providing at least one guideline to reduce the risk of repetitive stress injury to tendons, based on various examples;

[0029] Figure 5It is a transparent view (e.g., an image) of a body with one or more sensors attached, according to some aspects disclosed herein;

[0030] Figure 6 It is a flowchart depicting a method for modeling body tissues (e.g., soft tissues) based on some aspects disclosed herein;

[0031] Figure 7 Models of soft tissue segments without a healing injury zone, soft tissue segments with a 2 mm × 5 mm healing injury zone, soft tissue segments with a 4 mm × 5 mm healing injury zone, and soft tissue segments with a 6 mm × 5 mm healing injury zone are shown according to some aspects disclosed herein.

[0032] Figure 8 A model of strain on a soft tissue segment is shown, based on some aspects disclosed herein;

[0033] Figure 9 Models of soft tissue segments with 3 mm slotted tears and 2 mm V-shaped tears are shown, according to some aspects disclosed herein.

[0034] Figure 10 A model of strain on a soft tissue segment is shown, based on some aspects disclosed herein;

[0035] Figure 11 It is a graph of one or more SN fatigue curves based on aspects disclosed herein, including the subject's anatomical geometry (e.g., soft tissue); and

[0036] Figure 12 It is a schematic diagram of a computing system for performing at least a portion of the methods disclosed herein, based on some aspects of the present invention. Detailed Implementation

[0037] Exemplary aspects will now be described more fully with reference to the accompanying drawings. However, examples of this 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 this disclosure thorough and complete, and to fully convey its scope to those skilled in the art. In the drawings, some details may be simplified and / or drawn to facilitate understanding rather than to maintain strict structural accuracy, detail, and / or scale.

[0038] It will be understood that when a component is described as "on another component," "associated with another component," "connected to another component," "electrically connected to another component," or "attached to another component," it can be directly on, associated with, connected to, electrically connected to, or attached to another component, or there may be intermediate components present. Conversely, when a component is described as "directly on another component," "directly associated with another component," "directly connected to another component," "directly electrically connected to another component," or "directly attached to another component," there are no intermediate components present. As used herein, the term "and / or" includes any and all combinations of one or more associated listed items.

[0039] It will be understood that although the terms first, second, etc., may be used herein to describe various elements, components, and / or directions, these elements, components, and / or directions should not be limited by these terms. These terms are used only to distinguish one element, component, and / or direction from another. For example, a first element, first component, or first direction may be referred to as a second element, second component, or second direction without departing from the teaching of the example.

[0040] For ease of description, this document may use spatial relative terms such as “below,” “under,” “down,” “above,” and “up” 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 will be understood that, in addition to the orientations depicted in the figures, the spatial relative terms are intended to cover different orientations of the device in use or operation.

[0041] As used herein, a given “component” and corresponding “component connector” refers to at least two components that are structured or otherwise operable to engage, operatively connect, or otherwise associate with each other. In some aspects, a component is structured or otherwise operable to engage, operatively connect, or otherwise associate with multiple component connectors. In some aspects, a component connector is configured or otherwise operable to engage, operatively connect, or otherwise associate with multiple components.

[0042] As used herein, “subject” refers to an animal, such as a mammalian species (e.g., human) or a avian species (e.g., bird) or a non-mammal species (e.g., fish, mollusks, reptiles, amphibians, etc.). More specifically, a subject can be a vertebrate, such as a mammal, such as a mouse, primate, ape, or human. Animals include farm animals (e.g., production cattle, dairy cows, poultry, horses, pigs, etc.), grazing animals, and companion animals (e.g., pets or support animals). A subject can be a healthy individual, an individual who has or is suspected of having a disease or predisposition to disease, or an individual who needs or is suspected of needing treatment.

[0043] The terminology used herein is for the purpose of describing particular examples only and is not intended to limit the examples. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that, when used in this specification, the terms “comprises,” “comprising,” “includes,” and / or “including” specify the presence of the said features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0044] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the examples pertain. It will be further understood that terms (such as those defined in common 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 defined herein.

[0045] While traditional ergonomic practices aim to prevent injury, this has only been achieved, or so far, at a macroscopic level using epidemiological approaches: estimates of when an individual will self-report injury based on discomfort level or pain, based on estimated work exposure, and guidelines created below that threshold; and most injuries are defined by broad body regions (the entire shoulder, knee, or back) rather than individual sites. Engineering publications since the 1990s have shown that ex vivo tendon materials exhibit strength reduction after repetitive stress, consistent with the principles of structural fatigue. These works have not reported the utility of this information in creating models or preventing or predicting injuries. They neither explore the significant structural and performance differences between tendons and abiotic structures, nor do they require the identification (or development) of engineered materials that can be used as alternatives for further testing, replacing tendons that are difficult to obtain and undergo alteration and deterioration once removed from living tissue. A new approach is needed that connects multidisciplinary research, fills gaps, and leverages evolving technologies across multiple fields. Significant advances in computer technology have made the creation of materials models more attractive. Computational materials models can simulate the fatigue behavior of existing or potentially new materials. Ultrasound probing can estimate the acoustic properties of biomaterials within a living subject, and these acoustic properties can be correlated with certain mechanical properties, such as the 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 models that can generate guidelines for reducing or improving soft tissue damage. The model can also take individual characteristics into account to create personalized models. Working models can also be used in reverse: informing the design requirements for constructing artificial or replacement tendons, and how these tendons perform throughout the movement process.

[0046] This disclosure relates to methods, systems, computer-readable media, and related models for determining and predicting tendon use based in some respects on materials science principles such as force (e.g., in different postures), stress distribution, stress data, and material (tendon) performance parameters (e.g., health status, healing, cumulative damage, and injury). This disclosure identifies useful correlations between materials 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 mitigate the likelihood of injury. While much of this disclosure focuses on shoulder injuries and the supraspinatus tendon as a specific example, the methods and related aspects disclosed herein can also be applied substantially to any soft tissue (such as intervertebral discs, ligaments, tendons, and tendon systems of interest) and in biomimetic applications (such as in the design of artificial tendons). References are made below. Figure 4 Examples describing how this disclosure can be applied to any soft tissue.

[0047] Using the shoulder in the workplace presents challenges in extreme work environments. Shoulder injuries also frequently occur outside the workplace, such as at home and while participating in sports or other occupational activities. Current guidelines do not provide clearly acceptable limits for shoulder-based work activities in the workplace and do not consider the interactions of posture, force, and repetition, nor do they address the interactions of posture, force, repetition, duration, and vibration. Practitioners who cannot simply eliminate shoulder movement face multiple levels of unknown risks. Therefore, robust threshold limits for shoulder joint demands are needed, addressing the complex characteristics of upper limb work, including the interactions of force, repetition, posture, and work / rest cycles. These guidelines would be invaluable to industrial engineers and ergonomics professionals, but would also be helpful to physicians, physical therapists, sports medicine practitioners, and sports coaches.

[0048] Shoulder injuries are well-documented and common in extreme workplaces such as heavy manufacturing, welding, fishing, meat processing, heavy machining, auto repair, and painting. Data from the U.S. Bureau of Labor Statistics in 2013 showed that cumulative shoulder injuries accounted for 15% of all workplace musculoskeletal injuries, second only to lower back and general back injuries; however, shoulder injuries tend to be more severe, resulting in greater time loss.

[0049] The shoulder mechanism allows for the placement, function, and control of the hand, 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 fish, or handle materials. Tools, the hand, and the shoulder system are typically located above the head or in hard-to-reach places. The shoulder complex must support the weight of the arm, any tools used for the work, and the applied forces. Most manual tasks involving tools involve tasks repeated multiple times during the work process.

[0050] The neutral shoulder posture is with the upper arm extended and hanging by the side of the body. Each time the shoulder leaves this neutral position, the tendons are subjected to tension, thus creating stress within the tendons. Each time the shoulder moves away from the neutral position, force and repetition occur, leading to cumulative fatigue damage to the tendons.

[0051] Studies linking shoulder pain or rotator cuff damage to workplace factors have identified overhead work (defined as the elbow above the shoulder), applied force, repetitive movements, and body load as significant contributors. However, the quality of these studies is inconsistent, with evidence unreliably demonstrating significant dose-response relationships, although two studies since then have associated upper arm elevation above 90° with specific outcomes of tendon damage detected on MRI or impingement surgery. One review specifically focused on the evidence for workplace factors in rotator cuff tendon disease outcomes and found a lack of research.

[0052] Even for studies with specific outcomes, the exposure assessments in these research papers are too coarse, and the outcome classifications remain insufficiently specific to establish a strong quantitative association between "dose and response" or "exposure and risk." Therefore, the currently published guidelines for shoulder demand injury risk are inadequate for occupational ergonomic injury prevention programs. These guidelines merely recommend lowering or eliminating overhead or extended shoulder postures; they do not specify the extent to which these postures need to be lowered to significantly impact injury rates.

[0053] While eliminating repetitive, clumsy, or strenuous shoulder use is impractical in some industries, it can be improved. In the absence of available risk thresholds, the question remains: to what extent should these risk factors be reduced to prevent injury? The problem is further complicated by the fact that this type of work may involve many repetitive movements (painting) and / or forces (drilling) and / or loads (welding). Any useful guidelines should also include the interaction of risk factors and work / rest cycles.

[0054] Given that causal relationships cannot be established solely using epidemiological data, an alternative approach is needed. The models and related aspects disclosed in this paper bridge many gaps that traditional epidemiological studies cannot address, and, as an alternative, use a fatigue model of cumulative damage to predict and prevent certain aspects of impairment.

[0055] The term "fatigue" is used throughout this disclosure in its mechanical sense—structural degeneration caused by repetitive forces—rather than physiological fatigue, which is the inability to perform activities due to reduced muscle contractility. The term "resultant force" is used to describe the tension in tendons caused by the weight of the tool, body posture, and the force applied to the tool by the worker. More generally, "resultant force" refers to the combination of forces acting on soft tissues during work, including posture (e.g., shoulder position), vibration, tool weight, the force vector applied to the hand, arm weight, etc. Tension is relevant to 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.

[0056] When people engage in physical activity, the repeated stress on soft tissues can cause small cracks, often referred to as microtrauma or sub-ruptures. Sub-ruptures themselves are harmless because, given sufficient recovery time, the body will repair itself and become stronger; this is a potential benefit of exercise. If the recovery time is insufficient, tendons can become damaged, eventually leading to injury. The required sufficient recovery time for tendon use (or overuse) was previously undetermined, unknown, or undefined.

[0057] There is a need for well-defined and acceptable limits and rest cycles for shoulder-based work activities, which explain the interaction between posture, force, duration, vibration, and repetition.

[0058] The model for supraspinatus tendon fatigue and repair disclosed in this paper is beneficial for heavy industry, especially for overhead work, repetitive and forceful conditions, and also for sports. Tendons behave like materials, exhibiting predictable fatigue failure under given stress levels and cycles, but they are also capable of self-repair.

[0059] The method proposed in this paper, for example, utilizes principles from materials science and medicine to develop such models. These models can be used to set exposure limits and create available work / rest cycles to predict and reduce or improve overuse injuries of the shoulder, leading to a significant change in current methods for mitigating and reducing shoulder damage.

[0060] In some respects, the model presented in this paper does not include all aspects of tendon behavior. The prevalence of occupational shoulder injuries exceeds the limited research currently underway, and there are numerous opportunities for collaborative research among diverse groups such as aerospace engineers, fatigue specialists, orthopedic surgeons, and industrial ergonomics experts, which could potentially offer significant benefits to worker health.

[0061] The model disclosed in this paper 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 with unreliable models, and implement strength training to improve tendon material properties.

[0062] Note that the techniques disclosed are not limited to tendons. As described herein, the examples are presented by way of illustration and are not limited to 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 impaired using the examples disclosed herein.

[0063] This disclosure provides various methods for generating soft tissue injury models and guidelines for generating repetitive stresses in soft tissue materials. As mentioned above, the described portions may focus on tendons; however, it will be understood that this is only an exemplary type of soft tissue, and the methods disclosed herein are equally applicable to other types of soft tissues (e.g., intervertebral (spinal) discs, ligaments, etc.). Some aspects of these methods are... Figures 1 to 3The figure is schematically illustrated. As shown, method 100 includes generating one or more SN curves for one or more physical states of soft tissue (e.g., tendon) from one or more SN curve datasets (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 components 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 a growing tear or crack, cellular matrix damage or other biological damage; and 4) a state of catastrophic failure or separation of the tendon structure. Method 100 also includes generating one or more repetitive stress datasets describing the tendon (steps 104, 216). Method 100 also includes combining the SN curves (222) or data derived therefrom with the repetitive stress datasets (216) to predict tendon injury 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 injury under a given condition). In some aspects, method 100 typically also includes at least one guideline (steps 108, 224) for generating repetitive stresses on the tendon material based on a tendon injury model. 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, designed to protect human tissue material from the risk of injury due to tendon injury during human activities (such as manufacturing or other processing). In some aspects, the guideline includes tendon posture, number of repetitions of a given movement of the tendon, force applied to the tendon, duration of holding a given posture of the tendon, duration of repetitions of a given movement of the tendon, duration of a given force applied to the tendon, and combinations thereof. Also, as illustrated, in some aspects, the method further includes assessing the action of a given muscle (302) and the injury of a given tendon (304).

[0064] The methods disclosed herein encompass various 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 some aspects, the method includes applying a cumulative damage model to predict tendon injury under one or more conditions when combining the SN curves or data derived therefrom with a repetitive stress dataset (216) (204). In some aspects, the method includes obtaining an SN curve dataset using one or more data sources, including medical diagnostic techniques, such as ultrasound data, computerized axial computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material, animal material, polymer substitute material, molecular dynamics modeling (MDM) data, publication data, and combinations thereof (202, 204, 206).

[0065] 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, infraspinatus tendon, supraspinatus tendon, subscapularis tendon, deltoid tendon, biceps brachii tendon, triceps brachii tendon, brachioradialis tendon, supinator tendon, flexor carpi radialis tendon, flexor carpi ulnaris tendon, extensor carpi radialis tendon, extensor carpi radialis brevis tendon, iliopsoas tendon, obturator internus tendon, adductor longus tendon, adductor brevis tendon, adductor greater tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps femoris tendon, patellar tendon, hamstring tendon, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, and peroneus longus tendon. Tendons include the flexor digitorum longus tendon, interosseous tendon, flexor digitorum profundus tendon, abductor digiti minimi tendon, Opus pollicis tendon, flexor pollicis longus tendon, extensor tendon, abductor pollicis lateralis tendon, flexor pollicis longus tendon, flexor digitorum brevis tendon, block tendon, abductor pollicis longus tendon, flexor digiti minimi tendon, oculomotor tendon, levator palpebrae superioris tendon, masseter tendon, temporalis tendon, trapezius tendon, sternocleidomastoid tendon, semispinalis capitis tendon, splenius capitis tendon, mylohyoid tendon, thyrohyoid tendon, sternohyoid tendon, rectus abdominis tendon, external oblique tendon, transversus abdominis tendon, latissimus dorsi tendon, or erector spinae tendon, and combinations thereof. In some respects, tendons include mammalian tendons. In some of these respects, mammalian tendons include human tendons.

[0066] In some aspects, the method includes combining SN curves or data derived therefrom, along with repetitive stress datasets, with healing data to predict tendon injury under one or more conditions (212). In some aspects, this disclosure provides a method for predicting (e.g., anticipating) tendon injury in a subject, the method comprising: obtaining one or more usage datasets of at least one tendon of the subject, and comparing the usage datasets with a guide to repetitive stress on tendon materials generated by the method, thereby predicting tendon injury in the subject. In some aspects, physical states include no-damage states, sub-rupture damage states, crack initiation states, fracture states, or curves, and combinations thereof. In some aspects, one or more steps are at least partially performed by a computer. The system and associated computer-readable media are further described herein.

[0067] In some aspects, the method includes generating at least one guide (224) for repetitive stress of tendon material against the tendon from a tendon injury model (214), wherein the guide includes tendon posture, number of repetitions of a given movement of the tendon, force applied to the tendon, duration of holding the tendon in a given posture, duration of repetitions of a given movement of the tendon, duration of a given force applied to the tendon, and combinations thereof. In some aspects of these aspects, the guide 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 guide for repetitive stress of tendon material. In some aspects of these aspects, these methods include personalizing the guide for a given subject by applying one or more personal demographic variables to the guide (226). In some aspects of these aspects, these methods include using task information when generating the guide (228). In some aspects of these aspects, the task information includes tool weight and / or force vector.

[0068] In some aspects, the method includes estimating at least one stress distribution in the tendon to generate a repetitive stress dataset (218). In some aspects of these aspects, the method includes using at least one dimension of the tendon to estimate the stress distribution in the tendon (220). In some aspects of these aspects, the dimension includes at least one cross-sectional area of ​​the tendon. In some aspects of these aspects, the method includes using at least one cyclic curve to estimate the stress distribution in the tendon, the cyclic curve including a graph of at least one force applied to the tendon versus the number of repetitions of tendon failure (210). In some aspects of these aspects, the force is determined in one or more postures of the tendon (208). In some aspects of these aspects, the method includes using estimation and / or modeling techniques to determine the force, such as finite element modeling (FEM) and / or electromyography (EMG). In some aspects 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.

[0069] In some respects, the processing of combined SN curves involves modeling and healing the cumulative damage around multiple curves. For illustration, one example might include a repetitive stress dataset (e.g., a dataset that might correspond to the cumulative exposure a subject might experience during a task), which includes:

[0070] Task A Force: 150N Repeatability: 50

[0071] Task B Strength: 100N Repeatability: 500

[0072] Task C strength: 50N; Repeatability: 5000

[0073] In some respects, the number of repetitions is derived from observation or sampling of the task. In other respects, the stress figure is derived from a digital model of the subject's hand / arm / limb position while performing the task, plus any load the subject experiences. The output will be force (N). Stress is the force divided by the cross-sectional area of ​​the tendon (e.g., SST geometry), which in this example is 50 mm². 2 This will create stresses for Task A = 3 MPa, Task B = 2 MPa, and Task C = 1 MPa. In this example, some information is also provided about the point on the curve where no damage occurs and the damage-free state ends (and begins sub-fracture) at 4 MPa for 1000 cycles.

[0074] All these numbers are combined to determine the extent of tendon damage under consideration. In some respects, the miner's rule is used as part of this process. In the miner's rule, a value greater than 1 indicates that the damage exceeds the SN curve selected for comparison. In this case, since the no-damage state was chosen, a value greater than 1 means that the no-damage state is no longer applied, and one of the other states is indeed applied.

[0075] These tasks are combined with the no-damage state constraint, resulting in (3MPa*50+2MPa*500+1MPa*5000) / (4MPa*1000) = 1.53. This means that the no-damage state is exceeded because it > 1. Furthermore, if a given guideline specifies that damage is not expected at all, then the guideline is exceeded. Therefore, the next state (e.g., sub-rupture) needs to be calculated, and if it > 1, then the next state after that may need to be calculated, and so on, until the fracture state. In some respects, healing data are also applied if multiple tasks are combined with intermediate rest periods (e.g., percentage reduction in exposure, movement to a lower point on the curve, etc.).

[0076] In this example, the computation presented above provides a point solution for one iteration (with only one number and one answer). For better model fidelity, this iteration can be run multiple times, sampling from, for example, the distribution of SST geometry, a numerical model of forces on the tendon, and optionally some variations in repeated iterations. 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 boundaries or bands around it (e.g., a 95% confidence interval, etc.). Optionally, other variables (such as demographic data) are also added to the model.

[0077] Figure 4 This is a flowchart illustrating a method 400 for reducing (e.g., improving) repetitive stress damage to soft tissue during a treatment, according to various examples. Method 400 utilizes the material science properties of such soft tissue to determine guidelines that, when implemented with reference to method 400 as described herein, reduce the likelihood of harm to the person performing the treatment. For example, method 400 can be partially implemented using system 1200, as described below. Figure 5 As shown and described. Method 400 also includes additional computer actions to provide improvements in the field of industrial hygiene. Such actions include, for example, obtaining a repetitive stress dataset related to soft tissue and the treatment (e.g., a description of the force and number of repetitions per region of the soft tissue) and implementing guidelines to reduce the likelihood of repetitive stress injury to an individual during the treatment. Method 400 focuses on tendons as an example of soft tissue; however, as stated above, Method 400 can also be used, or alternatively, to reduce (e.g., improve) repetitive stress injury to other types of soft tissue (e.g., intervertebral (spinal) discs, ligaments, etc.).

[0078] Method 400 can be used to improve repetitive stress injuries in workers performing part of a manufacturing process; however, this is merely an example, and Method 400 can also be used outside the workplace, as described below. Each worker may have one or more tasks that constitute part of a manufacturing process, for example, on an assembly line. Each worker's task may be modified by one or more guidelines generated by Method 400. Alternatively, Method 400 can be used, for example, to improve repetitive stress injuries in athletes performing a training program. The athlete may perform one or more exercises that constitute part of the training program. The exercises may be modified by one or more guidelines generated by Method 400. In general, Method 400 can be used to improve repetitive stress injuries in any type of process, including repetitive human movement, and is not limited to manufacturing or sports processes.

[0079] 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 tendons or tendon complexes. Examples of such tendons and tendon complexes are shown in [reference]. Figures 1 to 3 As given above. Alternatively, method 400 can be used to improve damage to connective tissue or musculoskeletal soft tissue. Generally, non-limiting examples of soft tissues that can be implemented include tendons, tendon complexes, intervertebral (spinal) discs, and ligaments.

[0080] For example, in the case of a back injury, both the intervertebral discs and tendons are formed from soft tissues (e.g., type II collagen versus type I / III collagen). Therefore, the model input can be updated in method 400, and / or different anatomical structures can be incorporated into method 400 to extend it to other types of soft tissues (such as intervertebral discs). Bone properties can also be incorporated into the definition of the shoulder region. As a result, cracks can be studied in other parts of the body. Analysis of fibrous muscle tissue in the main load direction can also follow a similar approach to that used for tendons.

[0081] In method 402, method 400 includes obtaining at least one repetitive stress dataset related to soft tissue (e.g., tendons, ligaments, intervertebral (spinal) discs, etc.) and treatment. The repetitive stress dataset may be in the form of a computer file, for example, in tab-delimited or comma-delimited values ​​(CSV) format. By way of non-limiting example, the repetitive stress dataset can be obtained by reading it from persistent electronic storage by a computer, or by inputting the data into a file format and storing it in a computer.

[0082] Based on some examples, each repetitive stress dataset can represent the cumulative exposure that a subject may accumulate while performing a task that forms part of the overall treatment. The exposure can be in the form of multiple repetitions and the force exerted on the soft tissue at each repetition. For example, the force can be expressed in Newtons (N). Alternatively, each repetitive stress dataset can be in the form of the number of repetitions and the stress exerted 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).

[0083] Each repetitive stress dataset may also include a description of each repeated movement, for example, in narrative form. Multiple repetitive stress datasets can interpret multiple types of movements. Method 400 can obtain repetitive stress datasets by acquiring them in a computer-readable form (e.g., through user input) and providing them to a computer program that performs actions such as at least a portion of boxes 402, 404, 406, and 408.

[0084] In 404, method 400 includes accessing information characterizing at least two damage states. The information may be in the form of a computer file, for example, as value pairs in tab-separated or comma-separated value (CSV) format. By way of non-limiting example, the information may be accessed by reading it from permanent electronic storage by a computer, or by receiving and storing the information in a computer.

[0085] According to various examples, method 400 can access first information characterizing a first damage state and second information characterizing a second damage state. According to various examples, method 400 can also access third information characterizing a third damage state. Each piece of information can quantify the number of times the soft tissue transitions to the corresponding damage state under a given stress. For example, the first information can quantify the number of times the soft tissue transitions to the first damage state at each given stress; the second information can quantify the number of times the soft tissue transitions to the second damage state at each given stress; and, for example, including a third damage state, the third information can quantify the number of times the soft tissue transitions to the third damage state at each given stress.

[0086] Each piece of information can be in the form of a curve that quantifies the number of repetitions by which soft tissue transitions to the corresponding damage state at a given stress, for example, with stress as the independent variable and the number of repetitions as the dependent variable. For example, according to various examples as described herein, each piece of information can be in the form of an SN curve. When stored in a computer, this information can be in the form of an ordered set of pairs (S,R), where S represents stress and R represents the number of repetitions of transitioning to the corresponding state.

[0087] Depending on the examples, the first damage state can be a no-damage state, the second damage state can be a sub-rupture damage state, and, for example, including examples of this, the third damage state can be a tear extension state. (Note that any combination of two or more damage states may be used, not limited to those explicitly stated here, depending on the examples.) A no-damage state can represent a situation where microscopic damage (e.g., sub-rupture) occurs in soft tissue at substantially the same rate as it heals. Transitioning from a no-damage state can represent a situation where the rate of accumulation of sub-ruptures is faster than the healing rate of the corresponding soft tissue. A sub-rupture state can represent a situation where microscopic damage (e.g., sub-rupture) has accumulated but has not yet formed a macroscopic tear. Transitioning from a sub-rupture state can represent a situation where a macroscopic tear has formed. A tear extension state can represent a situation where a tear has formed and is propagating through the soft tissue. Transitioning from a tear extension state can represent a situation where the soft tissue has completely ruptured.

[0088] Please note that combining examples of either or both of the no-damage and sub-rupture states can predict damage to the subject's soft tissues before the subject is aware that damage has occurred. For example, soft tissues may be damaged in these states, but without causing pain or discomfort to the subject.

[0089] In 406, method 400 predicts conditions sufficient to damage soft tissue based at least on information characterizing the damage state and a repetitive stress dataset. The damage can be any of the following: the soft tissue accumulates microscopic damage at a rate faster than the soft tissue can heal (e.g., transitioning to a first state), the soft tissue is undergoing macroscopic tearing (e.g., transitioning to a second state), or the soft tissue is undergoing complete rupture (e.g., transitioning to a third state).

[0090] Prediction / foresight can be made using the materials science properties of soft tissue to determine such conditions. Based on some examples, predictions can be performed as follows: First, force data in a repetitive stress dataset can be converted to unit stress, if not in terms of stress. For example, a repetitive stress dataset could be the force acting on soft tissue for each repetition. By dividing this force by the cross-sectional area of ​​the soft tissue, the repetitive stress dataset is converted to unit stress for each repetition (and / or per repetition). For this purpose, the system can store soft tissue cross-sectional area data. Soft tissue cross-sectional area data can include the average cross-sectional area for various soft tissue types, specific cross-sectional areas for each demographic combination (e.g., biological sex, age, social gender), or combinations of these. Second, the repetitive stress dataset is compared with information characterizing the damage state. For example, for a repetitive stress dataset representing R repetitions at a stress level S, the stress level S can be considered as the independent variable in information representing the current damage state of the soft tissue, and the corresponding dependent variable R' for the number of repetitions used to transition to the damage 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 dataset. 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 dataset for stress level S reaches or exceeds the number of repetitions R' corresponding to S based on the information characterizing the current damage state to transition out of the current damage state, the soft tissue is predicted to suffer damage.

[0091] This process can be extended to include multiple repetitive stress datasets. For example, the products of the number of repetitions and stress levels from various repetitive stress datasets can be summed. This sum can be compared with the product of stress level and number of repetitions in information characterizing the current damage state. If the sum is large, soft tissue damage is predicted. Otherwise, the soft tissue is predicted to remain in its current damage state. Note that, as referenced above... Figures 1 to 3 The miner rules described in the example can be used for this comparison.

[0092] At 408, method 400 determines at least one guideline based on this prediction for reducing the risk of repetitive stress damage to soft tissue materials. Typically, this guideline reduces the number of repetitions and / or stress corresponding to actions in one or more repetitive stress datasets. Parameters can be reduced until the calculated predictions described above in reference box 406 show no damage. Such reduced parameters can form all or part of the guideline.

[0093] Typically, guidelines suggest reducing soft tissue stress by decreasing the forces acting on the soft tissue in any of a variety of ways. Forces acting on soft tissue can be a resultant force resulting from posture (or position), weight (e.g., the weight of an arm and / or holding an object such as a tool), applied force vector (e.g., pushing with the hand), vibration (from holding a vibrating object, such as a handheld tool), etc. According to some examples, forces are reduced by limiting any of the parameters mentioned above.

[0094] Forces on soft tissue may be selectively or additionally reduced, thereby restricting the position and / or posture of the subject's body or parts thereof, and thus affecting the position and / or posture of the soft tissue. Depending on various examples, guidelines may include restrictions on at least one of the position and / or posture of the soft tissue.

[0095] Here, "location" can refer to a quantitative representation of the subject's body or a part of the subject's body. For example, a measuring device can be used to define a location, with units such as length, angle, or xyz coordinates. For instance, a tendon location can be defined using 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. Depending on various examples, the location of a subject (or a part of the subject's body) can be determined by attaching a motion tracking system to the subject.

[0096] "Posture" can refer to a qualitative representation of a subject's body or a part thereof. Posture can be defined based on the relative position to identified landmarks. For example, a specific posture known as "overhead work" can be defined as a situation where the subject's elbow is higher than the subject's shoulder. Typically, posture can be defined qualitatively as body position so that it can be observed and compared with another observation or position. Depending on various examples, the position of a subject (or a part of the subject's body) can be determined through observational studies by ergonomic experts or industrial engineers.

[0097] Alternatively or additionally, the force on the soft tissue can be reduced, thereby limiting the duration of movement, position, or posture. That is, the guidelines may limit any one or a combination of the following: the duration of holding a given posture of the soft tissue, the duration of holding a given position of the soft tissue, the duration of repetition of a given movement of the soft tissue, and / or the duration of a given force applied to the soft tissue.

[0098] Based on some examples, guidelines may include imposed rest periods. Such examples can utilize representations of healing processes that counteract microscopic or macroscopic damage. Rest periods can represent sufficient time for such healing processes to counteract any accumulated damage.

[0099] Guidelines can be output in any of a variety of formats. Based on some examples, guidelines can be output in narrative form using pre-generated narrative templates. For instance, if calculations indicate that the number of repetitions should be reduced from 1000 to 725, a guide could populate these numbers into a template that partially states "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. Formatted guidelines can be output via display on a computer monitor, email, or any other technology used to provide information to individuals or for processing.

[0100] In step 410, method 400 implements guidelines in the process. For this purpose, method 400 may include providing guidelines to workers on an assembly line, for example, where the process is a manufacturing process. Workers can then modify their tasks accordingly. For example, if the process is a sports training process, guidelines can be provided to a coach, who can then modify the athlete's training plan accordingly. Furthermore, guidelines can be used to design production systems, products, work tasks, training plans, etc.

[0101] The systems and methods disclosed herein can generate models that use material properties to characterize the behavior of living mammalian soft tissues (e.g., tendons, discs, etc.) subjected to different loading conditions. Soft tissues can be modeled in response to a number of different loading conditions to characterize damage to soft tissues during overuse conditions, thereby reducing the likelihood of injury.

[0102] The model can be or may include (e.g., a single) finite element modeling (FEM) that determines or predicts material fatigue in soft tissues. Instead of mammals, or in addition to mammals, the model can also be or alternatively applied to other species, such as reptiles, amphibians, etc. In one example, soft tissue can be or may include the supraspinatus tendon (SST) in the shoulder. Instead of soft tissues, or in addition to soft tissues, the model can also be or alternatively applied to connective tissues, musculoskeletal tissues, intervertebral discs, etc.

[0103] As described in more detail below, the model can receive input data subjected to different load conditions. In one example, the input data may be or may include one or more (e.g., five) established ergonomic risk factors: force, posture, repetition, duration, and vibration. The model can demonstrate the correlations between simultaneous exposures to these risk factors. This paper demonstrates the relationships between simultaneous exposures to ergonomic risk factors, thus proving that the interaction of risk factors leads to a combined effect that may result in damage with a definite multiplier.

[0104] Input data may also or alternatively include the (linear and / or nonlinear) elastic modulus of soft tissue, strain on soft tissue, cross-sectional area of ​​soft tissue, percentage of healthy soft tissue, percentage of damaged / injured soft tissue, or a combination thereof. Input data may also or alternatively include (e.g.,) 3D anatomical geometry of the shoulder, load transfer paths between tissue and bone across one or more joints, external (e.g., task-related) boundary conditions (e.g., shoulder position or posture), internal (e.g., anatomically related) boundary conditions, or a combination thereof. Data may also or alternatively include parameterized and combinatorially interactive whole systems, rather than overly general task guidance based on empirical studies. In contrast, traditional ergonomic approaches rely on empirical datasets focused on resulting damage, rather than modeling damage initiation based on material behavior at the component level.

[0105] The model can output one or more fatigue SN curves. The model can also, or alternatively, output assessments of one or more (e.g., four) states of tissue damage: no damage, sub-rupture, partial damage, and complete damage. The model can also, or alternatively, output comparisons of various changes to work processes and tools (e.g., assessing reduced range of motion, power tools of different weights and vibration frequencies, the introduction of ergonomic aids, etc.), which can lead to optimized task design guidelines and / or recommended work intervals for a given task.

[0106] In one implementation, model fidelity can be improved by incorporating nonlinear behavior and introducing complex loading and vibration / fatigue cycles of soft tissue. This model can validate that finite element modeling is a viable method for evaluating healthy and partially damaged tissues, describing the fatigue failure characteristics of soft tissues (such as tendon materials) during overuse.

[0107] Vibration effects can be characterized by the hand passing through the body and entering soft tissues (e.g., tendons) while holding a vibrating tool. Characterizing soft tissue material behavior by capturing viscoelastic behavior across systems and characterizing the effects of hand / arm / limb (and other parts) vibrations can highlight the accelerated material fatigue and damage across the entire soft tissue geometry.

[0108] Figure 5 This is an image of the body of a subject 502 with one or more sensors attached (e.g., three sensors are shown: 520A-520C) according to some aspects disclosed herein. Subject 502 includes a neck 510, a shoulder 512, and an arm 514. Subject 502 also includes ligaments 530 and tendons (e.g., SSTs) 532.

[0109] Sensors 520A-520C can be attached to subject 502 and configured to measure one or more parameters. As shown, a first sensor 520A can be attached to the neck 510, a second sensor 520B can be attached to the shoulder 512, and a third sensor 520C can be attached to the arm 514. In another implementation, one or more of sensors 520A-520C (or another sensor) can be (e.g., directly) attached to subject 502 above / outside the ligament 530 and / or tendon 532 to be monitored. Sensors 520A-520C can also or alternatively be attached to different locations on subject 502 (e.g., feet, legs, hips, abdomen, back, neck, head, etc.). Furthermore, although three sensors 520A-520C are shown, more or fewer sensors can be used. The measured parameters can be or can include any of the input data described above (e.g., force, posture, repetition, duration, vibration, etc.).

[0110] Figure 6 This is a flowchart illustrating a method 600 for modeling soft tissue according to some aspects disclosed herein. Method 600 can monitor soft tissue to reduce the likelihood of damage to soft tissue performing tasks (e.g., in the workplace). Method 600 can utilize a computing system 1200 (see below). Figure 12 The method (shown and described) is partially implemented. Method 600 also includes providing improved computer-aided actions for industrial hygiene. Such actions include, for example, obtaining a repetitive stress dataset related to soft tissue and treatment (e.g., a description of the forces and the number of repetitions per region of the soft tissue) and implementing guidelines to reduce the likelihood of repetitive stress damage to individuals during treatment.

[0111] The following provides an exemplary order of steps 600. However, one or more steps of steps 600 may be performed in a different order, simultaneously, repeatedly, or omitted.

[0112] Method 600 may include receiving images, videos, or both that include or illustrate the anatomical geometry of a subject, as in 602. In one implementation, the anatomical geometry may be the geometry of a specific subject being analyzed (e.g., a specific person). In another implementation, the anatomical geometry may be the geometry of other similar subjects (e.g., not the specific person being analyzed). Images and / or videos may be or may include magnetic resonance imaging (MRI), ultrasound images, computed tomography (CT) scans, 3D scans, or combinations thereof. Anatomical geometry may be or may include shapes and / or spatial connections describing the 2D and / or 3D anatomical structures being analyzed. As used herein, “shape” may refer to an anatomical structure (e.g., soft tissue, bone, etc.) and / or soft tissue variations (e.g., length, area, etc.) relative to a given task location (e.g., overhead arm extension versus resting on one side). As used herein, “spatial connection” refers to the position of limbs and joints relative to the rest of the body and the task-specific kinematics. For example, a larger torque may be applied to a joint during a task requiring extension away from the body, or off-axis movements may cause shear forces. Anatomical structures can be mammalian anatomical structures (e.g., human anatomical structures); however, as stated above, other types of anatomical structures are also envisioned in this paper. Anatomical geometry can help create a basis for defining the motion paths and / or forces on the anatomical structures, which can be used to simulate workplace tasks.

[0113] Method 600 may also include measuring one or more parameters of the subject's anatomical geometry, as in 604. These parameters may or may not be measured using images, videos, or both. One or more parameters may be measured while the subject is performing a task (e.g., a workplace task). In one implementation, the parameters may be or may include any of the input data described above, such as ergonomic risk factors: force, posture, repetition, duration, vibration, or combinations thereof. In another implementation, the subject may be exposed to or subjected to one or more individual ergonomic risk factors, and the parameters may be or may include the soft tissue response to the ergonomic risk factors (e.g., strain, stress, etc.). The latter implementation can be used for detailed material characterization (e.g., cadaver or alternative DMA analysis) to obtain viscoelastic material properties that might be difficult to obtain from field-tested subjects.

[0114] Parameters can be measured by one or more sensors 520A-520C on subject 502 (e.g., when subject 502 performs a task). For example, a user (e.g., a worker) may have one or more tasks that form part of a manufacturing process (e.g., on an assembly line). Parameters can be used to define expected forces and motion paths on the anatomical construct, which can be used to characterize workplace tasks. Motion paths and forces can be applied to the anatomical geometry to produce a physical model prepared for integration with material inputs.

[0115] Method 600 may further include loading or importing anatomical geometry (from 602) and one or more parameters (from 604) into an FEM (e.g., a computational framework), as in 606. The imported anatomical geometry (from 602) may include MRI images, ultrasound scans, and other imaging techniques. The imported anatomical geometry (from 602) may also include direct measurements from a living subject using ultrasound. As an example, a subject's shoulder can be tested by ultrasound scanning while the subject stands upright with his / her arms fully extended forward, pressing against a target above his / her head while the applied force is measured. The ultrasound scan image of the tendon can then be digitized into a CAD geometry and imported into the FEM numerical engine. The same process can be repeated for different target locations relative to the subject's shoulder as the applied force also varies. By combining multiple measurements, the FEM predicts the stress and strain of the tendon for a given variation in location and applied force.

[0116] Method 600 may further include receiving a first set of material properties of the anatomical geometry of the subject, as in 608. More specifically, this may include receiving a first set of material properties of the soft tissue of the subject. In one implementation, the first set of material properties may be measured in a specific subject being analyzed (e.g., a specific person) using external imaging techniques such as shear wave elastography, ultrasound, or both. In another implementation, the aforementioned techniques may be used to measure the first set of material properties in other similar subjects (e.g., not the specific person being analyzed). In yet another implementation, the first set of material properties may be received or obtained from empirical studies or published research. The first set of material properties may be or may include in-plane modulus, out-of-plane modulus, Poisson's ratio, etc. The modulus may be linear, nonlinear, or both.

[0117] Method 600 may further include identifying a second set of material properties characterizing the subject's anatomical geometry, as in 610. This second set of material properties may characterize the material properties of the subject's soft tissues during the task. In one example, the second set of material properties may characterize isotropic and / or anisotropic compositional properties of the soft tissue, nonlinear behavior of the soft tissue, estimated injury state of the soft tissue, or combinations thereof. In another example, the soft tissue material modulus may stiffen under higher loads, tendon extension, applied vibration, or combinations thereof. In response, anisotropic behavior of the soft tissue may occur, which can be defined using the modulus in one or more vectors to obtain complex motion paths and load various ranges of motion. Acute injury and healing of tendon fibers can also alter local soft tissue material properties. If one or more material properties are unavailable, one or more material properties may be measured from a human test subject or surrogate using external imaging techniques or material sample combination techniques such as Dynamic Mechanical Analysis (DMA). For example, the second set of material properties may be measured using external imaging techniques or material sample combination techniques such as Dynamic Mechanical Analysis (DMA).

[0118] Method 600 may further include loading or importing the received material properties (from 606) and the characterized material properties (from 608) into a FEM (e.g., a computational framework), as in 612. The FEM may be based on simple and / or complex material properties. Complex material properties may include, but are not limited to, nonlinear, frequency-dependent, viscoelastic, orthotropic, or combinations thereof. For an FEM capable of computationally predicting the stress / strain state and properties of soft tissue (e.g., shoulder tendons), the tendon material may first be digitally represented as an orthotropic material (as in 612) using / based on published literature material properties (from 606). To improve accuracy, the use of a simple force / displacement characterization of the tendon material properties (from 608) may be integrated into the model. In another implementation, accuracy may be improved by integrating a more complex viscoelastic characterization of the tendon material properties (from 610) into the model. Characterization may be performed directly on the tendon or indirectly through methods as described in 602. Direct characterization can be based, at least in part, on different demographic or synthetic / alternative versions of cadaveric tendons from tendon materials. Indirect characterization can be performed by utilizing medically published and accepted references (from 602) to indirectly measure force / displacement or stress / strain of the tendon or a portion of the tendon (e.g., surface only).

[0119] Before performing or running the FEM (as described below), the anatomical geometry (from 602) may first be meshed to take into account the geometry (from 602) and material orientation (from 608 to 612). The FEM may also have specified boundary conditions to allow the applied load to act on the geometry (from 602). The load applied in the FEM may be specified to follow a cyclic path to reproduce the cyclic motion of the subject as physically observed in method 600.

[0120] Method 600 may also include performing or running the FEM, as in 614. This may include computationally processing the FEM (e.g., an integrated physical and material model), which can then be analyzed for outputs and trends of interest. The FEM may be performed / run using and / or based on anatomical geometry (from 602), one or more parameters (from 604), received material properties (from 606), characterized material properties (from 608), or a combination thereof.

[0121] Method 600 may also include determining one or more patterns of the subject's anatomical geometry (e.g., soft tissue), as in 616. Pattern determination may be based at least in part on the operation of the FEM. Therefore, patterns may be determined at least in part based on anatomical geometry (from 602), one or more parameters (from 604), received material properties (from 606), characterized material properties (from 608), or a combination thereof. Patterns may be or may include stress patterns, referring to stress on the subject's anatomical geometry (e.g., soft tissue). Patterns may also be or may include strain patterns, referring to strain on the subject's anatomical geometry (e.g., soft tissue). Patterns may also be or may include vibration patterns, referring to the natural frequencies that may exaggerate localized loads on the anatomical structure. For example, a vibration pattern may refer to vibrations applied by a power tool or similar device held by the subject during task performance, which may be translated into soft tissue (e.g., shoulder tendons) due to the tension or friction of a tendon against vibrating bone or other anatomical structures surrounding the tendon. These modes can also be or may include failure modes, which refer to progressive tissue damage. For example, failure modes can refer to soft tissue (e.g., tendon) fatigue, wear and tear on the tendon surface due to friction, partial blockage of the tendon, and / or compression of nerves adjacent to the tendon.

[0122] In one implementation, the model can be predictive (e.g., providing foresight). In other words, if the task continues, the model can predict the stress, strain, vibration, failure, etc., of the soft tissue at future times. For example, if the task continues, the model can predict when the stress, strain, vibration, failure, etc., of the soft tissue will reach predetermined levels.

[0123] Method 600 may also include generating one or more models of the subject's anatomical geometry (e.g., soft tissue), as in 618. The model can be determined at least in part based on a run of the FEM. Therefore, the model can be determined at least in part based on the anatomical geometry (from 602), one or more parameters (from 604), the received material properties (from 606), the characterized material properties (from 608), or a combination thereof. Examples of models are shown in... Figures 7 to 11 As shown in the image.

[0124] Figure 7 Models of a soft tissue segment 710 without a healed injury area 712, a soft tissue segment 720 with a 2 mm × 5 mm healed injury area 722, a soft tissue segment 730 with a 4 mm × 5 mm healed injury area 732, and a soft tissue segment 740 with a 6 mm × 5 mm healed injury area 742 are shown according to some aspects disclosed herein.

[0125] The soft tissue segment 710 can be represented as a unidirectional laminar volume (monolithic volume) (e.g., 8 layers), where ∈ 11 The orientation is along the length of the sample, and the soft tissue segments 720, 730, and 740 can be represented by quasi-isotropic (or quasi-symmetric) laminates only in the healed injury areas 722, 732, and 742. The quasi-symmetric laminate representation is numerically represented by modeling a stack of (45, 90, 45, 0) laminates, which implies that the laminates in areas 722, 732, and 742 are isotropic in the sample plane. The material monolaminate lamellar property values ​​are ∈ 11 =140MPa, ∈22=1MPa, and ∈33=1MPa, and vs=0.497* (incompressible), G12=70MPa (approximate), G13=70MPa (approximate), G23=70MPa (approximate). Engineering elastic constants material properties ∈ 11 ∈22, ∈33, and can be obtained from medical log publications vs, and G12, G13 and G23 can be approximated as ∈ 11 The value is half of the value. To perform the prediction, all nine simple engineering elastic constants of the material properties are required as FEM inputs. Classical laminate theory can be used to calculate the effective elastic properties.

[0126] Figure 8 A model of strain on soft tissue segments 710, 720, 730, and 740 is shown according to some aspects disclosed herein. The load is equal to 5% of the strain displacement along the length (e.g., 2.5 mm). The maximum principal mechanical strain is parallel to the load direction, as indicated by the arrow. Tendon tissue can be considered as a composite material.

[0127] Figure 9Models of a soft tissue segment 910 having a 3 mm slot tear 912 and a soft tissue segment 920 having a 2 mm V-shaped tear 922 are shown, according to some aspects disclosed herein.

[0128] Figure 10 A model of strain on soft tissue segments 910, 920 is shown according to some aspects disclosed herein. The load is equal to 5% strain displacement along the length (e.g., 2.5 mm). The maximum mechanical principal strain is perpendicular to the load direction, as indicated by the arrow. Due to material properties in the perpendicular direction, the mechanical strain may be larger, which can lead to tearing. Shoulder joint instability can generate more forces on the rotator cuff tendons, forces designed to move the shoulder joint, rather than stabilize it. This can lead to tendon degeneration and weakening, making them more prone to tearing. Rotator cuff tears are a symptom of shoulder joint instability and are a potentially missed diagnosis.

[0129] Figure 11 A graph 1100 is a plot of one or more SN fatigue curves 1110, 1120 based on aspects disclosed herein, including the subject's anatomical geometry (e.g., soft tissue). Graph 1100 plots the magnitude of cyclic stress (S) relative to the number of failure cycles (N) on a logarithmic scale. In other words, graph 1100 can show the number of predicted failure cycles for a given repetitive loading pattern. In one example, SN fatigue curve 1110 can represent a healthy tendon for a given task, and SN fatigue curve 1120 can represent a damaged tendon for a given task. In another example, SN fatigue curve 1110 can represent no vibration during a given task, and SN fatigue curve 1120 can represent vibration applied during a given task. Incorporating multiple inputs to varying degrees can result in more complex curves, which can be overlaid for comparison. Curves can also be generated for different task postures (e.g., 25% over-the-top extension, 50% over-the-top extension, 75% over-the-top extension, 100% over-the-top extension, sitting, standing, etc.).

[0130] In addition to or in addition to SN fatigue curves, FEM can output assessments of one or more (e.g., four) states of tissue damage: no damage, sub-rupture, partial damage, and complete damage. FEM can also, or alternatively, output comparisons of various variations in work processes and tools (e.g., assessments of reduced range of motion, power tools of different weights and vibration frequencies, the introduction of ergonomic aids, etc.), which can lead to optimized task design guidelines and / or recommended work intervals for a given task.

[0131] Method 600 may also include providing recommendations, as in 620. Recommendations may be based at least in part on the operation of the FEM (from 614), one or more modes (from 616), a model (from 618), or both. For example, recommendations may be based at least in part on stress, strain, or both in the soft tissue. Recommendations may be specifically tailored to the subject's anatomical geometry. In one implementation, recommendations may be specifically tailored to a particular subject (e.g., whose parameters are measured in 604). In another implementation, recommendations may also be specifically tailored to different (e.g., all) subjects who can perform the task. Recommendations may provide limitations on the task being performed to prevent the subject from damaging soft tissue. For example, recommendations may limit the amount of weight to be lifted during the task, the number of times the task is performed (e.g., repetitions), the amount of time the task is performed, or combinations thereof.

[0132] As a result, the recommendations can help improve repetitive stress injuries in workers performing part of a manufacturing process. Alternatively, method 600 can be used to improve repetitive stress injuries in athletes performing training programs. The athlete may have one or more exercises that form part of the training program. The exercises can be modified by the recommendations generated by method 600. Generally, method 600 can be implemented to improve repetitive stress injuries in any type of process, including repetitive movements of a person, not limited to manufacturing or sports processes.

[0133] Method 600 can be used to improve damage to any of a variety of soft tissues. According to some examples, method 600 can be used to improve damage to tendons or tendon complexes. (See above for reference.) Figures 1 to 3 Examples of such tendons and tendon complexes are presented. Alternatively, method 600 can be used to improve damage to connective tissue or musculoskeletal soft tissue. Typically, non-limiting examples of soft tissues from which the examples can be implemented include tendons, tendon complexes, intervertebral (spinal) discs, and ligaments.

[0134] Method 600 may further include comparing the FEM output with empirical studies, as in 622. For example, this could include comparing the mode (from 616) and / or the model (from 618) with repeatable stress data from empirical studies. This can help verify the accuracy of the FEM output. Method 600 can then return to step 614, where the FEM can be performed or run again (iteratively). In one implementation, the FEM can be modified (e.g., refined) at least in part based on the comparison (from 622) so that additional iterations of the FEM produce different (e.g., more accurate) outputs.

[0135] This disclosure also provides various systems and computer program products or machine-readable media. In some aspects, for example, the methods described herein may optionally be performed or facilitated using at least in part 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 logical instructions), etc. For illustration, Figure 12 A schematic diagram of an exemplary system suitable for use with at least several aspects of implementing the methods disclosed in this application is provided. As shown, system 1200 includes at least one controller or computer, such as server 1205 (e.g., a search engine server), which includes processor 1204 and memory storage device or memory 1206, and one or more other communication devices 1214 (e.g., client computer terminals, telephones, tablets, laptops, other mobile devices, etc.), which are located remotely from server 1205 and communicate with server 1205 via electronic communication network 1212 (such as the Internet or other interconnected networks). Communication devices 1214 typically include electronic displays (e.g., Internet-enabled computers, etc.) that communicate with, for example, the computer 1205 via network 1212, wherein the electronic displays include user interfaces (e.g., graphical user interface (GUI), web-based user interface, etc.) for displaying results when implementing the methods described herein. In some aspects, the communication network also includes physical transfer of data from one location to another, for example, using hard disk drives, thumb drives, or other data storage mechanisms. System 1200 also includes a program product 1208 stored on a computer or machine-readable medium, such as one or more of various types of memory (such as memory 1206 of server 1205), which can be read by server 1205 for execution by, for example, one or more other communication devices (such as communication device 1214) (e.g., schematically shown as a desktop or personal computer). In some aspects, system 1200 may optionally include at least one database server, such as server 1210 associated with an online website that stores data that is directly or searchable through server 1205 (e.g., control sample or comparison result data, indexed customized therapies, etc.). System 1200 may optionally include one or more other servers located remotely from server 1205, each server optionally associated with one or more database servers 1210 located remotely or locally on each other server. The other servers can advantageously serve geographically remote users and enhance geographically distributed operations.

[0136] As will be understood by those skilled in the art, the memory 1206 of server 1205 may optionally include volatile and / or non-volatile memory, including, for example, RAM, ROM, and disks or optical discs. Those skilled in the art should also understand that although illustrated as a single server, the configuration of server 1205 shown is given by way of example only, and other types of servers or computers configured according to various other methods or architectures may also be used. Figure 12 The server 1205 schematically shown represents a server, server cluster, or server group and is not limited to any single physical server. A 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 usage, needs, and capacity requirements of system 1200. Also, as understood by those skilled in the art, in these respects, communication devices 1214 (e.g., computers) can be, for example, laptop computers, desktop computers, tablet computers, personal digital assistants (PDAs), mobile phones, servers, or other types of computers. As known and understood by those skilled in the art, network 1212 can include the Internet, intranet, telecommunications network, extranet, or a World Wide Web of multiple computers / servers communicating with one or more other computers via a communication network and / or a local area network or other regional network.

[0137] As will be further understood by those skilled in the art, the exemplary program product or program product 1208 may optionally take the form of microcode, a program, a cloud computing format, a routine, and / or a symbolic language that provides control over hardware functions and directs its operation. According to the exemplary aspect, program product 1208 does not necessarily need to reside entirely in volatile memory, but may be selectively loaded as needed, according to various methods known and understood by those skilled in the art.

[0138] As will be further understood by those skilled in the art, the terms "computer-readable medium" or "machine-readable medium" refer to any medium that participates in providing instructions to a processor for execution. For illustration, the terms "computer-readable medium" or "machine-readable medium" include distributed media, cloud computing formats, intermediate storage media, computer execution memory, and any other media or devices capable of storing program product 1208 that implements the functions or processes of various aspects of this disclosure (e.g., for reading by a computer). "Computer-readable medium" or "machine-readable medium" can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical discs 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 forming 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 transmission. Exemplary forms of computer-readable media include floppy disks, floppy disks, hard disks, magnetic tapes, flash drives or any other magnetic media, CD-ROMs, any other optical media, punched cards, paper tapes, any other physical media with a perforated pattern, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves or any other media that a computer can read.

[0139] Program product 1208 may optionally be copied from a computer-readable medium to a hard disk or similar intermediate storage medium. When program product 1208 or portions thereof are to be run, it may optionally be loaded from its distribution medium, its intermediate storage medium, etc., into the execution memory of one or more computers, configuring the computers to operate according to various functions or methods. All these operations are well known to those skilled in the art, for example.

[0140] For further illustration, in some aspects, this application provides a system comprising one or more processors and one or more memory components communicating with the processors. The memory components may include one or more instructions that, when executed, cause the processors to perform operations. Operations may include receiving one or more images including the anatomical geometry of a subject. Operations may also include using one or more sensors attached to the subject to measure or receive multiple parameters of the subject's anatomical geometry. Operations may also include receiving a first set of material properties of the subject's soft tissue. Operations may also include identifying a second set of material properties characterizing the soft tissue as the subject performs a task. Operations may also include determining strain, stress, or both on the soft tissue, at least in part, based on one or more images, parameters, the first set of material properties, and the second set of material properties.

[0141] System 1200 typically also includes additional system components configured to perform various aspects of the methods described herein. In some aspects, one or more of these additional system components are located remotely from server 1205 and communicate with server 1205 via electronic communication network 1212, while in other aspects, one or more of these additional system components are located locally and communicate with server 1205 (i.e., in the absence of electronic communication network 1212) or communicate directly with, for example, communication device 1214 (e.g., a computer).

[0142] Enumeration Clause

[0143] 1. A method (600) for modeling soft tissue (530, 532) may include: receiving one or more images (500) of an anatomical geometry including soft tissue (530, 532) of a first subject (502). The method may further include: measuring multiple parameters of the anatomical geometry of the first subject (502) using one or more sensors (520A, 520B, 520C) attached to the first subject (502). The method may further include: receiving a first set of material properties of the soft tissue of the first subject (502), a second subject, or both. The method may further include: identifying a second set of material properties characterizing the soft tissue when the first subject (502) performs a task, wherein the second set of material properties differs from the first set of material properties. The method may further include: determining strain, stress, or both on the soft tissue based at least in part on the one or more images (500), the parameters, the first set of material properties, and the second set of material properties.

[0144] 2. The method (600) according to Clause 1, wherein the image (500) includes a magnetic resonance image, a computer-generated tomography image, an ultrasound image, or a combination thereof, and wherein the first subject (502) includes a living mammal.

[0145] 3. The method (600) according to Clause 1 or Clause 2, wherein the parameter is measured while the first subject (502) performs the task.

[0146] 4. The method (600) according to any one of clauses 1 to 3, wherein the parameters include force, posture, repetition, duration, vibration or a combination thereof.

[0147] 5. The method (600) according to any one of clauses 1 to 4, wherein the parameters describe the behavior of the soft tissue in response to the first subject (502) being exposed to force, posture, repetition, duration, vibration or a combination thereof during the performance of the task.

[0148] 6. The method (600) according to any one of clauses 1 to 5, wherein the first set of material properties includes in-plane modulus, out-of-plane modulus and Poisson's ratio.

[0149] 7. The method (600) according to any one of clauses 1 to 6, wherein the second set of material properties includes isotropic properties of the soft tissue, anisotropic properties of the soft tissue, nonlinear behavior of the soft tissue, and estimated damage state of the soft tissue.

[0150] 8. The method (600) according to any one of clauses 1 to 7, the method further comprising: generating a model (710, 720, 730, 740, 910, 920, 1100) describing the soft tissue, at least in part based on the strain, the stress, or both.

[0151] 9. The method (600) according to Clause 8, wherein the model (710, 720, 730, 740, 910, 920, 1100) includes a 3D model of the soft tissue, and wherein the model (710, 720, 730, 740, 910, 920) identifies the direction of the load on the soft tissue during the task and the direction of the strain, the direction of the stress, or both the direction of the strain and the direction of the stress.

[0152] 10. The method (600) according to Clause 8, wherein the model (1100) includes an SN fatigue curve that predicts the number of cycles performed by the first subject (502) during the task prior to damage to the soft tissue.

[0153] 11. A method (600) for modeling soft tissue (530, 532) may include: receiving one or more images (500) comprising an anatomical geometry of a subject (502), wherein the images (500) include magnetic resonance images, computed tomography images, ultrasound images, or combinations thereof, wherein the anatomical geometry comprises soft tissue (530, 532), and wherein the subject (502) comprises a living mammal. The method may further include: measuring a plurality of parameters of the anatomical geometry of the subject (502), wherein the parameters are measured using one or more sensors (520A, 520B, 520C) attached to the subject (502), wherein the parameters are measured while the subject (502) is performing a task, and wherein the parameters include force, posture, repetition, duration, and vibration. The method may further include: receiving a first set of material properties of the soft tissues (530, 532) of the subject (502), wherein the first set of material properties includes in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. The method may further include: identifying a second set of material properties characterizing the soft tissues (530, 532) during the task, wherein the second set of material properties differs from the first set of material properties, wherein the second set of material properties includes isotropic properties of the soft tissues (530, 532), anisotropic properties of the soft tissues (530, 532), nonlinear behavior of the soft tissues (530, 532), estimated damage state of the soft tissues (530, 532), or a combination thereof. The method may further include: running a finite element model based at least in part on the one or more images (500), the parameters, the first set of material properties, and the second set of material properties. The method may further include: determining, at least in part, the strain, stress, or both, on the soft tissue (530, 532) based on the operation of the finite element model. The method may further include: generating models (710, 720, 730, 740, 910, 920, 1100) describing the soft tissue (530, 532) based at least in part on the determined strain, the determined stress, or both.

[0154] 12. The method (600) according to Clause 11, wherein the first set of material properties are measured using shear wave elastography, ultrasound, or both.

[0155] 13. The method (600) according to Clause 11 or Clause 12, wherein the second set of material properties is measured using external imaging techniques, material sample combination techniques, or both.

[0156] 14. The method (600) according to any one of clauses 11 to 13, wherein the model (710, 720, 730, 740, 910, 920, 1100) comprises: a first SN fatigue curve (1110) describing the soft tissue (530, 532) in a healthy state; and a second SN fatigue curve (1120) describing the state of the soft tissue (530, 532) in response to the task.

[0157] 15. The method (600) according to any one of clauses 11 to 14, the method further comprising: providing a recommendation that provides limitations for the task performed by the subject (502) to prevent the subject (502) from damaging the soft tissues (530, 532), wherein the recommendation is based at least in part on the stress on the soft tissues (530, 532), the strain on the soft tissues (530, 532), or both.

[0158] 16. A system for characterizing the behavior of soft tissues (530, 532) in mammals may include a plurality of sensors (520A, 520B, 520C) configured to be attached to a human subject (502), wherein the sensors (520A, 520B, 520C) are configured to measure a plurality of parameters as the human subject (502) performs a repetitive task, and wherein the parameters include force exertion, posture, repetition, duration, and vibration. The system may further include a computing system (1200) configured to perform an operation. The operation may include receiving one or more images (500) including anatomical geometry of the human subject (502), wherein the images (500) include magnetic resonance images, computed tomography images, and ultrasound images, wherein the anatomical geometry includes soft tissues (530, 532). The operation may further include receiving the parameters from the sensors (520A, 520B, 520C). The operation may further include: receiving a first set of material properties of the soft tissues (530, 532) of the human subject (502), wherein the first set of material properties includes in-plane linear modulus, out-of-plane linear modulus, and Poisson's ratio. The operation may further include: identifying a second set of material properties characterizing the soft tissues (530, 532) during the repetitive task, wherein the second set of material properties differs from the first set of material properties, wherein the second set of material properties includes isotropic properties of the soft tissues (530, 532), anisotropic properties of the soft tissues (530, 532), nonlinear behavior of the soft tissues (530, 532), and an estimated damage state of the soft tissues (530, 532). The operation may further include: running a finite element model based at least in part on the one or more images (500), the parameters, the first set of material properties, and the second set of material properties. The operation may further include: predicting strain modes, stress modes, vibration modes, and failure modes of the soft tissues (530, 532) based at least in part on the operation of the finite element model. The operation may further include: generating models (710, 720, 730, 740, 910, 920, 1100) describing the soft tissues (530, 532) based at least in part on the strain modes, stress modes, vibration modes, failure modes, or combinations thereof.

[0159] 17. The system according to Clause 16, wherein the first set of material properties is measured using shear wave elastography, ultrasound, or both.

[0160] 18. The system according to Clause 16 or Clause 17, wherein the second set of material properties is measured using dynamic mechanical analysis.

[0161] 19. The system according to any one of Clauses 16 to 18, wherein the model (710, 720, 730, 740, 910, 920, 1100) comprises: a first SN fatigue curve (1110) describing the soft tissue (530, 532) prior to performing the repetitive task; and a second SN fatigue curve (1120) describing the soft tissue (530, 532) in response to performing the repetitive task.

[0162] 20. The system according to any one of clauses 16 to 19, the system further comprising: providing a recommendation that provides limitations for the repetitive task performed by the human subject (502) to prevent the human subject (502) from damaging the soft tissue (530, 532), wherein the recommendation is based at least in part on the strain mode, the stress mode, the vibration mode and the failure mode.

[0163] All documents described herein are incorporated herein by reference, including any priority documents and / or test procedures, up to the extent that they are inconsistent with this text. As will be apparent from the foregoing general description and specific aspects, while forms of various aspects have been illustrated and described, various modifications may be made without departing from the spirit and scope of this disclosure. Therefore, this disclosure is not intended to be limited thereto. Similarly, the term “comprising” is considered synonymous with the term “including.” Likewise, whenever a composition, element, or group of elements is preceded by the transitional phrase “comprising,” it should be understood that we also consider the same composition or group of elements preceding the description of that composition, element, or group of elements with the transitional phrases “consistently composed of,” “composed of,” “selected from the group of,” or “I,” and vice versa; for example, the terms “comprising,” “consistently composed of,” and “composed of” also include the product of a combination of elements listed after the term.

[0164] Although the foregoing disclosure has been described in some detail by way of example and illustration for clarity and understanding, it will be apparent to those skilled in the art, upon reading this disclosure, that various changes in form and detail may be made without departing from the true scope of this disclosure, and that it may be practiced 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 were specifically and separately indicated to be incorporated by reference.

Claims

1. A method for modeling soft tissue, the method comprising the following steps: Receive one or more images including the anatomical geometry of a first subject, wherein the anatomical geometry includes soft tissue; Multiple parameters of the anatomical geometry of the first subject are measured using one or more sensors attached to the first subject; The first set of material properties of the soft tissue received by the first subject, the second subject, or both; A second set of material properties characterizing the soft tissue when the first subject performs a task is identified, wherein the second set of material properties differs from the first set of material properties, and wherein the task includes a repetitive task as part of an assembly line. The strain, stress, or both on the soft tissue are determined, at least in part, based on one or more of the images, the parameters, the first set of material properties, and the second set of material properties; and In response to the strain, the stress, or both, the execution of the task is modified, wherein the task is modified to limit the number of times the task is executed or the amount of time the task is executed.

2. The method according to claim 1, wherein, The images include magnetic resonance images, computer-generated tomography images, ultrasound images, or combinations thereof, and wherein the first subject includes a living mammal.

3. The method according to claim 1 or claim 2, wherein, The parameter is measured while the first subject performs the task.

4. The method according to claim 2, wherein, The parameters include force, posture, repetition, duration, vibration, or a combination thereof.

5. The method according to claim 3, wherein, The parameters describe the behavior of the soft tissue in response to the first subject's exposure to force, posture, repetition, duration, vibration, or a combination thereof during the performance of the task.

6. The method according to claim 1 or claim 2, wherein, The first set of material properties includes in-plane modulus, out-of-plane modulus, and Poisson's ratio.

7. The method according to claim 1 or claim 2, wherein, The second set of material properties includes the isotropic properties of the soft tissue, the anisotropic properties of the soft tissue, the nonlinear behavior of the soft tissue, and the estimated damage state of the soft tissue.

8. The method according to claim 1 or claim 2, further comprising the following step: A model describing the soft tissue is generated, at least in part based on the strain, the stress, or both.

9. The method according to claim 8, wherein, The model includes a 3D model of the soft tissue, and wherein the model identifies the direction of the load on the soft tissue during the task, as well as the direction of the strain, the direction of the stress, or both the direction of the strain and the direction of the stress.

10. The method according to claim 8, wherein, The model includes an SN fatigue curve, which predicts the number of cycles performed by the first subject during the task before the soft tissue is damaged.

11. A method for modeling soft tissue, the method comprising the following steps: Receive one or more images including the anatomical geometry of a subject, wherein the images include magnetic resonance images, computed tomography images, ultrasound images, or combinations thereof, wherein the anatomical geometry includes soft tissue, and wherein the subject includes a living mammal; Multiple parameters of the subject's anatomical geometry are measured, wherein the parameters are measured using one or more sensors attached to the subject, wherein the parameters are measured while the subject is performing a task, wherein the task includes a repetitive task as part of an assembly line, and wherein the parameters include force, posture, repetition, duration, and vibration. The first set of material properties of the soft tissue received by the subject includes in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. A second set of material properties characterizing the soft tissue during the task is identified, wherein the second set of material properties differs from the first set of material properties, wherein the second set of material properties includes isotropic properties of the soft tissue, anisotropic properties of the soft tissue, nonlinear behavior of the soft tissue, estimated damage state of the soft tissue, or a combination thereof. The finite element model is run at least in part based on the one or more images, the parameters, the first set of material properties, and the second set of material properties; Based at least in part on the operation of the finite element model, the strain, stress, or both on the soft tissue are determined. A model describing the soft tissue is generated, at least in part, based on the determined strain, the determined stress, or both; and In response to the model, the execution of the task is modified, wherein the task is modified to limit the number of times the task is executed or the amount of time the task is executed.

12. The method according to claim 11, wherein, The first set of material properties were measured using shear wave elastography, ultrasound, or both.

13. The method according to claim 11 or claim 12, wherein, The second set of material properties was measured using external imaging techniques, material sample combination techniques, or both.

14. The method according to claim 11 or claim 12, wherein, The model includes: A first SN fatigue curve, the first SN fatigue curve describing the soft tissue in a healthy state; and The second SN fatigue curve describes the state of the soft tissue in response to the task.

15. The method according to claim 11 or claim 12, further comprising the step of: A recommendation is provided that provides limitations for the task performed by the subject to prevent the subject from damaging the soft tissue, wherein the recommendation is based at least in part on the stress, strain, or both on the soft tissue.

16. A system for characterizing the behavior of soft tissues in mammals, the system comprising: Multiple sensors are configured to attach to a human subject, wherein the sensors are configured to measure multiple parameters while the human subject performs a repetitive task, wherein the repetitive task is part of an assembly line, and wherein the parameters include force exertion, posture, repetition, duration, and vibration; and A computing system configured to perform operations, the operations including: Receive one or more images including the anatomical geometry of the human subject, wherein the images include magnetic resonance images, computed tomography images, and ultrasound images, wherein the anatomical geometry includes soft tissue; Receive the parameters from the sensor; The first set of material properties of the soft tissue of the human subject is received, wherein the first set of material properties includes in-plane linear modulus, out-of-plane linear modulus, and Poisson's ratio; A second set of material properties characterizing the soft tissue during the repetitive task is identified, wherein the second set of material properties differs from the first set of material properties, wherein the second set of material properties includes the isotropic properties of the soft tissue, the anisotropic properties of the soft tissue, the nonlinear behavior of the soft tissue, and the estimated damage state of the soft tissue. The finite element model is run at least in part based on the one or more images, the parameters, the first set of material properties, and the second set of material properties; Based at least in part on the operation of the finite element model, the strain mode, stress mode, vibration mode and failure mode of the soft tissue are predicted; A model describing the soft tissue is generated, at least in part based on the strain mode, the stress mode, the vibration mode, the failure mode, or a combination thereof; and In response to the model, the execution of the repetitive task is modified, wherein the task is modified to limit the number of times the repetitive task is executed or the amount of time to execute the repetitive task.

17. The system according to claim 16, wherein, The first set of material properties were measured using shear wave elastography, ultrasound, or both.

18. The system according to claim 16 or claim 17, wherein, The second set of material properties were measured using dynamic mechanical analysis.

19. The system according to claim 16 or claim 17, wherein, The model includes: A first SN fatigue curve, the first SN fatigue curve describing the soft tissue prior to performing the repetitive task; and The second SN fatigue curve describes the soft tissue in response to performing the repetitive task.

20. The system according to claim 16 or claim 17, further comprising: Recommendations are provided that limit the repetitive tasks performed by the human subjects to prevent damage to the soft tissues, wherein the recommendations are based at least in part on the strain mode, the stress mode, the vibration mode, and the failure mode.

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