Combining multiple ergonomic risk factors in a single, predictable finite model.

Abstract

A method for modeling soft tissue includes receiving one or more images depicting an anatomical geometry of a first object. The anatomical geometry includes soft tissue. The method also includes measuring a plurality of parameters of the anatomical geometry of the first object using one or more sensors attached to the first object. The method also includes receiving a first set of material properties for soft tissue of the first object, the second object, or both. The method also includes identifying a second set of material properties characterizing the soft tissue while the first subject is 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

Technical Field

[0001] [Cross - Reference to Related Applications]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 180,353, filed Apr. 27, 2021, which is hereby incorporated by reference in its entirety.

[0002] [Technical Field]

[0002] The subject matter described herein generally relates to the field of materials science and its application to ergonomics. More specifically, the subject matter disclosed herein relates to repetitive stress injuries of soft tissue materials and guidelines for minimizing the risk of such injuries.

Background Art

[0003]

[0003] Injuries due to overuse, particularly shoulder injuries including the supraspinatus tendon, are one of the most significant injuries based on ergonomics. Therefore, in order to address the tasks or task repetitions that cause the injury, it is necessary to understand, for example, for the tendon, the mechanics of workplace or other activities that cause stress or can bring about stress. For that understanding, information has been drawn from several research fields including medical devices, surgical techniques, medicine, engineering, materials science, and ergonomics. Furthermore, these research fields are not currently intersecting in a form where models and guidelines for material repetitive fatigue injuries can be easily created (or they are being developed). This is shown by a series of research and its focus. For example, research in medicine, therapy, and pharmacology is specialized (or focused) on individuals after an injury has occurred, which is, for example, surgical procedures, physical therapy, and treatments for accelerating recovery. The detection of injuries is focused after the patient self - reports the injury and does not screen for risks before the injury. There is still a need for models that can be used to create guidelines for reducing or improving soft tissue injuries due to workplace or other occupational and recreational activities.

Summary of the Invention

[0004]

[0004] This disclosure relates to systems for generating tendon injury models and related methods. Typically, these systems and related methods involve creating guidelines for mitigating or minimizing repetitive injuries to tendon materials.

[0005]

[0005] Various examples disclose a method to reduce the possibility of repetitive stress injuries to soft tissue when performing a process. This method Obtain at least one iterative stress dataset related to soft tissue and process; Accessing at least first information characterizing a first damage segment and second information characterizing a second damage segment, wherein the first information quantifies the number of repetitions at a given stress for the soft tissue to transition from the first damage segment, and the second information quantifies the number of repetitions at a given stress for the soft tissue to transition from the second damage segment; To predict conditions sufficient for soft tissue damage based on at least the first set of information, the second set of information, and repeated stress datasets; Based on such predictions, determine at least one guideline for mitigating the risk of repeated stress damage to soft tissue materials; and Implement at least one guideline in the process. Includes.

[0006]

[0006] The various optional features of the above example include the following: The first and second damage regimes may each include one of the following: No Damage Regime, Sub-Rupture Damage Regime, or Tear Propagation Regime. The method may include accessing third information characterizing a third damage regime, where the third information quantifies the number of iterations at a given stress for the soft tissue to transition from the third damage regime, where the first damage regime includes the No Damage Regime, the second damage regime includes the Sub-Rupture Damage Regime, and the third damage regime includes the Tear Propagation Regime. The iterative stress dataset may include forces across the cross-sectional area of ​​the soft tissue and at least one of the number of iterations or duration for at least one task of the process. Obtaining at least one iterative stress dataset related to the soft tissue and process may include estimating at least one stress distribution within the soft tissue. At least one guideline may include limitations relating to 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 for which the soft tissue maintains a given posture, the duration for which the soft tissue is subjected to repeated movements, or the duration for which a given force is applied to the soft tissue. Accessing at least first information characterizing a first injury segment and second information characterizing a second injury segment may include obtaining at least one of the following: ultrasound data, computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling (MDM) data, or published data.The soft tissues 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 magnus tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps femoris tendon, patellar tendon, hamstring tendons, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, flexor digitorum longus tendon, interosseous tendons, flexor digitorum profundus tendon, abductor digiti minimi tendon, opponens pollicis tendon, and flexor pollicis longus tendon. This may include the tendons of the extensor muscles, abductor pollicis tendon, flexor hallucis longus tendon, flexor digitorum brevis tendon, lumbrical muscles, abductor hallucis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, ocular muscles, levator palpebrae superioris tendon, masseter muscle tendon, temporalis muscle tendon, trapezius muscle tendon, sternocleidomastoid muscle tendon, semispinalis capitis muscle tendon, splenius capitis muscle tendon, mylohyoid muscle tendon, thyrohyoid muscle tendon, sternohyoid muscle tendon, rectus abdominis muscle tendon, external oblique muscle tendon, transversus abdominis muscle tendon, latissimus dorsi muscle tendon, or erector spinae muscle tendons. This method may include individualizing at least one guideline for a subject by applying one or more demographic variables of the subject to at least one guideline. This method may include obtaining one or more use datasets of the target soft tissue and estimating the damage to the target soft tissue by comparing the use datasets with at least one guideline.

[0007]

[0007] Various examples disclose computer systems for mitigating repetitive stress damage to soft tissue during the execution of a process. The system includes at least one electronic processor that executes instructions for performing an operation, the operation being: Obtain at least one iterative stress dataset related to soft tissue and process; Accessing at least first information characterizing a first damage segment and second information characterizing a second damage segment, wherein the first information quantifies the number of repetitions at a given stress for the soft tissue to transition from the first damage segment, and the second information quantifies the number of repetitions at a given stress for the soft tissue to transition from the second damage segment; Based on at least the first piece of information, the second piece of information, and the repeated stress dataset, predict conditions sufficient for soft tissue damage, and; Based on the said prediction, at least one guideline for mitigating the risk of repeated stress damage to soft tissue materials is determined, and at least one guideline is implemented in the said process. Includes.

[0008]

[0008] The various optional functions of the above example include the following: The first and second damage categories may each include one of the following: an undamaged category, a partially fractured damage category, or a fracture propagation category. The above operation may further include accessing third information characterizing a third damage category, where the third information quantifies the number of iterations at a given stress for the soft tissue to transition from the third damage category, where the first damage category includes an undamaged category, the second damage category includes a partially fractured damage category, and the third damage category includes a fracture propagation category. The iterative stress dataset may include forces across the cross-sectional area of ​​the soft tissue and at least one of the number of iterations or duration for at least one task of the process. Obtaining at least one iterative stress dataset related to the soft tissue and the process may include estimating at least one stress distribution within the soft tissue. At least one guideline may include limitations relating to 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 for which the soft tissue maintains a given posture, the duration for which the soft tissue is subjected to repeated movements, or the duration for which a given force is applied to the soft tissue. Accessing at least first information characterizing a first injury segment and second information characterizing a second injury segment may include obtaining at least one of the following: ultrasound data, computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive testing data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling (MDM) data, or published data.The soft tissues 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 magnus tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps femoris tendon, patellar tendon, hamstring tendons, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, and long This may include the tendons of the flexor muscles of the fingers, interosseous muscles, flexor muscles of the fingers deep, abductor digiti minimi tendon, opponens pollicis tendon, flexor pollicis longus tendon, extensor muscles, abductor pollicis tendon, flexor hallucis longus tendon, flexor muscles of the fingers brevis, lumbrical muscles, abductor hallucis tendon, flexor muscles of the fingers longus, abductor digiti minimi tendon, ocular muscles, levator palpebrae superioris tendon, masseter muscle tendon, temporalis muscle tendon, trapezius muscle tendon, sternocleidomastoid muscle tendon, semispinalis capitis muscle tendon, splenius capitis muscle tendon, mylohyoid muscle tendon, thyrohyoid muscle tendon, sternohyoid muscle tendon, rectus abdominis muscle tendon, external oblique muscle tendon, transversus abdominis muscle tendon, latissimus dorsi muscle tendon, or erector spinae muscle tendons. Soft tissue is not limited to tendons. Soft tissue may also include ligaments, intervertebral discs, muscles, and skin, or ligaments, intervertebral discs, muscles, and skin may be included in place of the above. For example, using the example of a lumbar spine injury, both the intervertebral discs and tendons are made of soft tissue (e.g., type II collagen vs. type I / III collagen). Therefore, by updating the model input and importing different anatomical structures, the operation can be extended to other types of soft tissue, such as intervertebral discs. Bone properties can also be incorporated into the definition of the shoulder region. As a result, it becomes possible to investigate fractures in other parts of the body. Analysis of muscle tissue, including fibers in the primary loading direction, can follow a similar methodology to that of tendons. The operation may further include individualizing at least one guideline for a subject by applying one or more demographic variables of the subject to at least one guideline. The operation may further include obtaining one or more use datasets for the soft tissue of the subject, and estimating the injury to the soft tissue of the subject by comparing the use datasets with at least one guideline.

[0009]

[0009] In one embodiment, the present disclosure provides a method for generating a tendon injury model (e.g., a tendon injury accumulation model). The method comprises generating one or more SN curves for one or more physical segments of at least one tendon from one or more SN curve datasets, wherein a given SN curve includes a plot of the magnitude of stress applied to the tendon against the number of repetitions until tendon failure. The method may also include generating one or more iterative stress datasets describing a tendon, and combining the SN curves or data derived from said SN curves, wherein a tendon injury model is generated by using iterative stress datasets to predict tendon injury under one or more conditions (e.g., a physical or specific representation or information of tendon injury under a given set of conditions). However, it should be noted that the physical mechanism by which damage accumulates in biomaterials such as collagen-based tendons is significantly different from the physical mechanism of conventional crystalline materials based on SN-type behavioral models. The latter common model is the accumulation of damage due to the accumulation of dislocations in (usually) metal particles or sliding obstacles within the crystal. As cycles repeat, dislocations occur in discontinuities in the microstructure of structural materials, gradually accumulating as they collide with obstacles. Even if the macroscopic stress is well below the material's yield strength, continuous periodic loading will eventually cause small cracks to form at the accumulation points. These then behave as material defects and eventually grow into cracks. Conventional approaches have attempted to model fatigue in soft tissues, but such attempts lead to inaccurate results because they treat soft tissues as if they had the fatigue properties of metals. However, while SN-type behavior is observed in the fatigue and cracking mechanisms of metals, there is no inherent similarity to collagen-based tendons subjected to similar periodic loading. This is because tendons lack dislocations that adapt to local plastic deformation. Instead, tendons respond to periodic loading through local stretching and twisting of collagen fibers, which can be considered a form of micro-damage. Such damage is repaired by the body through collagen reconstruction, but this is time-dependent and influenced by the body's healing process.Therefore, an effective model of damage accumulation in tendon structures incorporates the following: a) presentation of micro-damage accumulation prior to detectable defects (or ruptures); b) presentation of healing processes that counteract micro-damage at competing rates; c) presentation of a chain and expansion of micro-damage that results in detectable macroscopic defects in the tendon, usually presented as ruptures or cracks in the collagen structure, which may occur simultaneously with pain; d) presentation of expansion and growth of such ruptures or cracks over time (da / dn) when continuous periodic loading is applied above the critical threshold stress intensity Kc; and e) presentation of total loading cycles that result in catastrophic separation or de facto failure of the tendon bearing the load. This latter e) is a typical mode of data for SN fatigue types. The model components for each of the above processes identify four categories of damage accumulation: 1) no damage; 2) micro-damage (partial rupture) accumulation; 3) damage accumulation in the form of growing ruptures or cracks, cellular damage, or other biological damage; and 4) a state of catastrophic damage or separation of the tendon structure. These model components can be presented mathematically and integrated into a set model. Such a set model can also be verified by inspection or query methods capable of identifying the presence and extent of progressive micro-damage and fractures.

[0010]

[0010] In another aspect, the present disclosure provides a method for generating guidelines to avoid the accumulation of repetitive stress and / or tendon damage in tendon materials. The method comprises generating one or more SN curves for one or more physical segments of at least one tendon from one or more SN curve datasets, wherein a given SN curve includes a plot of the magnitude of the stress applied to the tendon and the number of repetitions until the tendon reaches a damage segment transition. The method may also comprise generating one or more repetitive stress datasets describing a tendon, and combining the SN curves or data derived from such SN curves, wherein the repetitive stress datasets are used to predict tendon damage under one or more conditions, and a tendon damage model is created. Furthermore, the method may also include generating at least one guideline regarding the repetitive stress on the tendon material of a tendon from a tendon injury model, wherein the guideline includes the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of the given posture of the tendon, the duration of the repetitions of a given movement of the tendon, the duration of the given force applied to the tendon, and combinations thereof, thereby generating a tendon injury model which is used to obtain at least one guideline.

[0011]

[0011] The methods of the present disclosure include various embodiments. In some embodiments, for example, the method includes combining multiple SN curves of physical sections to create at least one combined SN curve. In certain embodiments, the method includes combining an SN curve or data derived from such SN curve with a set of repeated stress datasets and applying at least one cumulative injury model to predict tendon injury under one or more conditions. In some embodiments, the method includes obtaining an SN curve dataset using one or more data sources, including medical diagnostic techniques such as ultrasound data, 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, published data, and combinations thereof.

[0012]

[0012] In certain embodiments, the tendons include the supraspinatus tendon. In some embodiments, the 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 magnus tendon, gluteus maximus tendon, gluteus medius tendon, quadriceps femoris tendon, patellar tendon, hamstring tendons, sartorius tendon, gastrocnemius tendon, Achilles tendon, soleus tendon, tibialis anterior tendon, peroneus longus tendon, and long finger. This includes flexor tendons, interosseous tendons, flexor digitorum profundus tendon, abductor digiti minimi tendon, opponens pollicis tendon, flexor pollicis longus tendon, extensor tendons, abductor pollicis tendon, flexor hallucis longus tendon, flexor digitorum brevis tendon, lumbrical tendons, abductor hallucis tendon, flexor digitorum longus tendon, abductor digiti minimi tendon, ocular tendons, 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 tendons, and combinations thereof. In some embodiments, the tendons include mammalian tendons. In certain embodiments of these, the mammalian tendons include human tendons. In other embodiments, the tendons include non-mammalian tendons.

[0013]

[0013] In certain embodiments, the method includes combining an SN curve or data derived from such SN curve, and a set of repeated stress datasets with healing data to predict tendon injury under one or more conditions. In some embodiments, the disclosure provides a method for predicting tendon injury in a subject, which includes obtaining one or more use datasets for at least one tendon in a subject, and comparing the use datasets with guidelines for repeated stress on the tendon material generated by the method, thereby predicting tendon injury in the subject. In certain embodiments, the physical classifications include undamaged classifications, partial fracture classifications, crack initiation classifications, fracture classifications, or fracture curves, and combinations thereof. In some embodiments, one or more steps are implemented at least partially by computer.

[0014]

[0014] In some embodiments, the method includes generating at least one guideline for repetitive stress on a tendon material from a tendon injury model, wherein the guideline includes the posture and / or position of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration for which the tendon maintains a given posture, the duration for which a given movement of the tendon is repeated, a given movement of the tendon, the duration for which a given force is applied to the tendon, and combinations thereof. In some of these embodiments, the guideline for repetitive stress on the tendon material includes one or more recommended use / rest cycles of the tendon under one or more sets of use conditions. In certain embodiments, the method includes verifying the guideline for repetitive stress on the tendon material. In some embodiments, the method includes personalizing the guideline for a given subject by applying one or more demographic variables of the subject to the guideline. In certain embodiments, the method includes using task information when generating the guideline. In certain embodiments, the task information includes tool weight and / or force vectors.

[0015]

[0015] In certain embodiments, the method includes estimating at least one stress distribution in a tendon to generate a set of repeated stress datasets. In some of these embodiments, the method includes estimating the stress distribution in a tendon using at least one dimension of the tendon. In some of these embodiments, the dimension includes at least one cross-sectional area of ​​the tendon. In some of these embodiments, the method includes estimating the stress distribution in a tendon using at least one cycle curve that includes a plot of at least one force applied to the tendon versus the number of repetitions until tendon failure. In certain embodiments, the force is determined in one or more positions of the tendon. In some of these embodiments, the method includes determining the force using estimation techniques and / or modeling techniques, such as finite element modeling (FEM) and / or electromyography (EMG). In some of these embodiments, the method includes using task information when determining the force applied to the position of the tendon. In some embodiments, the task information includes tool weight and / or force vectors.

[0016]

[0016] In other embodiments, the present disclosure provides a system comprising a controller having a computer-readable medium that includes non-temporary computer-executable instructions, or a controller that can access such computer-readable medium, which, when executed by at least one electronic processor, generates at least one or more SN curves for one or more physical regions of at least one tendon from one or more SN curve datasets, wherein a given SN curve includes a plot of the magnitude of stress applied to the tendon with the number of repetitions until tendon failure, and combines the SN curves or data derived from such SN curves with one or more repetitive stress datasets to generate a tendon injury model.

[0017]

[0017] In another aspect, the present disclosure provides a computer-readable medium that, when executed by at least one electronic processor, generates one or more SN curves for one or more physical regions of at least one tendon from one or more SN curve datasets, wherein a given SN curve includes a plot of the magnitude of stress applied to the tendon with the number of repetitions until tendon failure, and generates a tendon injury model by combining the SN curves or data derived from said SN curves with one or more repetitive stress datasets.

[0018]

[0018] In some embodiments, instructions of the system or computer-readable medium disclosed herein further perform combining multiple SN curves for a physical section to generate at least one combined SN curve. In certain embodiments, instructions of the system or computer-readable medium disclosed herein further perform at least: combining an SN curve or data derived from such SN curve with a set of repeated stress data to apply at least one cumulative injury model when predicting tendon injury under one or more conditions. In some embodiments, instructions of the system or computer-readable medium disclosed herein further perform at least: obtaining an SN curve set of data using one or more data sources, including medical diagnostic techniques (e.g., ultrasound data, computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive test data, cadaver material data, animal material data, polymer substitute material data, molecular dynamics modeling (MDM) data, or published data, and combinations thereof). In some embodiments, instructions in a system or computer-readable medium disclosed herein further perform at least the following: combine an SN curve or data derived from such SN curve, and a set of repeated stress datasets with healing data to predict tendon injury under one or more conditions. In certain embodiments, instructions in a system or computer-readable medium disclosed herein further perform at least the following: obtain one or more use datasets for at least one tendon of interest, and compare the use datasets with guidelines for repeated stress on tendon material to predict tendon injury of interest.

[0019]

[0019] In some embodiments, instructions of a system or computer-readable medium disclosed herein further perform at least one guideline for repetitive stress on the tendon material of a tendon from a tendon injury model, wherein the guideline includes the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof. In certain embodiments, instructions of a system or computer-readable medium disclosed herein further perform at least one guideline for repetitive stress on the tendon material. In some of these embodiments, instructions of a system or computer-readable medium disclosed herein further perform at least one guideline for a given subject by applying one or more demographic variables of the subject to the guideline. In some of these embodiments, instructions of a system or computer-readable medium disclosed herein further perform at least one guideline for a given subject by applying one or more demographic variables of the subject to the guideline. In some of these embodiments, instructions in the system or computer-readable medium disclosed herein further perform at least the estimation of at least one stress distribution in a tendon to generate a set of iterative stress datasets. In some of these embodiments, instructions in the system or computer-readable medium disclosed herein further perform at least the estimation of a stress distribution in a tendon using at least one dimension of the tendon. In some of these embodiments, instructions in the system or computer-readable medium disclosed herein further perform at least the estimation of a stress distribution within a tendon using at least one cycle curve including a plot of at least one force applied to the tendon versus the number of iterations until tendon failure.

[0020]

[0020] Various examples disclose a method for modeling soft tissue. The method includes receiving one or more images showing the anatomical geometric shape of a first object. The anatomical geometric shape includes soft tissue. The method also includes measuring several parameters of the anatomical geometric shape of the first object using one or more sensors attached to the first object. The method also includes receiving a first set of material properties for the soft tissue of the first object, a second object, or both. The method also includes identifying a second set of material properties that characterize the soft tissue while the first object is performing a task. The second set of material properties is different from the first set of material properties. The method also includes determining the strain of the soft tissue, the stress of the soft tissue, or both, based at least in part on one or more images, parameters, the first set of material properties, the second set of material properties, or a combination thereof.

[0021]

[0021] In another embodiment, the method includes receiving one or more images showing the anatomical geometry of a subject. The images may be magnetic resonance images, computed tomography images, ultrasound images, or a combination thereof, or may include a combination thereof. The anatomical geometry includes soft tissue. The subject is a living mammal. The method also includes measuring several parameters of the anatomical geometry of the subject. 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 motion, posture, repetition, duration, vibration, or a combination thereof. The method also includes receiving a first set of material properties for the soft tissue of the subject. The first set of material properties includes in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. The method also includes identifying a second set of material properties that characterize the soft tissue during the task. The second set of material properties is different from the first set of material properties. 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, the estimated damage state of the soft tissue, or a combination thereof. The method also includes running a finite element model based at least partially on one or more images, parameters, the first set of material properties, the second set of material properties, or a combination thereof. The method also includes determining strain, stress, or both on the soft tissue based at least partially on running the finite element model. The method also includes generating a model describing the soft tissue based at least partially on the determined strain, determined stress, or both.

[0022]

[0022] A method for characterizing the behavior of soft tissue in a mammal is also disclosed. The system includes a plurality of sensors configured to be attached to a subject. The sensors are configured to measure a plurality of parameters while a human subject is performing a repetitive task. The plurality of parameters includes forceful movement, posture, repetition, duration, vibration, or a combination thereof. The system also includes a computing system configured to perform operations. The operations include receiving one or more images indicative of the anatomical geometry of a human subject. The plurality of images can be or can include magnetic resonance images, computed tomography images, ultrasound images, or a combination thereof. The anatomical geometry includes soft tissue. The operations also include receiving parameters from the sensors. The operations also include receiving a first set of material properties for the soft tissue of the subject. The first set of material properties includes in-plane linear elastic modulus, out-of-plane linear elastic modulus, Poisson's ratio, or a combination thereof. The operations include identifying a second set of material properties that characterizes the soft tissue during the repetitive task. The second set of material properties is different from the first set of material properties. The second set of material properties includes isotropic properties of the soft tissue, anisotropic properties of the soft tissue, non-linear behavior of the soft tissue, an estimated damage state of the soft tissue, or a combination thereof. The operations also include performing a finite element model based at least in part on the one or more images, parameters, first set of material properties, second set of material properties, or a combination thereof. The operations also include predicting a strain mode, stress mode, vibration mode, and / or damage mode of the soft tissue based at least in part on the execution of the finite element model. The operations also include generating a model that describes the soft tissue based at least in part on the strain mode, stress mode, vibration mode, damage mode, or a combination thereof.

[0023]

[0023] The above and / or other aspects and advantages will become more apparent and will be more readily understood from the following detailed description of the embodiments, taken in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0024] [Figure 1] A flowchart generally showing exemplary method steps according to some aspects disclosed herein. [Figure 2] A schematic diagram showing exemplary method steps according to some aspects disclosed herein. [Figure 3] A schematic diagram showing exemplary method steps according to some aspects disclosed herein. [Figure 4] A flowchart showing a method of providing at least one guideline for reducing the risk of repetitive stress injury to a tendon according to various examples. [Figure 5] A perspective view (e.g., an image) of a body with one or more sensors attached according to some aspects disclosed herein. [Figure 6] A flowchart showing a method of modeling body tissue (e.g., soft tissue) according to some aspects disclosed herein. [Figure 7] Models for each of a soft tissue segment without a healed injury zone, a soft tissue segment having a 2 mm × 5 mm healed injury zone, a soft tissue segment having a 4 mm × 5 mm healed injury zone, and a soft tissue segment having a 6 mm × 5 mm healed injury zone, as disclosed herein. [Figure 8] A strain model for a soft tissue segment according to some aspects disclosed herein. [Figure 9] Models of a soft tissue segment having a 3 mm slot-like fracture and a soft tissue segment having a 2 mm V-shaped fracture according to some aspects disclosed herein. [Figure 10] A strain model for a soft tissue segment according to some aspects disclosed herein. [Figure 11] A graph including one or more S-N fatigue curves of the anatomical shape (e.g., soft tissue) of a subject according to some aspects disclosed herein. [Figure 12] This is a schematic diagram of a computing system for performing at least a portion of the methods disclosed herein, according to some aspects disclosed herein. [Modes for carrying out the invention]

[0025]

[0036] The exemplary embodiments described below will be explained in more detail with reference to the accompanying drawings. However, the examples of this disclosure can be embodied in many different forms and should not be considered as being limited to the examples described herein. These examples are rather intended 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 depicted for the sake of ease of understanding, rather than maintaining strict structural accuracy, detail, and / or scale.

[0026]

[0037] When an element is referred to as being “on” another element, “associated with” another element, “connected to” another element, “electrically connected to” another element, or “combined to” another element, it is understood that the element may be directly on the other element, associated with the other element, connected to another element, electrically connected to another element, combined with another element, or intervening to another element. In contrast, when an element is referred to as being “directly on” another element, “directly associated with” another element, “directly connected to” another element, or “directly combined to” another element, there is no intervening element. As used herein, the term “and / or” includes any combination of one or more of the related enumerated items.

[0027]

[0038] Terms such as "first," "second," etc., may be used herein to describe various elements, components, and / or directions, but it should be understood that these elements, components, and / or directions should not be limited by these terms. These terms are used solely to distinguish one element, component, and / or direction from another. For example, the first element, first component, or first direction may also be called the second element, second component, or second direction (these are merely illustrative examples).

[0028]

[0039] In this specification, for the sake of ease of explanation, spatially relative terms such as “beneath,” “below,” “lower,” “above,” and “upper” may be used to describe the relationship between one component and / or feature and another component and / or feature, or any other component and / or feature, as illustrated. It will be understood that spatially relative terms are intended to encompass different orientations of the device in use or operation, in addition to the illustrated orientation.

[0029]

[0040] As used herein, a given “component” and a corresponding “component connector” refer to at least two components structured or otherwise operable to be coupled, operably connected, or otherwise associated with one another. In certain embodiments, one component is structured or operable to be coupled, operably connected, or associated with multiple component connectors. In some embodiments, one component connector is structured or otherwise operable to be coupled, operably connected, or otherwise associated with multiple components.

[0030]

[0041] As used herein, “subject” means an animal, such as a mammal (e.g., human) or a bird (e.g., bird) species, or a non-mammalian (e.g., fish, mollusks, reptiles, amphibians, etc.). More specifically, the subject may be a mammal, such as a vertebrate, such as a mouse, primate, monkey, or human. Animals include livestock (e.g., production cattle, dairy cows, poultry, horses, pigs, etc.), sporting animals, and companion animals (e.g., pets or support animals). The subject may be a healthy individual, an individual with a disease or predisposition to a disease, or suspected to have a disease, or an individual in need of treatment, or suspected to need treatment.

[0031]

[0042] The terms used herein are intended solely to illustrate specific examples and are not intended to limit the examples. Where used herein, the singular forms “a,” “an,” and “the” also include the plural form unless otherwise explicitly stated in the context. Furthermore, where used herein, the terms “comprise,” “comprising,” “include,” and / or “including” identify the presence of the described features, integers, steps, actions, elements, and / or components, but are understood not to exclude the presence or addition of one or more other features, integers, steps, actions, elements, components, and / or groups thereof.

[0032]

[0043] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as they would be generally understood by those skilled in the art of the example. Furthermore, it will be understood that terms defined in commonly used dictionaries, for example, should be interpreted as having the same meaning as they do in the context of the relevant art, and not as idealized or overly formal unless expressly provided herein.

[0033]

[0044] While traditional ergonomic practices aim at wound prevention, macro-level prevention has been, or has been, only achieved through epidemiological methods. Based on estimated work exposure, estimates are made of when a person will self-report a wound based on discomfort or pain, guidelines are created to break that threshold, and most wounds are defined by generalized body areas (not individual components, but the entire shoulder, knee, or back). Engineering publications since the 1990s have demonstrated, consistent with the structural fatigue principle, that ex vivo tendon materials degrade in strength after repeated stress. These studies have not reported on the creation of models or the usefulness of this information in wound prevention or prediction. Here, the significant structural and property differences between tendons and non-biological structures have not been investigated, nor has the need to identify (or develop) artificial materials that can be used as substitutes for tendons for further testing (materials that are difficult to obtain and prone to change and degradation when removed from biological tissue) been investigated. A new approach is needed that connects interdisciplinary research, fills gaps, and leverages evolving technologies in several fields. Significant advances in computer technology have made the creation of material models more attractive. Computational material models can simulate the fatigue behavior of existing or potential new materials. Ultrasound can estimate the material acoustic properties of biological materials in living objects, and these can be correlated with specific mechanical properties such as elastic modulus. Motion tracking technology allows observation of a person in action and simulation of their movements within a digital body. These technologies present an opportunity to develop models that can generate guidelines for mitigating or improving soft tissue injuries. Such models can also be personalized to account for individual characteristics. Working models can also be used in reverse to inform design requirements for constructing artificial or replacement tendons, and how those tendons will function throughout the entire duration of movement.

[0034]

[0045] This disclosure relates, in certain embodiments, to methods, systems, computer-readable media, and related models for determining and predicting tendon use based on materials science principles, such as force (e.g., in different postures), stress distribution, stress data, and material (e.g., tendon) performance parameters (e.g., health status, healing, injury accumulation, injury). This disclosure identifies useful correlations between materials science principles and tendons. In some applications, the predictive models are used to address ergonomic issues related to the task, and guidelines are communicated and established to prevent or mitigate potential injuries. While much of this disclosure focuses on shoulder injuries and the supraspinatus tendon as one specific example, the methods and related embodiments disclosed herein can be applied to substantially any soft tissue (e.g., intervertebral discs (spine), ligaments, tendons, and tendinous systems of interest) and to biomimetic applications (e.g., the design of artificial tendons). An example of how this disclosure can be applied to any soft tissue is illustrated below in relation to Figure 4.

[0035]

[0046] As a further introduction, using the shoulders to perform tasks in the workplace is difficult in extreme work environments. Shoulder injuries often occur outside the workplace as well, for example, at home and while participating in sports or other professional activities. Current guidelines do not define clear and acceptable limits for shoulder-based work activities in the workplace, and do not consider the interaction of posture, force, and repetition, or the interaction of posture, force, repetition, duration, and vibration. Workplace workers who cannot simply eliminate shoulder movement face a certain degree of unknown risk. Therefore, there is a need for robust threshold limiting guidelines for shoulder joint demands that address the complex nature of upper limb work, including the interaction of force, repetition, posture, and work / rest cycles. Such guidelines would be extremely useful for engineers and ergonomics professionals, but could also be particularly useful for physicians, physical therapists, sports medicine professionals, and sports coaches.

[0036]

[0047] There is much literature on the common and frequent occurrence of shoulder injuries in demanding workplaces (e.g., heavy industry, welding, fishing, meat processing, heavy machinery, auto repair, and painting). According to 2013 data from the U.S. Bureau of Labor Statistics, cumulative traumatic shoulder injuries accounted for 15% of all musculoskeletal injuries in the workplace, surpassed only by injuries to the lower back and entire spine. However, shoulder injuries tend to be more severe and result in greater time loss.

[0037]

[0048] The shoulder mechanism allows for the positioning, function, and control of the hand (the part of the human body most useful for manual work or assembly that heavily utilizes tools). The hand, and by extension the arm and shoulder, moves for welding, painting, drilling, cutting, gutting fish, or handling materials. The system of tools, hand, and shoulder is often positioned overhead or in hard-to-reach places. The shoulder composite structure must support the weight of the arm, the tools held for the work, and the applied forces. Most manual work using tools involves tasks that are repeated many times throughout the work period.

[0038]

[0049] The neutral position of the shoulder is with the upper arm straight and hanging down at the side of the body. Each time the shoulder moves away from the neutral position, tension is applied to the tendons, creating stress within the tendons. This repeated force each time the shoulder moves away from the neutral position causes fatigue damage to accumulate in the tendons.

[0039]

[0050] Studies linking shoulder pain or rotator cuff injury to workplace factors have identified overhead work (defined as placing the elbow above the shoulder), applied force, repetitive movements, and physical load as important factors. However, the quality of these studies has varied, and the evidence has not consistently shown a significant dose-response relationship. Since then, however, two studies have linked humeral elevation beyond 90° to specific outcomes of tendon injury detected by MRI or impingement surgery. A review specifically focusing on evidence for workplace factors in rotator cuff injury outcomes has revealed a lack of research.

[0040]

[0051] Even in studies with specific outcomes, the exposure assessments in these research papers are often inadequate, and the outcome classifications are not yet specific enough to establish a strong quantitative relationship between "dose and response" or "exposure and risk." As a result, currently published guidelines on required shoulder injury risk are insufficient for use in occupational ergonomic injury prevention programs. These guidelines simply recommend reducing or eliminating overhead or shoulder-stretching postures, without specifying the degree of reduction required to have a significant impact on injury rates.

[0041]

[0052] In some industries, it is impossible to eliminate repetitive, awkward, or strainful shoulder use, but this can be improved. Without available risk thresholds, the question remains as to how much these risk factors should be mitigated to prevent injury. The issue is further complicated by the fact that this type of work may involve many repetitive motions (painting) and / or forces (drilling) and / or loads (welding). Useful guidelines should also include the interaction of risk factors and work / rest cycles.

[0042]

[0053] Given that epidemiological data alone cannot establish causality, an alternative approach is needed. The models and related embodiments disclosed herein fill many gaps that conventional epidemiological studies have failed to fill, instead using fatigue models of cumulative injury to predict and prevent injury in specific embodiments.

[0043]

[0054] Throughout this disclosure, the term “fatigue” is used in a mechanical sense, i.e., structural deterioration due to repeated forces, rather than physiological fatigue, which is the inability to perform an activity due to a decrease in muscle contractility. The term “resultant force” is used to describe the tension in tendons resulting from the weight of the tool, the posture of the body segments, and the force applied by the worker to the tool. More generally, “resultant force” refers to the combination of forces acting on soft tissues during work, including posture (e.g., shoulder position), vibration, the weight of the tool, the force vectors on the hand, and the weight of the arm. Tensile forces are related to the shoulder, but other soft tissues (e.g., intervertebral discs) can be subjected to compressive forces. Other types of soft tissue can be subjected to both compressive and tensile forces.

[0044]

[0055] When a person engages in physical activity, repetitive stress on soft tissues causes small cracks called microtraumas or subruptures. With sufficient recovery time, the body repairs itself to become stronger, so subruptures themselves do not harm the body. This is the fundamental benefit of exercise. If the recovery time is insufficient, tendons are damaged, and eventually wounds occur. The sufficient recovery time for tendon use (or overuse) has so far been uncertain, unknown, or undefined.

[0045]

[0056] A robust guideline is needed to provide clear and acceptable limits and rest cycles for shoulder-based work activities, taking into account posture, force, duration, vibration, and repetitive interactions.

[0046]

[0057] The model of supraspinatus tendon fatigue and repair cycle disclosed herein is particularly advantageous for overhead work, heavy industry involving repetition and force, and sports. Tendons behave like materials, exhibiting predictable fatigue failure at a given stress level and cycle, but they are also capable of self-repair.

[0047]

[0058] The approach described herein proposes such a model based, for example, on the principles of materials science and medicine. Using this model, it becomes possible to set exposure limits and create usable work / rest cycles to predict, mitigate, or improve shoulder injuries caused by overuse, bringing about a significant change to current approaches to mitigating and reducing shoulder injuries.

[0048]

[0059] In certain embodiments, the models disclosed herein do not encompass all aspects of tendon behavior. The prevalence of occupational shoulder injuries exceeds the limited amount of research currently being conducted, and there are many opportunities for collaborative research among diverse groups such as aerospace engineers, fatigue specialists, orthopedic surgeons, and industrial ergonomics, which could potentially bring immeasurable benefits to worker health.

[0049]

[0060] The models disclosed herein can be used, for example, to redesign work habits that exceed reasonable tendon tension or stress thresholds in order to improve the material properties of tendons, to create work-rest cycles based on collagen damage and repair rates, to identify individuals for whom the model is not conservative, and to implement strength training.

[0050]

[0061] It should be noted that the disclosed techniques are not limited to tendons. As described herein, certain examples are presented for illustrative purposes only and are not limited to shoulder tendons. However, the examples are not limited to these. Other types of soft tissues, such as intervertebral discs (spine) and ligaments, are suitable for analysis and wound treatment using the examples disclosed herein.

[0051]

[0062] This disclosure provides various methods for generating soft tissue injury models and guidelines for repeated stress on soft tissue materials. As described above, some parts of the description may focus on tendons. However, it will be understood that this is only one example of soft tissue, and that the methods disclosed herein are equally applicable to other types of soft tissue (e.g., intervertebral discs (spine), ligaments, etc.). Specific embodiments of these methods are schematically shown in Figures 1-3. As illustrated, method 100 includes generating one or more SN curves for one or more physical segments of soft tissue (e.g., tendons) from one or more SN curve datasets (steps 102, 204). A given SN curve typically includes a plot of the magnitude of the stress applied to the tendon against the number of repetitions until the tendon ruptures. The model components of each process described above will identify four categories of damage accumulation: 1) no damage, 2) accumulation of micro-damage (partial fracture), 3) damage accumulation in the form of rupture or crack growth, damage to the cellular matrix, or other biological damage, and 4) a state of catastrophic fracture or separation of the tendon structure. Method 100 also includes generating one or more iterative stress datasets describing the tendon (steps 104, 216). Method 100 also includes generating a tendon injury model by combining an SN curve (222), or data derived from said SN curve, with the iterative stress dataset (216) to predict tendon injury under one or more conditions (steps 106, 216) (e.g., a physical or specific representation or information about tendon injury under a given set of conditions). Method 100 also includes combining an SN curve (222) or data derived from said SN curve to predict tendon injury under one or more conditions and generate a tendon injury model (steps 106, 216) (e.g., a physical or specific representation or information of tendon injury under a given set of conditions). In certain embodiments, Method 100 also typically includes generating at least one guideline regarding tendon material repetition stress from the tendon injury model (steps 108, 224).As used herein, the term “guidelines” means evidence-based maximum acceptable limits in terms of force (e.g., vibration, tool weight, force vector on hand, arm weight, etc.), posture, position, frequency, duration, and / or recovery, for the purpose of protecting human tissue material from the risk of injury due to tendon injury during human activity, such as manufacturing or other processes. In some embodiments, guidelines include tendon posture, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of maintaining a given posture of the tendon, the duration of repetitions of a given movement of the tendon, a given movement of the tendon, the duration of a given force applied to the tendon, and combinations thereof. As also shown, the method also includes, in certain embodiments, evaluating the action of a given muscle (302) and injury to a given tendon (304).

[0052]

[0063] The methods of the present disclosure include various embodiments. In some embodiments, for example, the method includes combining multiple SN curves of physical sections to generate a combined SN curve (222). In certain embodiments, the method includes applying a cumulative injury model to predict tendon injury under one or more conditions when combining an SN curve (204) or data derived from such SN curve with a repeating stress dataset (216). In some embodiments, the method includes obtaining an SN curve dataset using one or more data sources, including medical diagnostic techniques, such as ultrasound data, computed tomography (CAT) scan data, magnetic resonance imaging (MRI) scan data, destructive test data, cadaver material, animal material, polymer substitute material, molecular dynamics modeling (MDM) data, publication data, and combinations thereof (202, 204, 206).

[0053]

[0064] Substantially any tendon can be evaluated as part of the method disclosed herein. Some exemplary tendons optionally 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 magnus 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. This includes the tendons of the flexor digitorum longus, interosseous muscles, flexor digitorum profundus, abductor digiti minimi, opponens pollicis, flexor pollicis longus, extensor muscles, abductor pollicis, flexor hallucis longus, flexor digitorum brevis, lumbrical muscles, abductor hallucis, flexor digitorum longus, abductor digiti minimi, ocular muscles, levator palpebrae superioris, masseter, temporalis, trapezius, sternocleidomastoid, semispinalis capitis, splenius capitis, mylohyoid, thyrohyoid, sternohyoid, rectus abdominis, external oblique, transversus abdominis, latissimus dorsi, or erector spinae tendons, and combinations thereof. In some embodiments, tendons include mammalian tendons. In certain embodiments of these embodiments, mammalian tendons include human tendons.

[0054]

[0065] In certain embodiments, the method includes predicting tendon injury under one or more conditions by combining an SN curve or data derived from such SN curve and a set of repeated stress data with healing data (212). In some embodiments, the disclosure provides a method for predicting (e.g., anticipating) tendon injury in a subject, which includes obtaining one or more use data for at least one tendon in a subject and comparing the use data with guidelines for repeated stress on the tendon material generated by the method, thereby predicting tendon injury in a subject. In certain embodiments, the physical classifications include undamaged classifications, partial fracture classifications, crack initiation classifications, fracture classifications or fracture curves, and combinations thereof. In some embodiments, one or more steps are implemented at least partially by computer. Systems and associated computer-readable media are further described herein.

[0055]

[0066] In some embodiments, the method includes generating at least one guideline (224) for repetitive stress on the tendon material of a tendon from a tendon injury model (214), the guideline including the posture of the tendon, the number of repetitions of a given movement of the tendon, the force applied to the tendon, the duration of the given posture of the tendon, the duration of the repetition of a given movement of the tendon, the duration of the force applied to the tendon, and combinations thereof. In some of these embodiments, the guideline for repetitive stress on the tendon material includes one or more recommended use / rest cycles of the tendon under one or more sets of use conditions. The method typically includes validation of the guideline for repetitive stress on the tendon material. In some of these embodiments, the method includes individualizing the guideline for a given subject by applying one or more demographic variables of the subject to the guideline (226). In some of these embodiments, the method includes using task information when generating the guideline (228). In some of these embodiments, the task information includes tool weight and / or force vectors.

[0056]

[0067] In certain embodiments, the method includes estimating at least one stress distribution within a tendon to generate a repetitive stress dataset (218). In some of these embodiments, the method includes estimating the stress distribution within a tendon using at least one dimension of the tendon (220). In some of these embodiments, the dimension includes at least one cross-sectional area of ​​the tendon. In some of these embodiments, the method includes estimating the stress distribution within a tendon using at least one cycle curve that includes a plot of at least one force applied to the tendon against the number of repetitions until tendon failure (210). In some of these embodiments, the force is determined in one or more positions of the tendon (208). In some of these embodiments, the method includes determining the force using estimation and / or modeling techniques such as finite element modeling (FEM) and / or electromyography (EMG). In some of these embodiments, the method includes using task information when determining the force applied to the tendon position. In some embodiments, the task information includes tool weight and / or force vectors.

[0057]

[0068] In some embodiments, the process of combining S / N curves involves cumulative damage modeling and healing centered on multiple curves. For illustrative purposes, one example may include a set of iterative stress datasets (this set may, for example, represent the exposures that the object may experience during a single operation):

[0058]

[0069] Task A force: 150N, repetitions: 50

[0059]

[0070] Task B force: 100N, repetitions: 500

[0060]

[0071] Task C force: 50N, repetitions: 5000

[0061]

[0072] The number of iterations is derived from observation or sampling of the task in a specific manner. Stress values ​​are derived by a digital model that, in some manner, applies the load borne by the subject to the position of the subject's hand / arm / limb during task execution. The output is force (N). Stress is calculated by dividing the force by the cross-sectional area of ​​the tendon (e.g., SST geometric shape model), which in this example is 50 mm. 2 Therefore, the stress for Task A is 3 MPa, the stress for Task B is 2 MPa, and the stress for Task C is 1 MPa. This explanation also provides information about the signal-to-noise curve where no damage occurs, and that the undamaged zone ends (and partial fracture begins) at 4 MPa after 1000 cycles (this is a point on the curve).

[0062]

[0073] All these values ​​are combined to determine the amount of damage to the tendon under consideration. In some embodiments, Miner's Rule is used as part of this process. In Miner's Rule, a value greater than 1 means that the selected signal-to-noise curve has been exceeded. In this case, since the no-injury category has been selected, a value greater than 1 means that the no-injury category no longer applies, and instead one of the other categories applies.

[0063]

[0074] When these tasks are combined with the limitation of the undamaged category, (3MPa × 50 + 2MPa × 500 + 1MPa × 5000) / (4MPa × 1000) = 1.53 This means that the value is greater than 1 (>1), and therefore exceeds the no-injury category. Also, if a given guideline specifies that no injury is desired, then that guideline is exceeded. Therefore, if the value is greater than 1 (>1), it will be necessary to calculate the next category (e.g., partial fracture), and so on. In some embodiments, if multiple tasks are combined with rest periods in between (e.g., a decrease in the percentage of exposure, a shift to a lower point on the curve), healing data will also be applied.

[0064]

[0075] In this explanation, the calculations shown earlier yield a point solution (only one number and one answer) for each iteration. To increase the fidelity of the model, this iteration can be run many times, sampling from, for example, SST geometric shapes, digital models of forces acting on tendons, and the distribution of any variable (variation) in the iterations. For example, this could be a model run multiple times with slightly different stochastic inputs, similar to a Monte Carlo simulation. This provides a risk output that includes several boundaries or bands around it (e.g., a 95% confidence interval). Optionally, other variables, such as demographics, can also be added to the model.

[0065]

[0076] Figure 4 is a flowchart of Method 400 for mitigating (e.g., improving) repetitive stress injuries to soft tissues when performing processes with various examples. Method 400 utilizes the material science properties of such soft tissues to determine guidelines that reduce the likelihood of people performing the process sustaining injuries when implemented as described with reference to Method 400. Method 400 can be partially implemented using System 1200, as illustrated and described below with reference to Figure 5, for example. Method 400 further includes non-computer operations that bring improvements in the field of industrial hygiene. Such operations include, for example, obtaining repetitive stress datasets related to soft tissues and processes (e.g., descriptions of force per unit area and number of repetitions of soft tissue) and implementing guidelines to reduce the likelihood of injuries for repetitive stress injuries experienced by individuals during the process. Method 400 focuses on tendons as an example of soft tissue. However, as mentioned above, Method 400 can also be used to mitigate (e.g., improve) repetitive stress injuries to other types of soft tissues (e.g., intervertebral discs (spine), ligaments, etc.), or as an alternative.

[0066]

[0077] Method 400 can be used to improve repetitive stress injuries in workers performing part of a manufacturing process. However, this is just one example, and Method 400 can also be used outside the workplace, as described below. Each worker may have one or more tasks that form part of a manufacturing process, such as an assembly line. Each worker's task may be modified by one or more guidelines created 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 have one or more exercises that constitute part of the training program. The exercises may be modified by one or more guidelines created by Method 400. Method 400 is not limited to manufacturing or athletic processes, but can be implemented to improve repetitive stress injuries in any type of process involving repetitive motion by a person.

[0067]

[0078] Method 400 can be used to improve wounds in any of various soft tissues. According to some examples, Method 400 can be used to improve wounds in tendons or tendon complexes. Examples of such tendons and tendon complexes are previously presented in connection with Figures 1-3. Alternatively, Method 400 can be used to improve wounds in connective tissue or musculoskeletal soft tissues. Generally, non-limiting examples of soft tissues to which the embodiment can be implemented include tendons, tendon complexes, intervertebral discs (spine), and ligaments.

[0068]

[0079] For example, using the example of a lumbar wound, both the intervertebral discs and tendons are made of soft tissue (e.g., type II and type I / III collagen). Therefore, the model input can be updated in Method 400 and / or different anatomical structures can be imported into Method 400 to extend Method 400 to other types of soft tissue such as intervertebral discs. Bone properties can also be incorporated into the definition of the shoulder region. As a result, fractures in other parts of the body can be studied. Analysis of muscle tissue with fibers in the primary loading direction can follow a similar methodology to that for tendons.

[0069]

[0080] In 402, method 400 includes obtaining at least one iterative stress dataset of soft tissue (e.g., tendons, ligaments, intervertebral (spinal) discs, etc.) and related processes. The iterative stress dataset may be in the form of a computer file, for example, tab-separated values ​​or comma-separated values ​​(CSV) format. In non-limiting examples, the iterative stress dataset may be obtained by being read by a computer from a persistent electronic storage device, or by inputting the data into a file format and storing it on a computer.

[0070]

[0081] According to several examples, each iterative stress dataset can represent the exposure an object may receive in the process of completing a single task that forms part of an overall process. This exposure may take the form of the number of repetitions and the force applied to the soft tissue with each repetition. The force may be expressed, for example, in Newtons (N). Alternatively, each iterative stress dataset may take the form of the number of repetitions and the stress on the soft tissue with each repetition. The stress may be the resulting stress, which may include stress due to posture (e.g., shoulder position), vibration, tool weight, force vectors on the hand, arm weight, etc. The stress may be expressed, for example, in megapascals (MPa).

[0071]

[0082] Each iterative stress dataset may further include a description of the iterative behavior, for example, in narrative form. Multiple iterative stress datasets can describe multiple types of motion. Method 400 can obtain the iterative stress datasets by obtaining them in a computer-readable format (for example, by user input), and by providing the iterative stress datasets to a computer program that performs the behavior of, for example, blocks 402, 404, 406, and 408.

[0072]

[0083] In 404, method 400 includes accessing information characterizing at least two damage categories. Such information may be in the form of a computer file, for example, pairs of values ​​in tab-separated or comma-separated (CSV) format. Such information may be accessed, in non-limiting examples, by being read by a computer from a persistent electronic storage device or by the information being received and stored in a computer.

[0073]

[0084] According to various examples, Method 400 can access first information characterizing a first damage category and second information characterizing a second damage category. According to various examples, Method 400 can further access third information characterizing a third damage category. Each piece of information can quantify the number of iterations at a given stress for the soft tissue to transition out of its respective damage category. For example, the first piece of information can quantify the number of iterations at a given stress for the soft tissue to transition out of the first damage state. The second piece of information can quantify the number of iterations at a given stress for the soft tissue to transition out of the second damage category. For example, if a third damage category is included, the third piece of information can quantify the number of iterations at a given stress for the soft tissue to transition out of the third damage category.

[0074]

[0085] Each piece of information may be in the form of a curve that quantifies the number of repetitions at a given stress until the soft tissue transitions from each damage segment, for example, with stress as the independent variable and repetitions as the dependent variable. For example, each piece of information may be in the form of an S / N curve, as described herein, according to various examples. When stored on a computer, such information may be in the form of an ordered set of pairs (S,R), where S represents stress and R represents the number of repetitions required to transition from each segment.

[0075]

[0086] According to various examples, the first injury segment may be the uninjured segment, the second injury segment may be the partially fractured segment, and the third injury segment may be the fracture propagation segment, if included (note that any combination of two or more injury segments is usable according to various examples, and is not limited to those explicitly described here). The uninjured segment can represent a situation in which microinjuries (e.g., partially fractures) occur in the soft tissue at substantially the same rate as healing. A transition from the uninjured segment may represent an accumulation of partially fractures at a rate faster than the healing rate of each soft tissue. The partially fractured segment may represent a situation in which microinjuries (partial fractures) are accumulating, but a macroscopic fracture has not yet formed. A transition from the partially fractured region may indicate that a macroscopic fracture has formed. The fracture propagation segment may represent a situation in which a fracture has formed and is propagating through the soft tissue. A transition from the fracture propagation segment may indicate that the soft tissue has completely fractured.

[0076]

[0087] In cases where either or both of the undamaged and partially fractured categories are included, it should be noted that damage to the subject's soft tissues can be predicted before the subject recognizes the occurrence of damage. For example, the soft tissues in these categories may be damaged, but they will not cause pain or pleasure to the subject.

[0077]

[0088] In 406, Method 400 includes predicting sufficient conditions for soft tissue injury based on at least information characterizing injury segments and repeated stress datasets. The injury may be any of the following: soft tissue accumulating microinjuries at a rate faster than the healing rate of the soft tissue (e.g., transition from a first segment), soft tissue undergoing macroscopic rupture (e.g., transition from a second segment), or soft tissue completely fractured (e.g., transition from a third segment).

[0078]

[0089] In prediction / forecasting, such conditions can be determined by utilizing the material science properties of soft tissue. According to some examples, prediction can be performed as follows: First, if stress has not already been addressed, force data in iterative stress datasets can be converted to units of stress. For example, iterative stress datasets can be expressed as the force acting on the soft tissue in each iteration. By dividing such forces by the cross-sectional area of ​​the soft tissue, iterative stress datasets are converted to units of stress per iteration (and / or per iteration). The system can store cross-sectional area data of the soft tissue for this purpose. Soft tissue cross-sectional area data may include the average cross-sectional area of ​​various soft tissue types, specific cross-sectional areas for demographic combinations (e.g., sex, age, gender), or combinations of such data. Second, iterative stress datasets are compared with information characterizing damage categories. For example, in the case of iterative stress datasets representing R iterations at stress level S, the stress level S can be considered an independent variable of information representing the current damage status of the soft tissue, and a corresponding dependent variable R' regarding the number of iterations for transitioning from a damage category can be identified. Thirdly, the number of repetitions R' for the identified dependent variable is compared with the number of repetitions R specified in the iterative stress dataset. If the former is greater than the latter, the soft tissue is predicted to remain in its current damaged state, and therefore no further damage is predicted. However, if the former is less than or equal to the latter, the soft tissue is predicted to transition from each damage category. In this case, the soft tissue is expected to be damaged. Therefore, the soft tissue is predicted to be damaged if the number of repetitions R specified in the iterative stress dataset for stress level S satisfies or exceeds the number of repetitions R' corresponding to each piece of information S characterizing the current damage category for transitioning from the current damage category.

[0079]

[0090] This process can be extended to include multiple iterative stress datasets. For example, the product of the number of repetitions and the stress level can be summed from various iteration datasets. This sum can be compared to the product of the stress level and the number of repetitions from information characterizing the current damage state. If the sum is large, the soft tissue is predicted to be damaged. Otherwise, the soft tissue is predicted to remain in its current damaged state. Note that Minor's rule can be used for this comparison, as explained in the previous example in relation to Figures 1-3.

[0080]

[0091] In 408, Method 400 includes determining, based on predictions, at least one guideline for reducing the risk of repetitive stress damage to soft tissue materials. This guideline generally reduces the number of repetitions and / or the amount of stress corresponding to the operation in one or more repetitive stress datasets. Such parameters can be reduced until damage is no longer predicted by the calculations described earlier with reference to Block 406. These reduced parameters may form all or part of the guideline.

[0081]

[0092] The guidelines generally state that stress on soft tissue can be reduced by reducing the force applied to the soft tissue in one of several ways. The force on the soft tissue may be the resultant force applied to the soft tissue, and this force may be the result of posture (or position), weight (e.g., holding an object such as an arm and / or a tool), the vector of the applied force (e.g., pushing with a hand), or vibration (e.g., holding a vibrating object such as a tool). In some examples, this force is reduced by limiting one of the above parameters.

[0082]

[0093] The forces acting on soft tissues can be reduced, either by imposing restrictions on the position and / or posture of the body or part thereof, thereby influencing the position and / or posture of the soft tissues. In various examples, such guidelines may include restrictions on at least one of the positions and / or postures of the soft tissues.

[0083]

[0094] Here, "position" refers to the quantitative characterization of the body or a part thereof of the object. For example, position can be defined using measuring instruments in units such as length, angle, and xyz coordinates. For example, the position of a tendon can be defined by coordinates (0cm, 5cm, 1cm), where the origin (0cm, 0cm, 0cm) is the point where the tendon attaches to the humerus, and these coordinates correspond to a position in the following planes: x = sagittal plane, y = transverse plane, z = coronal plane. The position of an object (or a part of the object's body) can be determined by attaching a motion tracking system to the object, according to various examples.

[0084]

[0095] "Posture" can refer to a qualitative characteristic of the body or a part thereof. Posture can be defined in terms of the relative position of a specified landmark. For example, a particular posture called "overhead work" can be defined as a situation where the subject's elbow is above its shoulder. Posture can generally be defined qualitatively by the position of the body, which allows for observation of the posture and comparison with other observations or positions. The position of an object (or a part of the object's body) can be determined by observational studies by ergonomicians or industrial engineers, according to various examples.

[0085]

[0096] Alternatively, or further, the forces acting on soft tissues can be reduced by limiting the temporary duration of movement, position, or posture. In other words, the guidelines may limit any or a combination of the following: the duration for which a given posture of soft tissue is maintained, the duration for which a given position of soft tissue is maintained, the duration for which a given movement of soft tissue is repeated, and / or the duration for which a given force is applied to the soft tissue.

[0086]

[0097] According to some examples, guidelines may include mandatory rest periods. In such examples, representations of healing processes to counteract microscopic or macroscopic damage can be utilized. The rest period may represent sufficient time for such healing processes to negate the accumulated damage.

[0087]

[0098] The guidelines can be output in various formats. In some cases, the guidelines can be output in a narrative format using a pre-generated narrative template. For example, if calculations indicate that the number of repetitions should be reduced from 1000 to 725, the guidelines can populate these numbers into a partially read template as follows: "The number of repetitions of action X should be reduced from Y to Z," where X is replaced with a description of the action, Y with 1000, and Z with 725. The formatted guidelines can be output by displaying them on a computer monitor, by email, or by other technologies that provide information to individuals or processes.

[0088]

[0099] In 410, Method 400 includes implementing guidelines in a process. To do so, Method 400 may include providing guidelines to workers on an assembly line (for example, if the process is a manufacturing process). The workers can modify their tasks accordingly. In the example where the process is a training process for athletes, the guidelines may be provided to the trainer, who can modify the training plan for the athletes accordingly. The guidelines may also be used in the design of production systems, products, work tasks, training plans, etc.

[0089]

[0100] The systems and methods disclosed herein can use material properties to generate models that characterize the behavior of soft tissues (e.g., tendons, intervertebral discs, etc.) of living mammals exposed to different loading conditions. Soft tissues can be modeled according to a number of different loading conditions to characterize damage to soft tissues in overuse situations in order to mitigate the possibility of injury.

[0090]

[0101] The model may be, or include, a (e.g., single) finite element model (FEM) for determining or predicting material fatigue of soft tissue. Instead of, or in addition to, mammals, the model may also be applicable to other species such as reptiles and amphibians. In one example, the soft tissue may be, or include, the supraspinatus tendon (SST) of the shoulder. Instead of, or in addition to soft tissue, the model may also be applicable to connective tissue, musculoskeletal tissue, intervertebral discs, and the like.

[0091]

[0102] As will be explained in detail below, the model can accept input data exposed to a variety of load conditions. For example, the input data may be or may include one or more (e.g., five) established ergonomic risk factors (forceful movement, posture, repetition, duration, and vibration). The model can demonstrate correlations between simultaneous exposures to these risk factors. This specification demonstrates correlations between simultaneous exposures to ergonomic risk factors, thereby proving that the interaction of risk factors results in a complex effect that can cause injury at a clear multiplier.

[0092]

[0103] Input data may also include, or instead include, the (linear and / or nonlinear) modulus of elasticity of soft tissue, soft tissue strain, soft tissue cross-sectional area, percentage of healthy soft tissue, percentage of damaged / injured soft tissue, or a combination thereof. Input data may also include, or instead include, 3D anatomical shape (e.g., for the shoulder), load transfer pathways between tissue and bone through one or more joints, external (e.g., task-related) boundary conditions (e.g., shoulder position or posture), internal (e.g., anatomical) boundary conditions, or a combination thereof. The data may also include, or instead include, an entire system of parameterized and combined interactions, unlike overly generalized task guidance based on empirical studies. In contrast, conventional ergonomic approaches rely on empirical datasets that focus on resulting wounds and do not model the onset of injury based on the behavior of materials at the component level.

[0093]

[0104] The model can output one or more fatigue signal-to-noise curves. The model can also output, or alternatively, an evaluation of one or more (e.g., four) categories of tissue damage: no damage, partial fracture, partial damage, and complete damage. The model can also output, or alternatively, a comparison of various changes to work procedures and tools (e.g., evaluation of reduced range of motion, power tools of different weights and vibration frequencies, introduction of ergonomically designed assistive devices), which may lead to optimized task design guidance and / or recommended work intervals for specific tasks.

[0094]

[0105] In one embodiment, the fidelity of the model can be increased by incorporating nonlinear behavior and introducing complex loading and vibration / fatigue cycles for soft tissue. This model can verify that finite element modeling is a viable approach for evaluating healthy and partially damaged tissue and can explain the fatigue failure characteristics of overused soft tissue (e.g., tendon material).

[0095]

[0106] The effects of vibration can be characterized from the hand through the body to soft tissues (e.g., tendons) (e.g., while holding a vibrating tool). By capturing viscoelastic behavior across the system, the behavior of soft tissue materials can be characterized, and by characterizing the effects of vibration on the hand / arm / limb (and other parts), accelerated material fatigue and accelerated wounds can be clearly identified throughout the soft tissue geometry.

[0096]

[0107] Figure 5 is an image of the body of an object 502 to which one or more sensors (e.g., three are shown: 520A to 520C) are attached, according to several embodiments disclosed herein. The object 502 includes a collar 510, shoulder 512, and arm 514. The object 502 also includes ligaments 530 and tendons (e.g., SST) 532.

[0097]

[0108] Sensors 520A–520C may be attached to the object 502 and may be configured to measure one or more parameters. As illustrated, the first sensor 520A may be attached to the collar 510, the second sensor 520B may be attached to the shoulder 512, and the third sensor 520C may be attached to the arm 514. In another embodiment, one or more of sensors 520A–520C (or other sensors) may be attached to the object 502 (e.g., directly) on or outside the ligaments 530 and / or tendons 532 of the object to be monitored. Sensors 520A–520C may also be attached to different locations on the object 502 (e.g., feet, legs, waist, abdomen, back, neck, head, etc.), or instead. Furthermore, although three sensors 520A–520C are shown, more or fewer sensors may be used. The parameters to be measured may be any of the above input data (e.g., forceful movement, posture, repetitions, duration, vibration, etc.), or may include any of these.

[0098]

[0109] Figure 6 is a flowchart of Method 600 for modeling soft tissue in several embodiments disclosed herein. Method 600 can monitor soft tissue to reduce the likelihood of injury to a person performing a task (e.g., in a workplace). Method 600 can be partially implemented using a computing system 1200 (described below with reference to Figure 12). Method 600 further includes non-computer operations that bring improvements in the field of industrial hygiene. Such operations include, for example, obtaining repetitive stress datasets related to soft tissue and processes (e.g., force per unit area of ​​soft tissue and number of repetitions) and implementing guidelines to reduce the likelihood of an individual suffering repetitive stress injury during a process.

[0099]

[0110] An exemplary sequence of steps 600 is provided below. However, one or more steps of step 600 can be performed in a different order, simultaneously, repeatedly, or omitted.

[0100]

[0111] Method 600 may include receiving images, videos, or both that include or show the anatomical geometric shape of an object, as in 602. In one embodiment, the anatomical shape may be that of a particular object being analyzed (e.g., a particular person). In another embodiment, the anatomical geometric shape may be that of another similar object (e.g., not a particular person being analyzed). Images and / or videos may be, or include, magnetic resonance images (MRI), ultrasound images, computed tomography images (CT scans), 3D scans, or a combination thereof. The anatomical geometric shape may be, or include, shapes and / or spatial connections that describe a 2D and / or 3D anatomical structure being analyzed. As used herein, “shape” may refer to anatomical structures (e.g., soft tissue, bone, etc.) and / or soft tissue variations (e.g., length, area, etc.) relative to a given working position (e.g., arm extended overhead or resting on the side). As used herein, “spatial connection” refers to the position of the limbs and joints relative to the rest of the body, as well as the specific kinetics of the task. For example, during a task that requires stretching away from the body, greater moments may be exerted on the joints, or shear forces may be induced by off-axis movement. Anatomical structures may be mammalian anatomical structures (e.g., human anatomical structures). However, as mentioned above, other types of anatomical structures are also considered herein. Anatomical shapes help create a basis for defining the motion pathways and / or forces acting on anatomical structures, which can be used to simulate tasks in the workplace.

[0101]

[0112] Method 600 may also include measuring one or more parameters of the anatomical geometric shape of the subject, as in 604. One or more parameters may be measured using images, videos, or both, or may not be measured. One or more parameters may be measured while the subject is performing a task (e.g., a work task). In one embodiment, the parameters may be or include any of the above-described input data (e.g., ergonomic risk factors: forceful movement, posture, repetition, duration, vibration, or a combination thereof). In another embodiment, the subject may be exposed to or experience one or more of the ergonomic risk factors, and the parameters may be or include soft tissue responses (e.g., tension, stress, etc.) in response to the ergonomic risk factors. This latter embodiment can be used for detailed characterization of materials (e.g., cadaver or surrogate DMA analysis) to obtain viscoelastic material properties that may be difficult to obtain from living test subjects.

[0102]

[0113] The parameters may be measured by one or more of the sensors 520A to 520C on the object 502 (for example, while the object 502 is performing a task). For example, a user (e.g., an operator) may have one or more tasks that form part of a manufacturing process (e.g., an assembly line). These parameters can be used to define the motion path and the expected forces acting on the anatomical structure, which can be used to characterize the tasks in the workplace. The motion path and forces are applied to the anatomical shape to create a physical model that can be immediately integrated with material inputs.

[0103]

[0114] Method 600 may also include loading or importing anatomical geometry (from 602) and one or more parameters (from 604) into a FEM (e.g., a computational framework), as in 606. Imported anatomical geometry (from 602) may include MRI images, ultrasound scans, and other imaging techniques. Imported anatomical geometry (from 602) may also include direct measurements from a living subject using ultrasound. For example, the shoulder of the subject can be ultrasound-scanned while measuring the applied force while the subject stands upright with their arms fully extended forward and pushing against an overhead target. The ultrasound scan images of the tendons may then be digitized into CAD geometry and imported into the FEM numerical engine. Since the applied force also changes, the same process can be repeated for different target positions relative to the subject's shoulder. By incorporating multiple measurements, the FEM predicts the stress and strain on the tendons for given changes in position and applied force.

[0104]

[0115] Method 600 may also include receiving a first set of material properties of the anatomical geometric shape of the object, as in 608. More specifically, this may include receiving a first set of material properties of the soft tissue of the object. In one embodiment, the first set of material properties may be measured in a specific object being analyzed (e.g., a specific person) using external imaging techniques, e.g., shear wave elastography, ultrasound, or both. In another embodiment, the first set of material properties may be measured in other similar objects (e.g., not the specific person being analyzed) using the techniques described above. In yet another embodiment, the first set of material properties may be received or derived from empirical studies or published investigations. The first set of material properties may be or include in-plane modulus, out-of-plane modulus, Poisson's ratio, etc. The modulus may be linear, nonlinear, or both.

[0105]

[0116] Method 600 may also include identifying a second set of material properties that characterize the anatomical geometric shape of the object, as in 610. The second set of material properties can characterize the material properties of the soft tissue of the object during the task. In one example, the second set of material properties can characterize the isotropic and / or anisotropic structural properties of the soft tissue, the nonlinear behavior of the soft tissue, the estimated injury state of the soft tissue, or a combination thereof. In another example, the modulus of elasticity of the soft tissue material may harden under higher loads, tendon stretching, applied vibration, or a combination thereof. Accordingly, anisotropic behavior of the soft tissue may occur, which allows the use of modulus definitions in one or more vectors for loads of complex motion paths and various ranges of motion. Acute injury and healing of tendon fibers can also alter the properties of local soft tissue material. If one or more material properties are unavailable, such material properties may be measured from a human object or surrogate animal using external imaging techniques or material sample composition techniques, such as dynamic mechanical analysis (DMA). For example, a second set of material properties can be measured using external imaging techniques or material sample composition techniques, such as dynamic mechanical analysis (DMA).

[0106]

[0117] Method 600 may also include loading or importing received material properties (from 606) and 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, nonlinearity, frequency dependence, viscoelasticity, orthotropy, or a combination thereof. To enable the FEM to computer-predict the stress / strain state and performance of soft tissue (e.g., shoulder tendons), tendon materials can first be numerically represented (as in 612) as orthotropic materials using / based on material properties (from 606) from published literature. To improve accuracy, the use of simple force / displacement property assessments (from 608) of tendon material properties may be integrated into the model. In another embodiment, accuracy may be improved by integrating more complex viscoelastic properties of tendon material properties (from 610) into the model. Characterization may be performed directly on tendons or indirectly through the methods described in 602. Direct characterization may be based at least partially on cadaver tendons, or synthetic / alternative versions of tendon material, from different demographics. Indirect characterization may be performed by utilizing medically published and approved references (from 602) to indirectly measure the force / displacement or stress / strain of the tendon or a portion of the tendon (e.g., only the surface).

[0107]

[0118] Before performing or conducting FEM (as described below), the anatomical geometric shape (from 602) can be first meshed, and the geometric shape (from 602) and material orientation (from 608 to 612) may be taken into consideration. FEM may also have boundary conditions defined so that the applied load acts on the geometric shape (from 602). The load applied in FEM may be defined to follow a periodic path in order to reproduce the physically observed periodic motion of the object represented in method 600.

[0108]

[0119] Method 600 may also include performing or doing FEM, as in 614. This may include computational processing of FEM (e.g., integrated physical and material models), thereby allowing for analysis of outputs and trends of interest. FEM may be performed / done using / based on anatomical geometric shapes (from 602), one or more parameters (from 604), received material properties (from 606), characterized material properties (from 608), or a combination thereof.

[0109]

[0120] Method 600 may also include determining one or more modes of the anatomical geometric shape of the object (e.g., soft tissue), as in 616. The modes may be determined at least in part on the performance of the FEM. Thus, the modes may be determined at least in part on the anatomical geometric shape (from 602), one or more parameters (from 604), received material properties (from 606), characterized material properties (from 608), or a combination thereof. The modes may be or include stress modes that refer to stress on the anatomical shape of the object (e.g., soft tissue). The modes may also be or include strain modes that refer to strain on the anatomical shape of the object (e.g., soft tissue). The modes may be or include vibration modes that refer to natural frequencies that can exaggerate local loads on the anatomical structure. For example, a vibration mode may refer to vibrations applied by a power tool or similar device held by the object during task performance, which can be transmitted to soft tissue (such as a shoulder tendon) as tension or friction of a tendon against other anatomical structures surrounding the vibrating bone or tendon. Such a mode may be or include a fracture mode that refers to progressive tissue damage. For example, a fracture mode may refer to fatigue of soft tissue (e.g., a tendon), abrasion of the tendon surface due to friction, partial loosening of the tendon, and / or pinching of a nerve adjacent to the tendon.

[0110]

[0121] In one embodiment, the mode can be predicted (e.g., it provides a prediction). In other words, this mode can predict future stress, strain, vibration, and fracture of soft tissue if the task continues to be performed. For example, these modes can predict when the stress, strain, vibration, and fracture of soft tissue will reach a predetermined level if the task continues.

[0111]

[0122] Method 600 may also include generating one or more models of the anatomical geometric shape of the subject (e.g., soft tissue), as in 618. The models may be determined at least in part on the basis of performing FEM. Thus, the models may be determined on the anatomical geometric shape (from 602), one or more parameters (from 604), received material properties (from 606), characterized material properties (from 608), or a combination thereof. Examples of such models are shown in Figures 7–11.

[0112]

[0123] Figure 7 shows models of soft tissue segments 710 without a healing injury zone 712, soft tissue segment 720 having a healing injury zone 722 of 2 mm × 5 mm, soft tissue segment 730 having a healing injury zone 732 of 4 mm × 5 mm, and soft tissue segment 740 having a healing injury zone 742 of 6 mm × 5 mm, according to several embodiments disclosed herein.

[0113]

[0124] The soft tissue segment 710 may be a unidirectional stacked (single stack) representation (e.g., 8 stacks) having ∈11 directions along the length of the sample, while the soft tissue segments 720, 730, and 740 may be quasi-isotropic (or quasi-symmetric) stacked representations only in the damaged zone 722 and the healing zones 722, 732, and 742. The quasi-symmetric stacked representation is numerically represented by modeling a (45,90-45,0)s stack, where this value means that the stacks in zones 722, 732, and 742 are isotropic in the plane of the sample. The characteristic values ​​of the single-ply stacked material are as follows: ∈11=140MPa, ∈22=1MPa, and ∈33=1MPa, vs=0.497* (incompressible), G12=70MPa (approximate), G13=70MPa (approximate), G23=70MPa (approximate). The engineering elastic constant material properties ∈11, ∈22, ∈33, and vs can be obtained from medical journal publications, and G12, G13, and G23 can be approximated to half the value of ∈11. To perform predictions, all nine simple engineering elastic constant material properties are required as FEM inputs. Conventional laminated plate theory can be used to calculate effective elastic properties.

[0114]

[0125] Figure 8 shows models of strain on soft tissue segments 710, 720, 730, and 740 according to several embodiments disclosed herein. The load corresponds to a 5% strain displacement along the length (e.g., 2.5 mm). The maximum mechanical principal strain is parallel to the load direction, as indicated by the arrows. Tendon tissue can be treated as a composite material.

[0115]

[0126] Figure 9 shows models of a soft tissue segment 910 having a 3 mm slot-shaped tear 912 and a soft tissue segment 920 having a 2 mm V-shaped tear 922, according to several embodiments disclosed herein.

[0116]

[0127] Figure 10 shows models of strain on soft tissue segments 910, 920 according to several embodiments disclosed herein. The load corresponds to a 5% strain displacement along the length (e.g., 2.5 mm). The maximum mechanical principal strain is perpendicular to the direction of the load, as indicated by the arrow. Mechanical strain can be larger due to the material properties in the perpendicular direction, which can lead to rupture. When the shoulder joint becomes unstable, the forces on the rotator cuff tendons (which are designed to move, not stabilize, the shoulder joint) can increase further. This can cause the tendons to degenerate and weaken, making them more susceptible to rupture. Rotator cuff tears are a symptom of shoulder joint instability, and the underlying issue is the lack of a true diagnosis.

[0117]

[0128] Figure 11 is a graph 1100 containing one or more SN fatigue curves 1110, 1120 for the anatomical geometric shape (e.g., soft tissue) of a subject, according to several embodiments disclosed herein. Graph 1100 plots the magnitude of periodic stress (S) against a logarithmic scale of the number of cycles to failure (N). In other words, graph 1100 can show the predicted number of cycles to failure for a given repetitive loading pattern. In one example, SN fatigue curve 1110 may represent a healthy tendon for a given task, and SN fatigue curve 1120 may represent a damaged tendon for a given task. In another example, SN fatigue curve 1110 may represent no vibration during a given task, and SN fatigue curve 1120 may represent vibration applied during a given task. Incorporating multiple inputs to varying degrees generates more complex curves, which can be superimposed for comparison. The curve can also be generated for different task postures (e.g., 25% overhead extension, 50% overhead extension, 75% overhead extension, 100% overhead extension, sitting, standing, etc.), or alternatively.

[0118]

[0129] Instead of, or in addition to, the SN fatigue curve, FEM can output an assessment of one or more (e.g., four) categories of tissue damage: no damage, partial fracture, partial damage, and complete damage. FEM can also, or instead, output comparisons of various changes to work procedures and tools (e.g., evaluation of reduced range of motion, power tools with different weights or vibration frequencies, and the introduction of ergonomically designed assistive devices), which can lead to optimized task design guidance and / or recommended work intervals for specific tasks.

[0119]

[0130] Method 600 may include providing recommendations, as in 620. Such recommendations may be at least partially based on the execution of FEM (from 614), one or more modes (from 616), models (from 618), or both. For example, such recommendations may be at least partially based on stress on soft tissue, strain on soft tissue, or both. Such recommendations may be specifically tailored to the anatomical shape of the subject. In one embodiment, such recommendations may be specifically tailored to a particular subject (e.g., one whose parameters were measured in 604). In another embodiment, such recommendations may be specifically, or instead, tailored to different (e.g., all) subjects on which the task can be performed. Such recommendations may impose limitations on the task being performed to prevent the subject from damaging soft tissue. For example, such recommendations may limit the amount of weight lifted during the task, the number of times the task is performed (e.g., repetitions), the duration of the task, or a combination thereof.

[0120]

[0131] As a result, these recommendations may help improve repetitive stress injuries in workers performing part of a manufacturing process. Alternatively, Method 600 may be used to improve repetitive stress injuries in exercisers performing a training program. The exercises may be modified according to the recommendations generated by Method 600. In general, Method 600 may be implemented to improve repetitive stress injuries in any type of process involving repetitive movements by humans, not limited to manufacturing or exercise processes.

[0121]

[0132] Method 600 can be used to improve injuries to any of various soft tissues. According to some examples, Method 600 can be used to improve injuries to tendons or tendon complexes. Examples of such tendons and tendon complexes are previously presented in connection with Figures 1-3. Alternatively, Method 600 can be used to improve injuries to connective tissue or musculoskeletal soft tissues. Generally, non-limiting examples of soft tissues to which the method can be applied include tendons, tendon complexes, intervertebral discs (spine), and ligaments.

[0122]

[0133] Method 600 may also include comparing the output of the FEM with that of an empirical study, as in 622. For example, this may include comparing the modes (from 616) and / or models (from 618) with iterative stress data in an empirical study. This may help verify the accuracy of the FEM output. Method 600 then returns to step 614, where the FEM may be run again (iteratively). In one embodiment, the FEM may be modified (e.g., improved) at least in part based on the comparison (from 622) so that additional iterations of the FEM produce different (e.g., more accurate) outputs.

[0123]

[0134] This disclosure also provides various systems and computer program products or machine-readable media. In some embodiments, for example, the methods described herein are optionally implemented or facilitated at least partially using systems, distributed computing hardware and applications (e.g., cloud computing services), telecommunications networks, communication interfaces, computer program products, machine-readable media, electronic storage media, software (e.g., machine-executable code or logical instructions), etc. For illustrative purposes, Figure 12 provides a schematic diagram of an exemplary system suitable for use when implementing at least an embodiment of the methods disclosed herein. As illustrated, system 1200 includes at least one controller or computer, for example, server 1205 (e.g., search engine server), which includes a processor 1204 and memory, storage device or memory 1206, and one or more other communication devices 1214 (e.g., client-side computer terminals, telephones, tablets, laptops, or other mobile devices), which are located remotely from server 1205 and communicate with server 1205 via a telecommunications network 1212, for example, the Internet or other internetworks. The communication device 1214 typically includes an electronic display (e.g., an Internet-enabled computer) that communicates with, for example, a server 1205 computer via a network 1212, wherein the electronic display includes a user interface (e.g., a graphical user interface (GUI), a web-based user interface, etc.) for displaying the results of performing the method described herein. In certain embodiments, the communication network also includes the physical transfer of data from one location to another using, for example, a hard drive, a thumb drive, or other data storage mechanism.System 1200 also includes a program product 1208 stored in one or more different types of memory (e.g., memory 1206 of server 1205) that is readable by a computer or machine-readable medium, such as server 1205, to facilitate executable files by, for example, one or more other communication devices, such as communication device 1214 (for example, schematically shown as a desktop or personal computer). In some embodiments, System 1200 also optionally includes a server 1210 associated with at least one database server, such as a server 1210 associated with an online website where data (e.g., control sample or comparator result data, indexed customized treatment methods, etc.) is stored, either directly or via server 1205. System 1200 also optionally includes one or more other servers located remotely from server 1205, each optionally associated with one or more database servers 1210 that are located remotely or locally with respect to each of the other servers. The other servers can provide services beneficial to geographically dispersed users and can enhance geographically distributed operations.

[0124]

[0135] As will be understood by those skilled in the art, the memory 1206 of server 1205 optionally includes volatile memory and / or non-volatile memory (including, for example, RAM, ROM, magnetic disks, or optical disks). Also, although shown as a single server, the illustrated configuration for server 1205 is illustrative, and it will be understood by those skilled in the art that other types of servers or computers configured according to various other methodologies or architectures are also available. Server 1205 schematically shown in Figure 12 represents a server, server cluster, or server farm, and is not limited to individual physical servers. A server site may be deployed as a server farm 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, demand, and capacity requirements of system 1200. It will also be understood by those skilled in the art that the communication device 1214 (e.g., computer) in these embodiments may be, for example, a laptop, desktop, tablet, personal digital assistant (PDA), mobile phone, server, or other type of computer. As is known to those skilled in the art, network 1212 may include part of the Internet, intranet, telecommunications network, extranet, or World Wide Web and / or local network or other area network of multiple computers / servers communicating with one or more other computers via a communication network. Furthermore, as will be understood to those skilled in the art, the exemplary program product or program product 1208 may optionally be in the form of microcode, a program, a cloud computing format, a routine, and / or a symbolic language, which provide one or more ordered sets of operations that control the functions of hardware and direct its operation. According to the exemplary embodiment, the program product 1208 also does not need to reside entirely in volatile memory, but can be selectively loaded according to various methodologies as needed, as is known and understood to those skilled in the art.

[0125]

[0137] Furthermore, as will be understood by those skilled in the art, the terms “computer-readable medium” or “machine-readable medium” refer to any medium involved in providing instructions to a processor for execution. For illustrative purposes, the terms “computer-readable medium” or “machine-readable medium” include, for example, distribution media, cloud computing formats, intermediate storage media, computer execution memory, and other media or devices capable of storing program product 1208, on which various aspects of the functions or processes of this disclosure are implemented for computer reading. “Computer-readable medium” or “machine-readable medium” can take many forms, including, but are not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical disks or magnetic disks. Volatile media include dynamic memory, for example, the main memory of a given system. Transmission media include coaxial cables, copper wires, and optical fibers, including wires that constitute a bus. Transmission media can also take the form of acoustic and light waves, for example, those generated in particular during radio and infrared data communications. Exemplary forms of computer-readable media include floppy disks, flexible disks, hard disks, magnetic tapes, flash drives, or other magnetic media; CD-ROMs, other optical media; punch cards, paper tapes, other physical media with hole patterns; RAM, PROMs, and EPROMs, FLASH-EPROMs, other memory chips or memory cartridges; carriers; or other computer-readable media.

[0126]

[0138] Program product 1208 is optionally copied from a computer-readable medium to a hard disk or similar intermediate storage medium. When program product 1208 or a part thereof is executed, it is optionally loaded from a distribution medium, intermediate storage medium, etc., into the execution memory of one or more computers and configured to operate according to various functions or methods. All such operations are well known, for example, to those skilled in the art of computer systems.

[0127]

[0139] To further explain, in certain embodiments, the present invention 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 an action. Such action may include receiving one or more images, including the anatomical shape of a subject. Such action may also include measuring or receiving a number of parameters of the anatomical geometric shape of the subject using one or more sensors attached to the subject. Such action may also include receiving a first set of material properties for the soft tissue of the subject. Such action may also include identifying a second set of material properties that characterize the soft tissue while the subject is performing a task. Such action may also include determining strain, stress, or both on the soft tissue based at least in part on one or more images, parameters, a first set of material properties, and a second set of material properties.

[0128]

[0140] System 1200 also typically includes additional system components configured to perform various embodiments of the methods described herein. In some of these embodiments, one or more of these additional system components are located remotely from server 1205 and communicate with server 1205 via the telecommunications network 1212. In other embodiments, one or more of these additional system components are located locally and communicate with server 1205 (i.e., if the telecommunications network 1212 is not available) or communicate directly with, for example, a communication device 1214 (e.g., a computer).

[0129]

[0141] List of clauses

[0130]

[0142] Clause 1. A method (600) for modeling soft tissue (530, 532) may include receiving one or more images (500) of a first object (502) including the anatomical geometric shape, wherein the anatomical geometric shape includes soft tissue (530, 532). The method may also include measuring several parameters of the anatomical geometric shape of the first object (502) using one or more sensors (520A, 520B, 520C) attached to the first object (502). The method may also include receiving a first set of material properties for the soft tissue of the first object (502), a second object, or both. The method may also include identifying a second set of material properties characterizing the soft tissue while the first object (502) is performing a task, wherein the second set of material properties is different from the first set of material properties. The method may also include determining strain on soft tissue, stress on soft tissue, or both, based at least in part on one or more images (500), a plurality of parameters, a first set of material properties, and a second set of material properties.

[0131]

[0143] Clause 2. The image (500) includes magnetic resonance imaging, computed tomography imaging, ultrasound imaging, or a combination thereof, and the first subject (502) includes a living mammal, the method (600) described in Clause 1.

[0132]

[0144] Clause 3. Multiple parameters are measured in the manner described in Clause 1 or 2 (600) while the first subject (502) is performing the task.

[0133]

[0145] Clause 4. The method described in any one of Clauses 1 to 3 (600), wherein multiple parameters include forceful movement, posture, repetition, duration, vibration, or a combination thereof.

[0134]

[0146] Clause 5. The method(600) described in any one of Clauses 1 to 4, wherein multiple parameters describe the behavior of soft tissue in a first object(502) that is subjected to forceful movement, posture, repetition, duration, vibration, or a combination thereof while performing a task.

[0135]

[0147] Clause 6. The method according to any one of Clauses 1 to 5, wherein the first set of material properties includes the in-plane modulus, the out-of-plane modulus, and Poisson's ratio (600).

[0136]

[0148] Clause 7. The method according to any one of Clauses 1 to 6, 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 (600).

[0137]

[0149] Clause 8. The method(600) of any one of Clauses 1 to 7, further comprising generating a model(710, 720, 730, 740, 910, 920, 1100) that describes soft tissue based at least partially on strain, stress, or both.

[0138]

[0150] Clause 9. Model (710, 720, 730, 740, 910, 920, 1100) includes a 3D model of soft tissue, and Model (710, 720, 730, 740, 910, 920) specifies the direction of load and strain, stress or both, applied to the soft tissue during the task, as described in Clause 8 (600).

[0139]

[0151] Clause 10. The method according to Clause 8 (600), wherein the model (1100) includes an SN fatigue curve that predicts the number of cycles performed by the first subject (502) during the task before soft tissue damage occurs.

[0140]

[0152] Clause 11. A method (600) for modeling soft tissue (530, 532) includes receiving one or more images (500) of the anatomical geometric shape of a subject (502), the images (500) including magnetic resonance images, computed tomography images, ultrasound images, or a combination thereof, the anatomical geometric shape including soft tissue (530, 532), and the subject (502) including a living mammal. The method also includes measuring several parameters of the anatomical geometric shape of the subject (502), the parameters being measured using one or more sensors (520A, 520B, 520C) attached to the subject (502), the parameters being measured while the subject (502) is performing a task, and the parameters being measured including forceful movement, posture, repetition, duration, and vibration. The method may also include receiving a first set of material properties for the soft tissue (530, 532) of the object (502), wherein the first set of material properties includes the in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. The method may also include identifying a second set of material properties that characterize the soft tissue (530, 532) during the task, wherein the second set of material properties differs from the first set of material properties, and the second set of material properties includes the isotropic properties of the soft tissue (530, 532), the anisotropic properties of the soft tissue (530, 532), the nonlinear behavior of the soft tissue (530, 532), the estimated damage state of the soft tissue (530, 532), or a combination thereof. The method may also include running a finite element model based at least in part on one or more images (500), a plurality of parameters, the first set of material properties, and the second set of material properties. The method may also include determining the strain, stress, or both of the soft tissue (530, 532) based at least in part on the execution of a finite element model. The method may also include generating a model (710, 720, 730, 740, 910, 920, 1100) describing the soft tissue (530, 532) based at least in part on the determined strain, determined stress, or both.

[0141]

[0153] Clause 12. The method according to Clause 11 (600), wherein a first set of material properties is measured using shear wave elastography, ultrasound, or both.

[0142]

[0154] Clause 13. The method according to Clause 11 or 12 (600), wherein a second set of material properties is measured using external imaging techniques, material sample composition techniques, or both.

[0143]

[0155] Clause 14. The method according to any one of Clauses 11 to 13 (600), wherein the model (710, 720, 730, 740, 910, 920, 1100) includes a first SN fatigue curve (1110) describing the soft tissue (530, 532) when healthy, and a second SN fatigue curve (1120) describing the state of the soft tissue (530, 532) depending on the task.

[0144]

[0156] Clause 15. A method (600) as described in any one of Clauses 11 to 14, which provides recommendations that impose limitations on tasks performed by the subject (502) in order to prevent damage to the subject (502) to soft tissue (530, 532), further comprising providing recommendations that are at least in part on stress on the soft tissue (530, 532), strain on the soft tissue (530, 532), or both.

[0145]

[0157] Clause 16. A system for characterizing the behavior of mammalian soft tissues (530, 532) comprises a plurality of sensors (520A, 520B, 520C) configured to be attached to a human subject (502), wherein the plurality of sensors (520A, 520B, 520C) are configured to measure a plurality of parameters while the human subject (502) is performing a repetitive task, the plurality of parameters may include forceful movement, posture, repetition, duration, and vibration. The system may also comprise a computing system (1200) configured to perform an action, which may also include receiving one or more images (500) of the anatomical geometry of the human subject (502), wherein the images (500) include magnetic resonance images, computed tomography images, and ultrasound images, and the anatomical geometry includes soft tissues (530, 532). The operation may also include receiving multiple parameters from multiple sensors (520A, 520B, 520C). The operation may also include receiving a first set of material properties for the soft tissue (530, 532) of an object (502) which is a human, wherein the first set of material properties includes the in-plane linear modulus, the out-of-plane linear modulus, and Poisson's ratio. The operation may also include identifying a second set of material properties that characterize the soft tissue (530, 532) during an iterative task, wherein the second set of material properties differs from the first set of material properties, and the second set of material properties includes the isotropic properties of the soft tissue (530, 532), the anisotropic properties of the soft tissue (530, 532), the nonlinear behavior of the soft tissue (530, 532), and the estimated damage to the state of the soft tissue (530, 532). The operation may also include running a finite element model based at least in part on one or more images (500), a plurality of parameters, a first set of material properties, and a second set of material properties. The operation may also include predicting strain modes, stress modes, vibration modes, and failure modes of soft tissue (530, 532) based at least in part on running the finite element model.The operation may also include generating models (710, 720, 730, 740, 910, 920, 1100) that describe the soft tissue (530, 532) based at least in part on strain modes, stress modes, vibration modes, fracture modes, or combinations thereof.

[0146]

[0158] Clause 17. The system described in Clause 16, wherein a first set of material properties is measured using shear wave elastography, ultrasound, or both.

[0147]

[0159] Clause 18. The system described in Clause 16 or 17, wherein a second set of material properties is measured using dynamic mechanical analysis.

[0148]

[0160] Clause 19. A system as described in any one of Clauses 16 to 18, wherein the model (710, 720, 730, 740, 910, 920, 1100) includes a first SN fatigue curve (1110) describing the soft tissue (530, 532) before the iterative task is performed, and a second SN fatigue curve (1120) describing the soft tissue (530, 532) in response to the iterative task being performed.

[0149]

[0161] Clause 20. A system according to any one of Clauses 16 to 19, which provides recommendations that impose limitations on repetitive tasks performed by a human object (502) in order to protect the human object (502) from damage to soft tissue (530, 532), further comprising providing recommendations that are at least in part based on strain modes, stress modes, vibration modes, and failure modes.

[0150] All documents described herein, including priority documents and / or test procedures, are incorporated herein by reference, to the extent that they do not conflict with the foregoing. While the forms of the embodiments have been illustrated and described, as will be apparent from the general descriptions and specific embodiments above, various modifications can be made without deviating from the spirit and scope of this disclosure. Therefore, this disclosure is not intended to be limited thereto. Similarly, the term “comprising” is to be considered synonymous with the term “including.” Similarly, whenever the transitional phrase “comprising” precedes a composition, element, or group of elements, it is understood that the same composition or group of elements may be considered using the transitional phrases “substantially consisting of,” “consisting essentially of,” “selected from a group consisting of,” or “I,” prior to any reference to the composition, element, or group of elements, and vice versa. For example, the terms “comprising,” “consisting essentially of,” and “consisting of” also include the results of combinations of elements listed after the terms.

[0151]

[0163] While the above disclosure has provided some detail with illustrations and examples for clarity and understanding, it will be apparent to those skilled in the art that various modifications of form and detail can be made without departing from the true scope of this disclosure and that these modifications can be implemented within the scope of the attached claims. For example, all methods, systems, and / or components thereof, or other embodiments can be used in various combinations. All patents, patent applications, websites, other publications, or documents referred to herein are incorporated by reference in whole for any purpose, just as each individual item is specifically and individually indicated to be incorporated by reference.

Claims

1. A method (600) for modeling soft tissue (530, 532), Receiving one or more images (500) including the anatomical geometric shape of a first object (502), wherein the anatomical geometric shape includes soft tissue (530, 532), Measuring multiple parameters of the anatomical geometric shape of the first object (502) using one or more sensors (520A, 520B, 520C) attached to the first object (502), To receive a first set of material properties for the soft tissue of the first object (502), the second object, or both thereof, Identifying a second set of material properties that characterize the soft tissue while the first object (502) is performing the task, wherein the second set of material properties is different from the first set of material properties, and The one or more images (500) are meshed and loaded into a finite element model, and then the multiple parameters, the first set of material properties, and the second set of material properties are loaded into the finite element model. After this, the finite element model is run to determine the strain on the soft tissue, the stress on the soft tissue, or both. Methods that include...

2. The method (600) according to claim 1, wherein the image (500) includes a magnetic resonance image, a computed tomography image, an ultrasound image, or a combination thereof, and the first subject (502) includes a living mammal.

3. The method according to claim 1 or 2 (600), wherein the plurality of parameters are measured while the first object (502) is performing the task.

4. The method according to any one of claims 1 to 3 (600), wherein the plurality of parameters include forceful movement, posture, repetition, duration, vibration, or a combination thereof.

5. The method according to any one of claims 1 to 4 (600), wherein the plurality of parameters describe the behavior of the soft tissue in response to the first object (502) being subjected to vigorous movement, posture, repetition, duration, vibration, or a combination thereof while performing the task.

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

7. The method according to any one of claims 1 to 6 (600), 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 any one of claims 1 to 7 (600), further comprising generating a model (710, 720, 730, 740, 910, 920, 1100) describing the soft tissue based at least partially on the strain, the stress, or both.

9. The method of claim 8 (600), wherein the model (710, 720, 730, 740, 910, 920, 1100) includes a 3D model of the soft tissue, and the model (710, 720, 730, 740, 910, 920) specifies the direction of the load applied to the soft tissue during the task, the direction of the strain, the stress, or both.

10. The method according to claim 8 (600), wherein the model (1100) includes an S-N fatigue curve that predicts the number of cycles performed by the first object (502) during the task before soft tissue damage occurs.

11. A method (600) for modeling soft tissue (530, 532), Receiving one or more images (500) including the anatomical geometric shape of a subject (502), wherein the images (500) include magnetic resonance images, computed tomography images, ultrasound images, or a combination thereof, the anatomical geometric shape includes soft tissue (530, 532), and the subject (502) includes living mammals. Measuring a plurality of parameters of the anatomical geometric shape of the object (502), wherein the plurality of parameters are measured using one or more sensors (520A, 520B, 520C) attached to the object (502), the plurality of parameters are measured while the object (502) is performing a task, and the plurality of parameters include forceful movement, posture, repetition, duration, and vibration. Receiving a first set of material properties for the soft tissue (530, 532) of the subject (502), wherein the first set of material properties includes the in-plane modulus, out-of-plane modulus, Poisson's ratio, or a combination thereof. The task involves identifying a second set of material properties that characterize the soft tissue (530, 532) during the task, wherein the second set of material properties differs from the first set of material properties, and includes isotropic properties of the soft tissue (530, 532), anisotropic properties of the soft tissue (530, 532), nonlinear behavior of the soft tissue (530, 532), estimated damage state of the soft tissue (530, 532), or a combination thereof. The process involves meshing the one or more images (500) and loading them into a finite element model, and then loading the multiple parameters, the first set of material properties, and the second set of material properties into the finite element model, and then running the finite element model. Based at least partially on the execution of the finite element model, the strain on the soft tissue (530, 532), the stress on the soft tissue (530, 532), or both, and To generate a model (710, 720, 730, 740, 910, 920, 1100) describing the soft tissue (530, 532) based at least partially on the determined strain, the determined stress, or both. Methods that include...

12. The method according to claim 11 (600), wherein the first set of material properties is measured using shear wave elastography, ultrasound, or both.

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

14. The aforementioned models (710, 720, 730, 740, 910, 920, 1100) When healthy, a first S-N fatigue curve (1110) describes the soft tissues (530, 532), and A second S-N fatigue curve (1120) describing the state of the soft tissue (530, 532) according to the aforementioned task. The method according to any one of claims 11 to 13, including (600).

15. The method (600) of any one of claims 11 to 14, further comprising providing a recommendation that imposes limitations on the task performed by the object (502) in order to prevent the object (502) from damage to the soft tissue (530, 532), wherein the recommendation is provided that is at least partially based on stress on the soft tissue (530, 532), strain on the soft tissue (530, 532), or both.

16. A system for characterizing the behavior of mammalian soft tissues (530, 532), wherein the system is A plurality of sensors (520A, 520B, 520C) configured to be attached to a human subject (502), wherein the plurality of sensors (520A, 520B, 520C) are configured to measure a plurality of parameters while the human subject (502) is performing a repetitive task, the plurality of parameters including forceful movement, posture, repetition, duration, and vibration, and the plurality of sensors (520A, 520B, 520C) A computing system (1200) configured to perform the operation and Equipped with, The aforementioned operation, Receiving one or more images (500) including the anatomical geometric shape of the subject (502), which is a human being, wherein the images (500) include magnetic resonance images, computed tomography images, and ultrasound images, and the anatomical geometric shape includes soft tissue (530, 532). Receiving the multiple parameters from the multiple sensors (520A, 520B, 520C), Receiving a first set of material properties for the soft tissue (530, 532) of the subject (502) which is a human being, wherein the first set of material properties includes the in-plane linear modulus, the out-of-plane linear modulus, and Poisson's ratio. Identifying a second set of material properties characterizing the soft tissue (530, 532) during an iterative task, wherein the second set of material properties differs from the first set of material properties, and the second set of material properties includes isotropic properties of the soft tissue (530, 532), anisotropic properties of the soft tissue (530, 532), nonlinear behavior of the soft tissue (530, 532), and estimated damage to the state of the soft tissue (530, 532). The process involves meshing the one or more images (500) and loading them into a finite element model, and then loading the multiple parameters, the first set of material properties, and the second set of material properties into the finite element model, and then running the finite element model. Predicting strain modes, stress modes, vibration modes, and failure modes of the soft tissue (530, 532) based at least partially on the execution of the finite element model, and To generate a model (710, 720, 730, 740, 910, 920, 1100) that describes the soft tissue (530, 532) based at least partially on the strain mode, the stress mode, the vibration mode, the failure mode, or a combination thereof. A system that includes this.

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

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

19. The aforementioned models (710, 720, 730, 740, 910, 920, 1100) A first S-N fatigue curve (1110) describing the soft tissue (530, 532) before performing the aforementioned repetitive task, and A second S-N fatigue curve (1120) describing the soft tissue (530, 532) in response to the aforementioned repetitive task. The system according to any one of claims 16 to 18, including the system described in any one of claims 16 to 18.

20. A system according to any one of claims 16 to 19, which provides recommendations that impose limitations on repetitive tasks performed by the human subject (502) in order to protect the human subject (502) from damage to the soft tissue (530, 532), further comprising providing recommendations that are at least in part based on the strain mode, the stress mode, the vibration mode, and the fracture mode.

Images