Means and methods for reducing lower limb injuries

Abstract

The use of sensors to study the internal and external forces of the distal lower limb during movement is described. An inertial measurement unit (IMU) and electromyography (EMG) sensors are combined to calculate ankle neuromuscular forces and torques, which are compared to plantar pressure and torque measurements using a pressure mat. Image tracking data is then used to inform a machine learning model that predicts actual distance movement from video-recorded movements. A mobile application is developed that allows users to view simulations of their own movements.

Description

[Technical field]

[0001] The present disclosure relates generally to orthopedics, and more specifically, to means and methods for reducing the risk of distal lower extremity injuries.

[0002] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 62 / 951,969, filed December 20, 2020, the disclosure of which is incorporated herein by reference in its entirety. [Background technology]

[0003] Lower limb injuries account for more than 50 percent of injuries in athletes. The majority of lower limb injuries are ankle injuries and ligament sprains, but can also include injuries to the medial gastrocnemius of the calf muscle. These injuries have a direct effect on the natural gait of the injured person. The muscles of the body contract in different ways to move and stabilize certain parts of the body. The natural human gait is equipped with a "shock absorber" that transmits and transforms the energy generated by the muscles and reflected from the ground to the body. The energy reflected from the ground is transformed into different forms of energy (such as heat and force) and processed by different layers of tissues and muscles that act and affect the movement throughout the joints of the lower limbs. Changes in the alignment of the body through the foot affect the efficiency of movement. When the lower limbs are injured, this impact movement is hindered and rarely fully recovered, resulting in impaired gait and reduced athletic performance.

[0004] Quantifying neuromuscular effort is extremely useful in determining an individual's propensity for injury. There is general agreement in the art that both extrinsic and intrinsic factors play a role in determining an individual's propensity for injury, particularly to the lower extremities. Extrinsic risk factors include, but are not limited to, playing level, skill level, shoe type, ankle support device, playing surface, etc. Intrinsic risk factors include, but are not limited to, age, sex, menstrual cycle (females only), previous injury and inadequate rehabilitation, body size, limb dominance, flexibility (including general joint laxity, ankle and knee laxity, muscle stiffness, range of motion), limb circumference, muscle strength, postural stability, anatomical alignment, foot morphology, aerobic capacity, etc.

[0005] Traditionally, orthopedic researchers, clinicians, and athletic trainers have sought ways to reduce the risk of lower extremity injuries through a deeper understanding of foot function. Several validated performance tests are known in the art, including the Rootian Theory, Tissue Stress Theory, and Preferred Movement Pathway Theory. The Rootian Theory is a systematic classification of foot function and foot pathologies based on alignment and proposes the use of orthotics to correct abnormal alignment and prevent and treat foot injuries. The Tissue Stress Theory focuses on the pathological magnitude of stress acting within the structural elements of the foot and lower extremity. Conversely, the Preferred Movement Pathway Theory focuses on the influence of both alignment and stress on distal lower extremity function to ensure muscles move in the preferred pathway. Unfortunately, although these theories are widely accepted, they do not determine internal and external muscle action or joint torque and displacement for the purpose of measuring the risk of lower extremity injury.

[0006] Attempts have been made to solve this problem, but have been unsuccessful. One exemplary attempt can be found in U.S. Patent No. 7,582,064 B2, which generally discloses a system and method for foot assessment. Although this disclosure generally teaches how to assess the associated correlation between foot displacement and propensity for lower extremity injury, this disclosure does not determine internal and external muscle work or joint torques and displacements for the purpose of measuring the risk of lower extremity injury.

[0007] Another attempt can be found in U.S. Patent No. 9,829,493 B2, which generally discloses a method and apparatus for assessing risk of joint injury. Although this disclosure generally teaches test kits and methods for identifying possible interventions to reduce risk of joint injury, this disclosure emphasizes biomechanical movement patterns and does not determine internal and external muscle work or joint torque and displacement for the purpose of measuring risk of lower extremity injury.

[0008] Yet another attempt can be found in U.S. Patent No. 6,192,329 B1, which generally discloses a method and apparatus for assessing injury risk in children. This disclosure considers computer and physical anatomical models to assess injury risk, but does not determine internal and external muscle work or joint torques and displacements for the purpose of measuring lower extremity injury risk.

[0009] Other disclosures, such as U.S. Patent No. 9,008,784 A1 and U.S. Patent Application Publication No. 2018 / 0028109 A1, teach wearable devices such as knee braces to prevent risk of lower extremity injuries, particularly knee injuries, but these disclosures are limited to the use of physical devices and do not measure actual injury risk.

[0010] Various attempts have been made in the related art to measure the risk of lower limb injury, but have been unsuccessful. Therefore, there is a need for a more effective means of determining how efficiently a person maintains neuromuscular stability of the lower limb within a particular environment (i.e., orthotics, shoes, braces, or prosthetics) in order to measure the risk of lower limb injury. Summary of the Invention

[0011] It should be understood that all embodiments in this disclosure are provided as illustrative and non-limiting of many possible embodiments. Furthermore, terms such as "is," "can," and "will" are used synonymously and interchangeably with terms such as "may," "may provide for," and "it is contemplated that the present invention may."

[0012] Additionally, all elements listed by name, such as inertial measurement unit ("IMU"), electromyogram sensor, neuromuscular, fatigue, orthotics, lower extremities, etc., are meant to include or encompass all equivalents of such elements, and such equivalents are contemplated for each element named herein.

[0013] For purposes of summary, certain aspects, advantages, and novel features of the invention are provided herein. It is to be understood that not all such aspects, advantages, or novel features are provided in any particular embodiment. Thus, the disclosed subject matter may be embodied or implemented to achieve or optimize one aspect, advantage, or novel feature or group of features without achieving all aspects, advantages, or novel features taught or suggested.

[0014] In view of the aforementioned shortcomings inherent in the known technology, the present invention provides more effective means and methods for identifying correlated physiological factors related to internal and external joint function and determining how effectively an individual maintains neuromuscular stability of the lower extremities within a particular environment (i.e., an orthotic, shoe, brace, or prosthesis).The overall object of the present invention, as described in more detail below, is to measure and reduce the risk of injury to the lower extremities.

[0015] The features of the invention believed to be novel are particularly pointed out and distinctly claimed in the concluding portion of the specification. By way of non-limiting example, the invention provides novel means and methods for identifying correlated physiological factors related to internal and external joint function and determining how effectively a person maintains neuromuscular stability of the lower extremities within a particular environment (i.e., an orthotic, shoe, brace, or prosthesis). These and other features, aspects, and advantages of the invention will be better understood with reference to the following drawings and detailed description.

[0016] The present invention relates to lower limb injury prediction. By utilizing IMU, computer vision, EMG sensors and novel injury prediction algorithms, individuals can identify key correlated physiological factors related to internal and external joint functioning and gauge their risk of lower limb injury.

[0017] According to one aspect of the present invention, there is provided a muscle activity correlation method for calculating ground reaction forces which are used to know both the observed ankle forces and torques in the following equations:

number

[0018] According to another aspect of the present invention, a muscle activity correlation method calculates ankle torque for an individual subject. The method first establishes a relationship between muscle activity and ground reaction forces and uses position information from an IMU and EMG measured muscle activity or contraction. A total torque force analysis of an individual subject's ankle is performed as follows, taking into account the IMU in relation to the ground contact point, the ankle joint, and the foot's center of mass:

number

[0019] According to another aspect of the present invention, a muscle activity correlation method is provided for calculating moment stability at the ankle of an individual subject utilizing the force vector observed in the following equation:

number

[0020] According to a further aspect of the present invention, the data from the IMU and EMG sensors are measured simultaneously, as the MVC matches the frequency of readings between the IMU and EMG sensors. [Brief description of the drawings]

[0021] Those skilled in the art will appreciate that the drawings, described below, are for illustrative purposes only and are not intended to limit the scope of the present teachings in any way.

[0022] [Figure 1] FIG. 1 is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 2A] FIG. 2A is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 2B] FIG. 2B is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 3A] FIG. 3A is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 3B] FIG. 3B is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 4A] FIG. 4A is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 4B] FIG. 4B is a graphical depiction of data collected during the course of a first experiment relating to at least one embodiment of the present invention. [Figure 5A] FIG. 5A is a graphical depiction of data collected during the course of a second experiment relating to at least one embodiment of the present invention. [Figure 5B] FIG. 5B is a graphical depiction of data collected during the course of a second experiment relating to at least one embodiment of the present invention. [Figure 6] FIG. 6 is a perspective view of at least one embodiment of the present invention. [Figure 7] FIG. 7 is a graphical depiction of data collected during the course of a third experiment relating to at least one embodiment of the present invention. [Figure 8]FIG. 8 is a schematic diagram of at least one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] (Abbreviations and definitions) IMU: As used herein and conventionally in the art, the term "IMU" refers to an inertial measurement unit, which is an electronic device that combines accelerometers, gyroscopes, and sometimes magnetometers to measure and report the specific forces, angular velocity, and sometimes orientation of an object. IMUs are also referred to as motion / motion capture technology.

[0024] EMG: As used herein, the term "EMG" or "electromyography" refers to an electrodiagnostic medical technique used to evaluate and record the electrical activity produced by skeletal muscles, performed with an instrument called an electromyograph.

[0025] Sensor: As used herein, the term "sensor" is used interchangeably with IMU and EMG (i.e., an IMU sensor or an EMG sensor) and refers to a device that measures the small electrical signals generated by an individual subject's muscles when the muscles move, such as when the individual subject performs a muscle activity.

[0026] Pressure Mat: As used herein, the term "pressure mat" refers to a device that measures the pressure distribution between any two mating or contacting surfaces. As used herein, a pressure mat is used to measure the plantar pressure and force exerted by an individual subject during specific muscle activities.

[0027] Plantar Pressure: As used herein, the term "plantar pressure" refers to the pressure field acting between the foot and its supporting surface during certain muscle activities.

[0028] Concentric Contraction: As used herein, the term "concentric contraction" refers to a type of muscle activation that produces tension in an individual subject's muscle while the required muscle movement causes the muscle to shorten (or contract).

[0029] Statistical Analysis Software: As used herein, the term "statistical analysis software" refers to tools that aid in the collection and analysis of statistically-based data to provide scientifically-based insights into patterns and trends.

[0030] Ground reaction force: As used herein, the term "ground reaction force" is a force that acts in the opposite direction to a movement. For example, when a person jumps, there is a downward reaction force acting against the ground as he or she pushes off the ground and launches into the air.

[0031] Parity Plot: As used herein, the term "parity plot" refers to a scatter plot graph comparing experimental data with tabulated data, where "x" is the tabulated data and "y" is the corresponding experimental value.

[0032] Motion Mapping: As used herein, the term "motion mapping" refers to the correlation between muscle movements and specific activities.

[0033] Neuromuscular Stability: As used herein, the term "neuromuscular stability" refers to the repetitive peripheral nervous system formation of stereotyped patterns of muscle contraction that are primarily used to provide stability to the skeletal system against gravity. For example, human posture, the static and dynamic upright posture of the musculoskeletal system, is a direct result of consistent "neuromuscular stability" driven by the peripheral nervous system.

[0034] Numerical Representation: As used herein, the term "numerical representation" refers to a quantitative value that directly relates to muscle activity or a neuromuscular measure of a physiological variable collected from an association vector for muscle activity.

[0035] Medial gastrocnemius or gastrocnemius: As used herein, the term "medial gastrocnemius" refers to the medial head of the gastrocnemius muscle, which is located at the back of the human lower leg and extends from the knee to the heel, and is also collectively referred to as the "calf muscles." As referred to herein, the medial gastrocnemius is at greater risk of injury than the lateral gastrocnemius head.

[0036] Calf Raise: As used herein, the term "calf raise" is a method of training the gastrocnemius, tibialis posterior, peroneus, and soleus muscles of the lower leg. As referred to herein, the specific movement performed is plantar flexion or ankle extension.

[0037] Computer Vision or Computer Vision Technology: As used herein, the terms "computer vision" or "computer vision technology" are an interdisciplinary field of science that deals with how computers gain advanced understanding from digital images and videos and use input data to predict specific outcomes. Computer vision technology is also known as behavior / motion capture technology.

[0038] As used herein, words and terms referring to bones, muscles, or tendons located in the lower extremities of the body are intended to have their ordinary meanings known in the art.

[0039] The present invention overcomes the limitations of the prior art by providing a new, more effective means for determining how effectively a person maintains neuromuscular stability of the lower extremities within a particular environment (i.e., orthotic, shoe, brace, or prosthesis) in order to measure and reduce the risk of lower extremity injury.

[0040] All dimensions specified in this disclosure are for illustrative purposes only and are not intended to be limiting. Additionally, the proportions shown in these figures are not necessarily to scale. As will be understood by those of skill in the art with reference to this disclosure, the actual dimensions and proportions of an embodiment or elements of an embodiment disclosed in this disclosure will be determined by its intended use.

[0041] It should be understood that the drawings and associated description are provided to illustrate contemplated embodiments of the invention and are not intended to limit the scope of the invention. References in this specification to "one embodiment" or "an embodiment" are intended to indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearance of the phrase "in one embodiment" or "an embodiment" in various places in this specification are not necessarily all referring to the same embodiment.

[0042] Throughout the drawings, reference numbers are reused to indicate correspondence between referenced elements and the first digit of each reference number indicates the drawing in which the element first appears.

[0043] As used in this disclosure, the term "comprise", and variations of terms such as "comprising", "comprises", and "comprised" are not intended to exclude other additives, components, integers, or steps, unless the context otherwise requires.

[0044] In the following description, specific details are provided to provide a thorough understanding of the embodiments. However, those skilled in the art will appreciate that the embodiments can be practiced without these specific details. In order not to obscure the embodiments, well-known features, elements, or techniques may not be shown in detail.

[0045] Aspects of the present teachings can be further understood with reference to the following examples, which should not be construed in any way as limiting the scope of the present teachings.

[0046] The premise of this study is that traditional methods of identifying risk for lower extremity injury do not efficiently determine how a person maintains neuromuscular stability within a given environment.

[0047] The experimental results generally disclosed herein were conducted to test methods that have the potential to accurately, appropriately, and efficiently measure a subject's risk of lower extremity injury.

[0048] Detailed Description of the Invention The present invention discloses a method and system for detecting and reducing the risk of lower extremity injury. The independent and dependent variables relate to the muscle range of motion of an individual subject and thus the likelihood of risk of lower extremity injury. The independent variables may include the weight and muscle composition of the individual subject. The dependent variable may be the selected physical activity or the selected time interval during which each muscle activity is performed.

[0049] The present invention identifies correlations between independent and dependent muscle activity variables ("Muscle Activity Correlation"). Specifically, Muscle Activity Correlation identifies key correlated physiological factors related to internal and external joint motion and determines the degree of risk of lower extremity injury that an individual subject may sustain.

[0050] Analysis of the independent and dependent variables influencing muscle activation correlations was performed by first calculating the ground reaction forces for specific muscle activations. Calculating the ground reaction forces identifies both the ankle forces and torques during specific muscle activations.

number

[0051] In a preferred embodiment of the present invention, muscle movements and activity of an individual subject can be observed in a controlled environment and joint displacements of the individual subject's joints connected to muscles and / or muscle groups of interest can be determined during controlled and specified muscle activity.

[0052] The total torque force analysis acting on an individual subject's ankle considers the IMU in relation to the contact point with the ground, the ankle joint and the foot's center of mass, and determines the joint displacements of the joints connected to the muscle and / or muscle group of interest during a specific muscle activity, which can be observed as follows:

number

[0053] Additionally, muscle activity correlations determine an individual subject's neuromuscular performance during specific muscle activities, as well as neuromuscular stability and joint displacement of the muscle and / or muscle group of interest, to measure an individual subject's risk of lower extremity injury.

[0054] Ankle moment vector M A and the moment vector M of the contact point between the ground and the foot G is the ground reaction force data F G Combined with the force vectors, this is used to determine the neuromuscular performance of an individual subject by calculating the moment stability of the ankle for that individual subject.

number

[0055] Neuromuscular action takes into account the efferent and afferent actions of a muscle and / or muscle group of interest during a particular muscle activity.

[0056] As shown in Figure 1, muscle activity correlation measurements are observed during specific sets of activities at specific time intervals while each individual subject is using a commercially available pressure mat and while each individual subject is connected to an EMG. The muscle activity correlation measurements shown in Figure 1 identify eccentric and concentric contraction data collected from a subject while the subject is standing on a commercially available pressure mat while EMG sensors are simultaneously connected to the center of the subject's tibialis anterior muscle and the center of the subject's medial and lateral gastrocnemius muscles. The output of the pressure mat measurements and EMG measurements are displayed as a percentage of the user's standing force data or ground reaction force data, as shown in Figure 1.

[0057] As shown in Figure 2A and Figure 2B, the force data output from the pressure mat and EMG are compared to squat and calf raise movements for an individual subject. In the graphical depiction shown, the X-axis is the ground reaction force calculated using the muscle activity of the medial gastrocnemius (measured by maximal volar contraction) and the body weight of the individual subject, and the Y-axis is the ground reaction force measured using the pressure mat. When compared using a parity plot, as shown in Figure 2B, the force output from the pressure mat and EMG indicate a significantly higher similarity between the measurement of the primary muscle used to perform the calf raise (medial gastrocnemius) and the prediction of the calf raise due to the reduced influence of other muscle groups such as the peroneus longus, tibialis posterior, and tibialis anterior.

[0058] The correlation between force rate and muscle activity, i.e., increased muscle activity during a particular movement such as the calf raise, also increases plantar pressure, can be seen in Figures 1, 2A, and 2B and can be summarized as identifying the neuromuscular activity of an individual subject.

[0059] Attention is directed to Figures 3A and 3B, which show graphical depictions of the device peak measurements of Figure 2B. The reviewer can see that Figure 3A shows a complete weight fraction data set acquired by the pressure mat, and Figure 3B shows a complete weight fraction data set acquired by EMG during a particular period of movement. The reviewer can also see that the vertical lines in the device peak measurements of Figures 3A and 3B indicate the location of the peaks calculated by the statistical analysis software. Specifically, the peaks in Figure 3A represent when the subject is standing stationary during one complete repetitive movement, and the peaks in Figure 3B represent when the subject is at the highest position of the calf raise.

[0060] Torque is the rate of change that a joint experiences in relation to the force generated under the foot and the contraction of the muscles. In the embodiment shown in Figures 4A and 4B, the y-axis shows torque values ​​calculated using the ground reaction force measured by the pressure mat. The x-axis torque is calculated similarly to the y-axis, but the ground reaction force is calculated using the individual user's medical gastrocnemius muscle activity and the individual user's body weight.

[0061] Figures 1, 2A and 2B, 3A and 3B, and 4A and 4B show the accuracy of using EMG calculations rather than a commercially available pressure mat to determine neuromuscular work in individual subjects, and Figures 5A and 5B show neuromuscular stability in individual subjects.

[0062] Neuromuscular stability is achieved through specific muscle activity TIFF2025506546000008.tif889, TIFF2025506546000009.tif893 and The data is determined by analyzing the vector calculations in TIFF2025506546000010.tif792. Analysis of each individual subject's neuromuscular performance and neuromuscular stability (collectively "Movement Mapping Analysis") identifies the specific movements that pose the greatest potential injury risk for each individual subject. The data also establishes muscle fatigue thresholds and injury risk.

[0063] As shown in Figures 5A and 5B, by combining position data from the IMU with muscle activity or contraction measured by EMG, a relationship between muscle activity and ground reaction forces is established.

[0064] To enhance the correlation between force rate and muscle activity, and as a replaceable IMU alternative, computer vision algorithms (collectively, "Motion Capture Technology") are utilized to capture, process, and analyze visual data related to an individual subject's movements during defined exercises and physical goals.

[0065] Taken individually, both EMG and motion capture technologies are a means of providing an individual subject with immediate feedback on neuromuscular performance and stability. As shown in Figure 6, when IMU sensors are placed on the torso, lower back, left and right thighs, left and right ankles, and left and right shins, and EMG sensors are simultaneously placed on the left and right medial gastrocnemius and left and right lateral gastrocnemius of a subject, the sensors work in tandem to determine real-time displacement between each joint in a single neuromuscular performance or series of neuromuscular performances.

[0066] In a preferred embodiment of the present invention, the fusion of EMG and IMU sensors provides accurate measurements of body and muscle position, and isolated neuromuscular action, in a single signal as shown in FIG. 7, with evidence that the amount of muscle action and pressure are each increasing at the same time and at the same rate.

[0067] 7, EMG is fused with motion capture technology such as an IMU ("sensor fusion") to provide consistent and accurate peak amplitudes across many sampled data, and sensor fusion enables skeletal tracking of individual subject movements, joints, and limbs (not shown).

[0068] In a preferred embodiment of the present invention, the specific muscle activity is obtained TIFF2025506546000011.tif890, TIFF2025506546000012.tif888 and The data obtained from the vector calculation of TIFF2025506546000013.tif789 is assigned a numerical value to determine a baseline numerical representation based on neuromuscular stability. The predictive computer model is generated and customized to determine thresholds of neuromuscular activity, joint displacement, and neuromuscular stability for selected muscles and / or muscle groups of interest, thereby determining the probability of risk of neuromuscular injury within a given range of muscle activity.

[0069] The predictive computer model is further customized to analyze discrepancies between an individual subject's baseline numerical representation and additional data points obtained from neuromuscular work, joint displacement, or neuromuscular stability to generate specific muscle activity criteria to reduce the probability of risk of neuromuscular injury within a predetermined range of muscle activity. By creating a machine learning predictive computer model that generates a probability of risk of neuromuscular injury and muscle activity criteria to reduce the risk of neuromuscular injury without the aid of an EMG sensor, the present invention is distinguished from prior art that relies on the continuous use of EMG sensors to assess and / or determine risk of neuromuscular injury.

[0070] In another preferred embodiment, skeletal tracking data measurements from sensor fusion enable the algorithms of the present invention to predict muscle movements and risk of injury based on an individual subject's previous sensor fusion data measurements and joint displacement data. This data, when used in conjunction with a mobile application as shown in Figure 8, enables individual subjects to determine specialized algorithm-based exercises and activities that reduce their risk of injury. These algorithm-based exercises represent movements that are optimized for the individual subject based on the data presented. (Conclusions, Impact and Scope)

[0071] Although the present invention has been described in some detail, it is understood that the disclosure has been made by way of example and that other versions are possible. Since the above description may be modified in various ways without departing from the scope of the present invention, it is intended that all matter contained in the above description or shown in the accompanying drawings be used in an illustrative and not limiting sense. The spirit and scope of the appended claims should not be limited to the description of the preferred versions contained in this disclosure.

[0072] All features disclosed in this specification, including the claims, abstract, and drawings, and all steps of the disclosed methods or processes, may be combined in any combination, except combinations in which at least some features and / or steps are mutually exclusive. Each feature disclosed in the specification, including the claims, abstract, and drawings, may be replaced with alternative features serving the same, equivalent, or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each disclosed feature is merely one example of a generic series of equivalent or similar features.

[0073] Any claim element that does not expressly recite a "means" for performing a particular function or "steps" for performing a particular function is not to be construed as a "means" or "step" clause as specified in 35 U.S.C. §112.

[0074] While the invention has been disclosed in connection with numerous embodiments shown and described in detail, various modifications will be readily apparent to those skilled in the art.

Claims

1. 1. A method and system for detecting a risk of a lower extremity injury, the method comprising: (a) determining a joint displacement of at least one joint connected to a particular muscle during a particular muscle activity; (b) determining neuromuscular performance of the particular muscle during the particular muscle activity; and (c) determining the neuromuscular stability of said joints and specific muscles; and (d) determining a numerical representation of the neuromuscular activity and neuromuscular stability of the specific muscle and a specified joint displacement of the joint connected to the specific muscle; The method and system include:

2. The method of claim 1 , wherein the neuromuscular performance is determined by at least one sensor, the at least one sensor measuring efferent and afferent action of specific muscles.

3. The method of claim 2 , wherein the efferent and afferent actions of a particular muscle are determined by electrical potentials generated by the particular muscle during a particular muscle activity.

4. The method of claim 1 , wherein the joint displacements are determined by at least one sensor, the at least one sensor measuring body movement and skeletal frame orientation during specific muscle activities.

5. The method of claim 4 , wherein the orientation is determined by calculating specific forces, angular velocities and skeletal positions during specific muscle activities.

6. The method of claim 4 , wherein the at least one sensor is a motion capture device.

7. The method of claim 1 , wherein the neuromuscular stability is determined by a series of vector calculations derived from specific muscle activity.

8. The method of claim 1 , wherein the numerical representations of neuromuscular work, joint displacement and neuromuscular stability are determined by a series of vector calculations derived from specific muscle activations.

9. 10. The method of claim 8, wherein the numerical representations of the neuromuscular effort, joint displacement and neuromuscular stability of a particular muscle are received as at least one data sequence, and the data sequence is maintained in a history database.

10. 9. The method of claim 8, wherein a difference from the numerical representation of neuromuscular performance and joint displacement of a specific muscle is calculated between the numerical representation and at least one other data sequence, the at least one other data sequence including neuromuscular performance, joint displacement or neuromuscular stability for at least one specific muscle activity.

11. 11. The method of claim 10, wherein the numerical representations of the neuromuscular work, joint displacement and neuromuscular stability of a particular muscle determine a probability of risk of neuromuscular injury within a given range of muscle activity.

12. The method of claim 12, wherein the probability of risk of neuromuscular injury is declared when the difference exceeds a numerical expression.

13. 1. A system for detecting a risk of a lower extremity injury, the system comprising: one or more processors; a data store coupled to the one or more processors; the data store storing instructions that, when executed by the at least one processor, cause the one or more processors to receiving joint displacement data for at least one joint connected to a particular muscle during a particular muscle activity; receiving neuromuscular performance data of the particular muscle during the particular muscle activity; determining a neuromuscular stability of the joint and the particular muscle based on the received joint displacement data and the received neuromuscular performance data; generating a numerical representation of the neuromuscular stability of the joint and the particular muscle; A system that causes a system to perform an operation including:

14. 14. The system of claim 13, wherein the numerical representations of the neuromuscular effort of the particular muscle, the joint displacement and the neuromuscular stability are received as at least one data sequence, and the data sequence is maintained in a history database.

15. 15. The system of claim 14, wherein the system is configured to classify the at least one data sequence, the system being each trained to use the at least one data sequence as a training data set.

16. 14. The system of claim 13, wherein the system is configured to calculate a difference between the numerical representation and a second set of at least one other data sequence, the second set of at least one other data sequence comprising neuromuscular work, joint displacement or neuromuscular stability for at least one specific muscle activity.

17. 17. The system of claim 16, wherein the system analyzes the calculated difference between the numerical representation and the second set of at least one other data sequence and generates a muscle activity metric based on the calculated difference.

18. 20. The system of claim 17, wherein the derived muscle activity metrics are directed to reducing a risk of neuromuscular injury.

19. A non-transitory computer-readable medium storing a program, the program being readable by a processor. receiving joint displacement data for at least one joint connected to a particular muscle during a particular muscle activity; receiving neuromuscular performance data of the particular muscle during the particular muscle activity; determining a neuromuscular stability of the joint and the particular muscle based on the received joint displacement data and the received neuromuscular performance data; generating a numerical representation of the neuromuscular stability of the joint and the particular muscle; A non-transitory computer-readable medium for causing a program to be executed.

20. 20. The non-transitory computer-readable medium of claim 19, wherein the numerical representations of the neuromuscular effort of the particular muscle, the joint displacement, and the neuromuscular stability are received as at least one data sequence, and the data sequence is maintained in a history database.

Images