Patent Description:
The embodiments herein generally relate to injury risk prediction and adaption, and more particularly to, a system and method for injury risk prediction and corresponding corrective action for high contact type activity.

High contact type activities are those that can produce a high impact on contact during an activity. High demand sports such as soccer, basketball and football fall in the category of high contact type activities. Anterior cruciate ligament (ACL) injuries are the most predominant form of knee injuries faced by athletics participating in high demand sports.

Typically, most of ACL injuries are non-contact type, and are sustained due to side cutting maneuvers or when landing from a jump. Single leg landing is one such athletic maneuver, associated with most high demand sports, which has one of the highest risks of ACL injury. ACL injuries cause devastating consequences to subjects' quality of life along with inducing a lifetime financial burden to society. According to an estimate, lifetime financial burden of said injuries to society is estimated to be around US$<NUM> billion annually when treated with ACL reconstruction and US$<NUM> billion when treated with rehabilitation. Even with ACL reconstructions, subjects usually have abnormal strength, proprioception, balance, and Neuro-muscular control patterns as well as increased risks for re-injury in post-reconstructed ACLs.

In addition to the above mentioned, the inventors have recognized certain technical problems associated with currently available solutions pertaining to injuries sustained during high contact type activity. For example, the current solutions have little or no capability to observe such injuries during in-vivo testing. For preventing sports related injuries, understanding of injury mechanisms and identification of risk factors along with development and evaluation of injury prevention strategies are required. The current solutions lack understanding and identification of the injury mechanisms and risk factors for ACL injury. Consequently current ACL injury prevention solutions have limitations that prevent them from being effective. <CIT> discloses systems and methods that measure and analyze a user's movement during a specific activity, then provide immediate, focused feedback as to how the user can modify the movement. Howeve4r, D1 is silent on the specific parameters employed by the present application.

The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below.

In view of the foregoing, an embodiment herein provides methods and systems for injury risk prediction and corresponding corrective action for high contact type activity. The method includes generating a personalized full body musculoskeletal model to depict the knee and ankle joint behavior of a subject during the contact type activity, via one or more hardware processors. Further, the method includes simulating one or more contact type activities using the personalized full body musculoskeletal model, via the one or more hardware processors. Furthermore, the method includes identifying a plurality of injury biomarkers based on the one or more contact type activities, via the one or more hardware processors. Also, the method includes analyzing a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during contact type activity calculated with respect to the plurality of injury biomarkers to predict said risk of injury, via the one or more hardware processors. Moreover, the method includes generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correction action against the predicted risk of injury, via the one or more hardware processors. The plurality of optimal muscle co-activation parameters are indicative of muscle synergy during the one or more contact type activities.

In another aspect, a system for injury risk prediction and corresponding corrective action for high contact type activity is provided. The system includes one or more memories; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are capable of executing programmed instructions stored in the one or more memories to generate a personalized full body musculoskeletal model to depict the knee and ankle joint behavior of a subject during one or more contact type activity. Furthermore, the one or more hardware processors are configured by the instructions to simulate the contact type activities using the personalized full body musculoskeletal model. Moreover, the one or more hardware processors are configured by the instructions to identify a plurality of injury biomarkers based on the one or more contact type activities. Also, the one or more hardware processors are configured by the instructions to analyze a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during the one or more contact type activities calculated with respect to the plurality of injury biomarkers to predict said risk of injury. Also, the one or more hardware processors are configured by the instructions to generate, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correction action against the predicted risk of injury, the plurality of optimal muscle co-activation parameters indicative of muscle synergy during the one or more contact type activity.

In yet another aspect, a non-transitory computer-readable medium having embodied thereon a computer program for executing a method for injury risk prediction and corresponding corrective action for high contact type activity is provided. The method includes generating a personalized full body musculoskeletal model to depict the knee and ankle joint behavior of a subject during one or more contact type activities, via one or more hardware processors. Further, the method includes simulating a one or more contact type activities using the personalized full body musculoskeletal model. Furthermore, the method includes identifying a plurality of injury biomarkers based on the one or more contact type activities. Also, the method includes analyzing a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during the one or more contact type activities calculated with respect to the plurality of injury biomarkers to predict said risk of injury. Moreover, the method includes generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correction action against the predicted risk of injury. The plurality of optimal muscle co-activation parameters are indicative of muscle synergy during the one or more contact type activity.

The same numbers are used throughout the drawings to reference like features and modules.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

High contact type activities are those that can produce a high impact on contact during an activity. High demand sports such as soccer, basketball and football fall in the category of high contact type activities. Anterior cruciate ligament (ACL) injuries are the most predominant form of knee injuries faced by athletics participating in high demand sports. Typically, most of ACL injuries are non-contact type, and are sustained due to side cutting maneuvers or when landing from a jump. Single leg landing is one such athletic maneuver, associated with most high demand sports, which has one of the highest risks of ACL injury.

Conventionally available solutions pertaining to ACL injuries have little or no capability to observe such injuries during in-vivo testing. For preventing sports related injuries, understanding of injury mechanisms and identification of risk factors along with development and evaluation of injury prevention strategies are required. The current solutions lack understanding and identification of the injury mechanisms and risk factors for ACL injury. Consequently current ACL injury prevention solutions have limitations that prevent them from being effective.

Various embodiments presented herein disclose system and method for injury risk prediction and corresponding corrective action for high contact type activity. The disclosed method and system combine motion analysis of the subject and musculoskeletal modelling techniques to provide estimates of muscle forces during a landing manoeuvres during the high contact type activity. The system facilitates neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system.

In an embodiment, the method includes designing a model for predicting chance of injury during contact type sports/ exercise, and an optimal neuro-muscular controller to adjust muscle synergy so that the predicted injury can be reduced or avoided. The disclosed model for predicting the chance of said injury risk simulates various high contact type activities and determines effect of said activities on full body knee and ankle biomechanics. The embodiments identify potential injury biomarkers of said activities' injuries and determine the effect of said injury biomarkers on changing muscle activation of associated muscles. Based on said determination and biomechanical modeling, a neuro-muscular controller associated with the disclosed system adjusts the optimal synergistic muscle activation parameters to prevent the injury condition. Herein, it will be noted that the simulations of the said activity risks can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. Additionally, the simulation of injury mechanism using neuro-muscular modelling is done to determine optimal and synergistic muscle activations that can be induced in athletic training to prevent the injury. An example of the musculoskeletal model developed in accordance with disclosed embodiment is illustrated and described further with reference to <FIG>.

Referring to <FIG>, an example personalized full body musculoskeletal model <NUM> developed to simulate single leg drop jump activity is illustrated. For the brevity of description, the personalized full body musculoskeletal model may hereinafter be referred to as a model <NUM>. Further, <FIG> illustrate expanded view of positioning of the additional ligaments in the knee joint region <NUM>, <NUM>, <NUM>, respectively of the simulated model <NUM>. For example <FIG> illustrates an anterior cruciate ligament, <FIG> illustrates a posterior cruciate ligament, and <FIG> illustrates a medial cruciate ligament and a patellar ligament.

The methods and systems are not limited to the specific embodiments described herein. In addition, the method and system can be practiced independently and separately from other modules and methods described herein. Each device element/module and method can be used in combination with other elements/modules and other methods.

The manner, in which the system and method for injury risk prediction and corresponding corrective action for high contact type activity shall be implemented, has been explained in details with respect to the <FIG>. While aspects of described methods and systems for injury risk prediction and corresponding corrective action for high contact type activity can be implemented in any number of different systems, utility environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).

Referring now to <FIG>, a network implementation <NUM> of system <NUM> for injury risk prediction and corresponding corrective action for high contact type activity is illustrated, in accordance with an embodiment of the present subject matter. The system is adapted to develop a musculoskeletal model with enhanced knee joint anatomy, including <NUM> degrees of freedom (DoF) knee joint and <NUM> ligament bundles. Additionally, the system <NUM> is caused to simulate high contact type activities such as a drop jump exercise to induce an injury condition and subsequent analysis of muscle condition, joint kinematics and kinetics associated with risk of ACL injury. Further, the system <NUM> develops a neuro-muscular controller which generates optimal muscle co-activation parameters to adapt the subject's body so that the chances of predicted injury is reduced or avoided.

Although the present subject matter is explained considering that the system <NUM> is implemented for injury risk prediction and corresponding corrective action for high contact type activity, it may be understood that the system <NUM> may not be restricted to any particular machine or environment. The system <NUM> can be utilized for a variety of domains where PPG signal quality assessment is to be determined. The system <NUM> may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a smart phone, a wearable device, and the like.

Herein, the system <NUM> may acquire an input data for modelling a personalized full body musculoskeletal model via devices and/or machines <NUM>-<NUM>, <NUM>-<NUM>. <NUM>-N, collectively referred to as devices <NUM> hereinafter. In an embodiment, the devices <NUM> may include high end motion measurement system such as VICON™ or equivalent optical marker based motion analysis, Ground Reaction force plate, myoelectric devices, and so on. In an embodiment, the devices <NUM> may be embodied in handheld electronic device, a mobile phone, a smartphone, a portable computer, a PDA, and so on. The devices <NUM> are communicatively coupled to the system <NUM> through a network <NUM>, and may be capable of providing input data to the system <NUM>.

In one implementation, the network <NUM> may be a wireless network, a wired network or a combination thereof. The network <NUM> can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network <NUM> may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network <NUM> may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

In an embodiment, the system <NUM> may be embodied in the computing device <NUM>. The system <NUM> may also be associated with a data repository <NUM> to store at least data required for modeling the personalized full body musculoskeletal model. Additionally or alternatively, the data repository <NUM> may be configured to store data and/or information generated during injury risk prediction and corresponding corrective action for high contact type activity. The repository <NUM> may be configured outside and communicably coupled to the computing device <NUM> embodying the system <NUM>. Alternatively, the data repository <NUM> may be configured within the system <NUM>. An example implementation of the system <NUM> for injury risk prediction and corresponding corrective action for high contact type activity is described further with reference to <FIG>.

<FIG> illustrates a block diagram of an exemplary system <NUM> for injury risk prediction and corresponding corrective action for high contact type activity, in accordance with an example embodiment. The system <NUM> may be an example of the system <NUM> (<FIG>). In an example embodiment, the system <NUM> may be embodied in, or is in direct communication with the system, for example the system <NUM> (<FIG>). The system <NUM> includes or is otherwise in communication with one or more hardware processors such as a processor <NUM>, at least one memory such as a memory <NUM>, and an I/O interface <NUM>. The processor <NUM>, memory <NUM>, and the I/O interface <NUM> may be coupled by a system bus such as a system bus <NUM> or a similar mechanism.

The hardware processor <NUM> may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor <NUM> is configured to fetch and execute computer-readable instructions stored in the memory <NUM>.

The I/O interface <NUM> may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like The interfaces <NUM> may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. Further, the interfaces <NUM> may enable the system <NUM> to communicate with other devices, such as web servers and external databases. The interfaces <NUM> can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces <NUM> may include one or more ports for connecting a number of computing systems with one another or to another server computer. The I/O interface <NUM> may include one or more ports for connecting a number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory <NUM> includes a plurality of modules <NUM> and a repository <NUM> for storing data processed, received, and generated by one or more of the modules <NUM>. The modules <NUM> may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types. Additionally, the other modules <NUM> may include programs or coded instructions that supplement applications and functions of the system <NUM>. The repository <NUM>, amongst other things, includes a system database <NUM> and other data <NUM>. The other data <NUM> may include data generated as a result of the execution of one or more modules in the modules <NUM>. Herein, the memory for example the memory <NUM> and the computer program code configured to, with the hardware processor for example the processor <NUM>, causes the system <NUM> to perform various functions described herein under.

As discussed above, for injury risk prediction and corresponding corrective action for high contact type activity, the system <NUM> is caused to generate a personalized full body musculoskeletal model of a subject. The personalized full body musculoskeletal model (hereinafter referred to as a musculoskeletal model) depicts the knee and ankle joint behaviour of the subject during the contact type activity. An example representation of a musculoskeletal model along with the knee and ankle joint behaviour of the subject is illustrated with reference to <FIG>.

In an example embodiment, the system <NUM> may utilize an OpenSim™ platform to generate the musculoskeletal model. In an embodiment, the system <NUM> is caused to improvise generic model, for example as the one generated by the OpenSim™ platform in terms of ligament placement and a modified 3DoF knee to facilitate a sliding motion. In an embodiment, musculoskeletal model includes at least <NUM> muscle actuators a <NUM> DoF knee with ligament connectivity, and <NUM> ligament bundles designed and placed with respect to anatomical constraints and bounds. In an example embodiment, a group of <NUM> different ligaments naming, ACL (anterior and posterior), PCL (anterior and posterior), Fibular Collateral ligament (lateral), Patella-femoral ligament and Tibial collateral ligament (anterior, intermediate and posterior) may be modelled to depict the knee and ankle joint behaviour during a high contact type activity, such as sports activity.

The system <NUM> simulates one or more contact type activities using the musculoskeletal model. For example the system <NUM> may be caused to simulate, by using the musculoskeletal model, a jump (for instance, a single leg drop jump) from a four different heights, ranging from <NUM> to <NUM> on a solid platform. In typical scenarios, unsupervised and sudden jump such as the one simulated herein may cause tear in ACL ligament. Herein, the purpose of said simulation is to induce an injury scenario during the high contact type activity.

Whether an injury has occurred or not is reflected by one or more injury biomarkers. In an embodiment, the system <NUM> identifies a plurality of injury biomarkers based on the one or more contact type activities. Examples of said injury biomarkers include, but are not limited to hip abduction angle, knee abduction angle, loading at knee joint, and so on. Said injury biomarkers can be calculated from forward and inverse dynamics. In an embodiment, the plurality of injury biomarkers are identified through inverse dynamics simulation of the musculoskeletal model. These parameters have been analyzed in OpenSim™ platform to check whether there is any chance of injury, incurred from the high contact type activity.

The system <NUM> analyzes a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups of the subject during the contact type activity calculated with respect to the plurality of injury biomarkers so as to predict said risk of injury. In an embodiment, said plurality of parameters may include subject's muscle condition, joint kinematics and kinetics associated with said risk. In case of an ACL injury, the plurality of participating muscles groups includes quadriceps Hamstring, and Tibialis anterior and Gastrocnemius. Herein, a change in an activation function of participating muscles groups produce detectable changes in the plurality of injury biomarkers.

Once the system <NUM> predicts chances of injury, calculated with respect to specific injury biomarkers including hip abduction angle and knee loading, a neuro-muscular controller is designed, which adjusts muscle co-activation and synergy of two groups of participating muscles, namely Quadriceps: hamstring and gastrocnemius: Tibilias Anterior.

In an embodiment, the system <NUM> generates, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correction action against the predicted risk of injury. Herein, the plurality of optimal muscle co-activation parameters are indicative of muscle synergy during the contact type activity. For example, co-activation may refer to simultaneous contraction of agonist and antagonist muscle pair around a specific joint to provide better joint stability. Optimally adjusting the co-activation ratio of these muscle groups effect the outcome of the selected injury biomarkers. For example, increasing quadriceps to hamstring ratio decreases hip abduction angle reduces chances of ACL injury.

In an embodiment, the system <NUM> designs a plurality of such neuro-muscular controllers by varying the co-activation level during the high contact type activity. From amongst the plurality of neuro-muscular controllers, the system <NUM> may then select a neuro-muscular controller that may be capable of generating the plurality of the optimal muscle co-activation parameters.

The system <NUM> selects a plurality of combinations of the plurality of muscles groups. For example, specific muscles identified may include quadriceps, hamstring and tibialis anterior and gastrocnemius. A change in activation functions of these muscle groups produce maximum detectable changes in the biomarkers selected as ACL injury predictor. Herein, from the analysis of the injury biomarkers, the injury biomarkers Hip abduction angle, knee adduction load, knee flexion and ankle inversion angle have been identified through inverse dynamics simulation of the model developed. In an embodiment, the inverse dynamic simulation may be performed by using an Inverse Dynamic (ID) tool associated with the OpenSim platform. The ID Tool determines generalized forces (e.g., net forces and torques) at the joints responsible for the contact type activity. Given the kinematics (e.g., states or motion) describing the movement of a model and perhaps a portion of the kinetics (e.g., external loads) applied to the model, the ID Tool uses these data to perform an inverse dynamic analysis. Classical mechanics mathematically expresses the mass-dependent relationship between force and acceleration, F = ma, with equations of motion. The ID tool solves these equations, in the inverse dynamics sense, to yield the net forces and torques at each joint which produce the movement.

In an embodiment, the system <NUM> varies activation of selected muscle groups by assigning incremental values to the co-activation ratios corresponding to a plurality of combinations of muscle groups, thereby optimally adjusting the co-activation ratio of the plurality of muscle groups. For example, the system <NUM> may assign incremental values to the co-activation ratios corresponding to said as combinations: combination <NUM>: Quad: ham <NUM>, TA: LG <NUM>; combination <NUM>: Quad: ham <NUM>, TA: LG <NUM>, and so on. For each of the plurality of combinations, the system <NUM>.

For each selected combination, the system <NUM> calculates a muscle tendon dynamics using muscle characteristics and force, velocity relation. Thereafter, the system <NUM> computes multibody dynamic equation and derives the kinematic parameters (including acceleration, velocity and position) that the simulated model needs to follow for the calculated muscle excitation. Said calculation is termed as forward dynamics loop, as is shown and described in <FIG>. This loop is repetitively calculated for the plurality of combinations till the best result is obtained. Additionally, the system <NUM> calculates values of the plurality of injury biomarkers and compares till a set of predefined constraints are satisfied. In an embodiment, the set of predefined constraints includes hip abduction angle to be minimum, knee flexion angle to be maximum, knee abduction loading to be minimum, ankle inversion angle to be minimum. After computation of all the combinations, the combination which best matches the constraint are selected by the system as the optimal muscle co-activation parameters to avoid the predicted injury. Herein, it will be noted that the disclosed system <NUM> can be used to train athletes or to prepare therapy regiments to avoid or rehabilitate from injuries.

In an embodiment, the system <NUM> includes a real time biofeedback module, based on observations done on sensing (motion parameters and EMG signals), to minimize the error between simulated movement and actual movement of the subject. For example, in case best result for a subject is obtained at a combination of Quad: ham activation <NUM>, Ta: LG activation: <NUM>, then said values can be considered as final output of the system <NUM>. Depending upon said values, specific training can be registered for the subject, so that the activation function is controlled and risk of injury is reduced. In an embodiment, the system <NUM> may provide the plurality of optimal muscle co-activation parameters to the musculoskeletal model. The musculoskeletal model may re-simulate the plurality of contact type activities based on the plurality of optimal muscle co-activation parameters. An example process flow for injury risk prediction and corresponding corrective action for high contact type activity is further described with reference to <FIG>.

<FIG> illustrates a process flow <NUM> for injury risk prediction and corresponding corrective action for high contact type activity, in accordance with an example embodiment. The disclosed method predicts risk of injury during contact type activity such as a high fall scenario performed by a subject (for example, a sports person), and subsequently generates a corrective model, which can be used to retrain the muscles and reduce the chances of injury to the subject.

In an embodiment, the process flow <NUM> is initiated by generating a simulation platform <NUM> including a full body musculoskeletal model <NUM> at <NUM>. Accordingly, said simulation platform <NUM> includes the developed musculoskeletal model <NUM> along with a multibody dynamics engine <NUM>, inherent to OpenSim™ software. The musculoskeletal model <NUM> along with the multibody dynamics <NUM> constitutes the simulation platform <NUM>. Said simulation platform <NUM> is used to simulate any particular ambulatory activity or exercise. The developed musculoskeletal model <NUM> is simulated to jump (for example, a single leg drop jump as illustrated in <FIG>) from various different heights, ranging from <NUM> to <NUM> on a solid platform at <NUM>. Unsupervised and sudden jump from such heights may cause tear in the ACL ligament. Herein, purpose of the simulation by the simulation platform <NUM> is to induce an injury scenario.

An improvement of the disclosed musculoskeletal model as compared to the conventional models is in terms of ligament placement and a modified 3DoF knee to facilitate sliding motion. In an embodiment, a group of <NUM> different ligaments naming, ACL (anterior and posterior), PCL (anterior and posterior), Fibular Collateral ligament (lateral), Patella- femoral ligament and Tibial collateral ligament (anterior, intermediate and posterior) are modelled to depict the knee and ankle joint behaviour during the contact type activity such as ACL injury.

Weather an injury has occurred or not is reflected by one or more injury biomarkers. Said injury biomarkers are calculated from forward and inverse dynamics, as will be explained further. Using the inverse dynamics simulation of the musculoskeletal model, one or more injury biomarkers including, but not limited to, hip abduction angle, knee adduction load, knee flexion and ankle inversion angle are identified at <NUM>. Herein, for the purpose of experimentation, ratio of specific muscle groups to be controlled, are identified. Examples of said muscle group's ratios include, but are not limited to, quadriceps-hamstring ratio and tibialis anterior-gastrocnemius ratio. It will be understood that a change activation function of said muscle groups produce a substantial (or maximum) detectable changes in the injury biomarkers selected as ACL injury predictor.

The one or more injury biomarkers are analyzed to check whether there is any chance of ACL injury, incurred from the jump. In an embodiment, the one or more injury biomarkers are analysed in OpenSim platform. Once the musculoskeletal model predicts chances of injury, calculated with respect to said injury biomarkers, a neuro-muscular controller is designed at <NUM>, which facilitates in adapting and/or preventing the predicted injury. Herein, it will be understood that adaptation to prevent injury is based on the concept of muscle synergy.

Different muscle characteristics may be compared based on varying the level of co-activation of participating muscle groups. In an embodiment, the muscle co-activation and synergy of two groups of muscle are adjusted. In an example embodiment, said muscle groups include, but are not limited to, Quadriceps: hamstring and gastrocnemius: Tibilias Anterior. Optimally adjusting the co-activation ratio of these muscle groups effect the outcome of the selected injury biomarkers. For example, increasing quadriceps to hamstring ratio decreases hip abduction angle, which in turn, reduces chances of ACL injury.

In an embodiment, the co-activation ratio of muscle groups are optimally selected to best select a neuro-muscular controller from amongst a plurality of neuro-muscular controllers, which can provide chances of injury reduction to the predicted injury model. In an example embodiment, three such neuro-muscular controllers are designed, by varying the co-activation level during the high contact activity, for example drop jump activity. From the different possible combinations, the optimal controller is selected to facilitate adjustments to avoid the predicted injury. An example of optimally selecting the neuro-muscular controller is described further with reference to <FIG>.

Referring now to <FIG> an example process flow <NUM> for optimally selecting the neuro-muscular controller is described in accordance with an example embodiment. In the present example, four muscles have been selected and including the Quadriceps: hamstring and gastrocnemius: Tibilias Anterior. Muscle co-activation and synergy of two groups of muscle are adjusted to effect the outcome of the selected injury biomarkers. In an example implementation, activation of the selected muscles can vary from <NUM> to <NUM>, precision step being <NUM>. So, in incremental steps of <NUM>, <NUM>, and so on, activation for all these muscle are varied in <NUM> steps. For aforementioned four muscles, grouped in pair (for example, a pair of Quadriceps: hamstring and a pair of gastrocnemius: Tibilias Anterior muscles), around ninety different possible combinations are obtained. For instance, combination <NUM>: Quadriceps: hamstring <NUM>, gastrocnemius: Tibilias Anterior <NUM>; Combination <NUM>: Quadriceps: hamstring <NUM>, gastrocnemius: Tibilias Anterior <NUM>, and so on.

For each of the selected combinations, the neuro-muscular controller calculates the muscle tendon dynamics using muscle characteristics and force, velocity relation at <NUM>. Further, the neuro-muscular controller computes multibody dynamic equation and derives the kinematic parameters (acceleration, velocity and position) that the simulated model needs to follow for the calculated muscle excitation. Said calculation is termed as forward dynamics loop, as shown in <FIG>. The loop is repetitively calculated for all combination till the best result is obtained. The forward dynamic loop is explained further in description below.

In forward dynamics, a mathematical model describes how coordinates and their velocities change due to applied forces and torques (moments). From Newton's second law, it can be described that the accelerations (rate of change of velocities) of the coordinates in terms of the inertia and forces applied on the skeleton as a set of rigid-bodies: <MAT>.

The net muscle moments, τm, in turn, are a result of the moment arms, R(q), multiplied by muscle forces, f, which are a function of muscle activations, a, and muscle fiber lengths, l, and velocities, l̇.

Muscle fiber velocities are governed by muscle contraction dynamics, Λ, which is dependent on the current muscle activations and fiber lengths as well as the coordinates and their velocities. Activation dynamics, A , describes how the activation rates, ȧ, of the muscles respond to input neural excitations, x, generally termed the model's controls. These form a set of differential equations that model musculoskeletal dynamics. Muscle force and muscle activations are based on a Hill type muscle model, used in Opensim which correlates muscle activation with muscle force or torque.

The force-producing properties of muscle are complex and for simplicity, lumped-parameter, dimensionless muscle models, capable of representing a range of muscles with different architectures, are most commonly used in the dynamic simulation of movement. In a complex musculoskeletal model, the model can be actuated by <NUM> or more muscle-tendon units, each of which is represented as a Hill-type contractile element in series with a tendon.

The 'thelen model' (Model used in OpenSim™) uses a standard equilibrium muscle model based on the Hill model. The muscle-tendon complex consists of three components: a contractile element (CE), a parallel element (PE), and a series element (SE). The muscle force generated is a function of three factors: the activation value (a), the normalized length of the muscle unit, and the normalized velocity of the muscle unit. The functions describing the force generated by a muscle as its length varies are called the active length curve (AL) for the contractile element and the passive length curve (PL) for the parallel element.

The parameters used to characterize each muscle are maximum isometric force, optimal muscle fiber length, tendon slack length, maximum contraction velocity, and pennation angle. During a forward dynamic simulation, the muscle force is calculated using two states: the activation value and the muscle fiber length.

Musculoskeletal dynamics equations are solved using <NUM>th order Runge Kutta Feldberg integrator. The integrator applies muscle actuator as the force/ torque supply and calculates a defined trajectory of motion, based on which, the kinematic parameters are calculated.

Referring back to <FIG>, the method incorporates a feedback loop, where the outputs of the neuro-muscular controller at <NUM> is introduced in the simulation platform <NUM> and the simulation platform <NUM> is again simulated with the predefined high contact level activity. Depending upon the control action of the neuro-muscular controller at <NUM>, injury risks can be lowered due to muscular adaptation to prevent the injury. In an embodiment, the total simulation model can be validated with actual data (without inducing injury condition) for proper calibration of the model. For this purpose, joint kinematic and kinetic data are measured from a subject at <NUM> and fitted to OpenSim model. OpenSim recalculates the system dynamics and muscle activation using the in-build Computed muscle control (CMC) block <NUM>. Muscle conditions adapted by the controller can be used to train athletes to reduce chances of such injury. This methodology can also be used extensively in therapy planning and rehabilitation training. In an embodiment, the method may include providing real time biofeedback at <NUM> based on observations done on sensing (motion parameters and EMG signals), to minimize the error between simulated movement and actual movement of the subject.

<FIG> illustrates a flow diagram of a method <NUM> for risk prediction and corrective action for a contact type activity, according to some embodiments of the present disclosure. The method <NUM> may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method <NUM> may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method <NUM> is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method <NUM>, or an alternative method. Furthermore, the method <NUM> can be implemented in any suitable hardware, software, firmware, or combination thereof. In an embodiment, the method <NUM> depicted in the flow chart may be executed by a system, for example, the system <NUM> of <FIG>. In an example embodiment, the system <NUM> may be embodied in an exemplary computer system.

Referring to <FIG>, in the illustrated embodiment, the method <NUM> is initiated when at <NUM>, the method <NUM> includes generating a personalized full body musculoskeletal model to depict the knee and ankle joint behaviour of a subject during the contact type activity. An example of the personalized full body musculoskeletal model is illustrated and described with reference to <FIG>. At <NUM>, the method <NUM> includes simulating a plurality of contact type activities using the personalized full body musculoskeletal model. At <NUM>, the method <NUM> includes identifying a plurality of injury biomarkers based on the plurality of contact type activities. At <NUM>, the method <NUM> includes analyzing a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during contact type activity calculated with respect to the plurality of injury biomarkers to predict said risk of injury. At <NUM>, the method <NUM> includes generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correction action against the predicted risk of injury. The plurality of optimal muscle co-activation parameters are indicative of muscle synergy during the contact type activity.

<FIG> illustrates experimental results for observation of the muscle activity, joint kinetics and kinematics, in accordance with an example embodiment. <FIG> illustrates muscle activity of nine sets of muscles including emimembranosus, semitendinosus, bicep femoris, rectus femoris, Vastus Medialis, Vastus Intermediaries, Vastus Lateralis, Gastrocnemius, Soleus, Tibilias Anterior and Tibilias Posterior during a drop jump activity from four different heights. <FIG> illustrates Joint kinetics including Hip adduction, rotation, flexion, Knee Adduction, rotation, flexion, Ankle flexion, and inversion during drop jump activity from four different heights. <FIG> illustrate Joint kinematics including Hip adduction, rotation, flexion, Knee Adduction, rotation, flexion, Ankle flexion, and inversion during drop jump activity from four different heights. It will be noted that the terminology Adduction, flexion, inversion, and rotation have been referred to as 'add', 'flex', 'inv' and 'rot', respectively in <FIG>. Based on the observation of the muscle activity, joint kinetics and kinematics, illustrated in <FIG>, a plurality of injury biomarkers are identified. ACL injury is indicated by :.

Major cause of ACL injury is the high activation of Quadriceps group of muscles and relative inactivity of Hamstring group of Muscles. Gastronomies muscles are antagonist of ACL, higher activation leads to ACL injury. ACL injury can thus be reduced by increasing the activity of Hamstring group of muscles, decreasing Gastronomies activation and increased Soleus activation.

In an experiment, three neuro-muscular controllers are designed based on varying co-activation of Quadriceps-Hamstring (QH) ratio and Gastronemius-Tibialis Anterior (GTA) ratio. Synergistic muscle activation based neuro-muscular controller design adjusts level of activation, adapting to a better neuro-muscular control to prevent injury.

The Controller response in terms of joint kinematics and Kinetics is analyzed to determine the optimal controller selection, as is illustrated further in <FIG>.

<FIG> illustrates controller response to injury biomarkers, in accordance with an example embodiment. Herein, the desired trend is low hip adduction angle (less than <NUM> degree) illustrated in <FIG>, high knee flexion illustrated in <FIG> and low knee joint loading illustrated in <FIG>.

The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules.

Claim 1:
A processor-implemented method (<NUM>) for risk prediction and corrective action for a contact type activity, the method comprising:
generating a personalized full body musculoskeletal model (<NUM>) with a <NUM> degrees of freedom (DoF) knee with ligament connectivity to facilitate a sliding motion, upon acquiring an input data from a device, (<NUM>), to depict a knee and ankle joint behavior of a subject during the contact type activity, via one or more hardware processors;
simulating one or more contact type activities using the personalized full body musculoskeletal model (<NUM>, <NUM>) in a simulation platform, (<NUM>) via the one or more hardware processors;
identifying a plurality of injury biomarkers based on the one or more contact type activities, (<NUM>), via the one or more hardware processors, wherein the plurality of injury biomarkers are identified through inverse dynamics simulation of the personalized full body musculoskeletal model;
analyzing, via the one or more hardware processors, a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during the one or more contact type activities, including muscle condition, joint kinematics and kinetics associated with said risk, calculated with respect to the plurality of injury biomarkers to predict said risk of injury; (<NUM>),
designing a plurality of neuro-muscular controllers, which adjust muscle co-activation and synergy of a combination of groups of participating muscles, by varying the co-activation level during the contact type activity,
performing, for each combination
calculating a muscle tendon dynamics using muscle characteristics and force, velocity relation,
computing multibody dynamic equation and deriving kinematic parameters, the kinematic parameters comprising acceleration, velocity and position;
selecting the neuro-muscular controller capable of generating a plurality of optimal muscle co-activation parameters, which muscle co-activation parameters are indicative of muscle synergy during the one or more contact type activities, (<NUM>)
generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by the selected designed neuro-muscular controller via the one or more hardware processors, to adapt the plurality of participating muscle groups for providing the correct action against the risk of injury being predicted.