FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM FOR EVALUATION AND IDENTIFICATION OF OPTIMAL MOTION USING IN-SILICO MUSCULOSKELETAL
LOWER LIMB MODEL
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[001] The disclosure herein generally relates to the field of musculoskeletal models, and, more particularly, to a method and a system for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model.
BACKGROUND
[002] Musculoskeletal (MSK) system provides multiple functionalities such as support, movement, joint compliance, muscle redundancy, adaptation to change in load, and so on that involves integrated participation of different muscle, neural command and the skeletal system. With the knee joint being a crucial joint in the MSK system, knee related injuries and ailments are the most common MSK disorders, ranging from strained ligament, cartilage tear, traumatic injury, sports injury, age related degeneration and Osteoarthritis.
[003] For a good understanding of biomechanical processes of MSK system related to injury, wear and tear, adaptation to orthopedic treatment, rehabilitation and optimal implant design for treatment is knee arthroplasty (TKA), the knowledge of the loading conditions and contact forces in MSK system forms is essential. An important requirement in the treatment of any injuries, wear and tear of the MSK system is training and rehabilitation therapy.
[004] Through training and rehabilitation therapy, altered gait kinematics is an effective way of redistributing joint loads. Various gait modification techniques like walking at reduced speed, toe-out gait, medial thrust gait, increased trunk sway and assisted walking with pole supports have been reported to be efficient in reducing joint loads and contact forces. Hence the effect of gait modification techniques on the MSK system as a whole is an active area of research, wherein MSK modelling is very popular for its non-intrusive nature of modelling as it considers capabilities and limitations of a patient to identify a design suits that patient, without requiring any physical efforts of the patient. Availability of in vivo

data has revolutionized the use and applicability of the computational MSK models for predicting contact dynamics and aiding in surgical and functional rehabilitation treatments of the MSK disorders.
[005] Numerous techniques are available for knee modelling, wherein some state-of-the-art MSK models provide non-invasive way to calculate joint mechanics and predict loading based on kinetic and kinematic constraints but lead to a generic modeling framework neglecting the personalization effect. On the other hand, medical imaging based workflow followed by Finite element model (FEM) approaches introduce a patient specific feel but are extremely complicated, time consuming and computationally extensive. Hence there is a need for a less complicated and personalized modelling of MSK systems.
SUMMARY
[006] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method and a system for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model is provided. The system includes a memory storing instructions, one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to receive a first input from a subject, via the one or more hardware processors, wherein the first input is associated with a plurality of physical parameters of the subject and the first input is collected from the subject using a plurality of wearable devices and a plurality of sensors. The system is further configured to receive a second input associated with a digital record of the subject’s health information, via the one or more hardware processors, wherein the second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR). The system is further configured to generate a musculoskeletal (MSK) model of the subject using the first input and

the second input based on a musculoskeletal model generation technique, via the one or more hardware processors, wherein the MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region. The system is further configured to estimate a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs, via the one or more hardware processors, wherein the set of multi-body dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force. The system is further configured to generate an in-silico musculoskeletal lower limb of the subject’s lower limb, via the one or more hardware processors, wherein the in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model is generated by incorporating the set of multi-body dynamics in the MSK model. The system is further configured to identify an optimal motion configuration for the subject’s lower limb for a plurality of gaits, via the one or more hardware processors, using the in-silico musculoskeletal lower limb based on an optimal motion configuration technique.
[007] In another aspect, a method evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model is provided. The method includes receiving a first input from a subject, wherein the first input is associated with a plurality of physical parameters of the subject and the first input is collected from the subject using a plurality of wearable devices and a plurality of sensors. The method further includes receiving a second input associated with a digital record of the subject’s health information, wherein the second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR). The method further includes generating a musculoskeletal (MSK) model of the subject using the first input and the second input based on a musculoskeletal model generation technique, wherein the MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region. The method further includes estimating a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs, wherein the set of multi-body

dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force. The method further includes generating an in-silico musculoskeletal lower limb of the subject’s lower limb, wherein the in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model generated by incorporating the set of multi-body dynamics in the MSK model. The method further includes identifying an optimal motion configuration for the subject’s lower limb for a plurality of gaits, using the in-silico musculoskeletal lower limb based on an optimal motion configuration technique.
[008] In yet another aspect, a non-transitory computer readable medium evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model is provided. The program includes receiving a first input from a subject, wherein the first input is associated with a plurality of physical parameters of the subject and the first input is collected from the subject using a plurality of wearable devices and a plurality of sensors. The program further includes receiving a second input associated with a digital record of the subject’s health information, wherein the second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR). The program further includes generating a musculoskeletal (MSK) model of the subject using the first input and the second input based on a musculoskeletal model generation technique, wherein the MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region. The program further includes estimating a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs, wherein the set of multi-body dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force. The program further includes generating an in-silico musculoskeletal lower limb of the subject’s lower limb, wherein the in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model generated by incorporating the set of multi-body dynamics in the MSK model. The program further includes identifying an optimal motion configuration for the subject’s lower limb for a plurality of gaits, using the in-silico musculoskeletal lower limb based on an optimal motion configuration technique.

[009] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[010] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[011] FIG.1 illustrates an exemplary system for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model according to some embodiments of the present disclosure.
[012] FIG.2 is a functional block diagram of the system for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model according to some embodiments of the present disclosure.
[013] FIG.3A and FIG.3B is a flow diagram illustrating a method for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model in accordance with some embodiments of the present disclosure.
[014] FIG.4 illustrates an MSK model in accordance with some embodiments of the present disclosure.
[015] FIG.5 is a flow diagram illustrating a method for optimal motion configuration technique in accordance with some embodiments of the present disclosure.
[016] FIG.6 illustrates the estimation of tibiofemoral (TF) contact force across the stance phase for all the gait types using graphs for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model in accordance with some embodiments of the present disclosure.
[017] FIG.7 illustrates gait cycle in bar plot representation using graphs for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[018] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[019] Referring now to the drawings, and more particularly to FIG.1 through FIG.7, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[020] FIG.1 is a functional block diagram of a system 100 for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model in accordance with some embodiments of the present disclosure.
[021] In an embodiment, the system 100 includes a processor(s) 104, communication interface device(s), alternatively referred as input/output (I/O) interface(s) 106, and one or more data storage devices or a memory 102 operatively coupled to the processor(s) 104. The system 100 with one or more hardware processors is configured to execute functions of one or more functional blocks of the system 100.
[022] Referring to the components of system 100, in an embodiment, the processor(s) 104, can be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 can 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 one or more hardware processors 104 is configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100

can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, a network cloud and the like.
[023] The I/O interface(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, a touch user interface (TUI) and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface (s) 106 can include one or more ports for connecting a number of devices (nodes) of the system 100 to one another or to another server.
[024] The memory 102 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.
[025] Further, the memory 102 may include a database 108 configured to include information regarding several algorithms for generating the MSK models. Thus, the memory 102 may comprise information pertaining to input(s)/output(s) of each step performed by the processor(s) 104 of the system 100 and methods of the present disclosure. In an embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I/O interface 106.
[026] Functions of the components of system 100 are explained in conjunction with functional overview of the system 100 in FIG.2 and flow diagram of FIGS.3A and 3B for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model.
[027] The system 100 supports various connectivity options such as BLUETOOTH®, USB, ZigBee and other cellular services. The network environment enables connection of various components of the system 100 using any communication link including Internet, WAN, MAN, and so on. In an exemplary embodiment, the system 100 is implemented to operate as a stand-alone

device. In another embodiment, the system 100 may be implemented to work as a loosely coupled device to a smart computing environment. The components and functionalities of the system 100 are described further in detail.
[028] FIG.2 is a functional block diagram of the various modules of the system of FIG.1, in accordance with some embodiments of the present disclosure. As depicted in the architecture, the FIG.2 illustrates the functions of the components of a system 200 that includes evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model. The system 200 is an example of system 100 (FIG. 1).
[029] The system 200 for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model is configured for receiving a first input from a subject via a first input module 202, wherein the first input is associated with a plurality of physical parameters of the subject. The system 200 is further configured for receiving a second input associated with a digital record of the subject’s health information via a second input module 204. The system 200 further comprises a musculoskeletal (MSK) model generator 206 configured for generating a musculoskeletal (MSK) model of the subject using the first input and the second input based on a musculoskeletal model generation technique. The system 200 further comprises a multi-body dynamics estimator 208 configured for estimating a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs. The system 200 further comprises an in-silico MSK model module 210 configured for generating an in-silico musculoskeletal lower limb of the subject’s lower limb using the set of multi-body dynamics and the MSK model. The system 200 further comprises an optimal motion configurator 212 configured for identifying an optimal motion configuration for the subject’s lower limb for a plurality of gaits using the in-silico musculoskeletal lower limb. The system 200 further comprises an output module 214 for displaying the optimal motion configuration for the lower limb based on the in-silico musculoskeletal lower limb generated using the MSK model.
[030] The various modules of the system 100 for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model

are implemented as at least one of a logically self-contained part of a software program, a self-contained hardware component, and/or, a self-contained hardware component with a logically self-contained part of a software program embedded into each of the hardware component that when executed perform the above method described herein.
[031] Functions of the components of the system 200 are explained in conjunction with functional modules of the system 100 stored in the memory 102 and further explained in conjunction with flow diagram of FIG.3A and FIG.3B. The FIG.3A and FIG.3B with reference to FIG.1, is an exemplary flow diagram illustrating a method 300 for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model using the system 100 of FIG.1 according to an embodiment of the present disclosure.
[032] The steps of the method of the present disclosure will now be explained with reference to the components of the system (100) for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model and the modules (202-214) as depicted in FIG.2 and the flow diagrams as depicted in FIG.3A and FIG.3B. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[033] At step 302 of the method (300), the first input is received from a subject via a first input module 202. The first input is associated with a plurality of physical parameters of the subject. The first input is collected from the subject using a plurality of wearable devices and a plurality of sensors.
[034] In an embodiment, the first input comprises a set of parameters for knee joint of the subject, a set of parameters for hip of the subject, a set of parameters for the lower limb of the subject, a weight bearing at the subject’s knee joints, a muscle strength of the subject, and a muscular stress of the subject.

[035] In an example scenario, the set of parameters for knee joint includes a knee flexion-extension, an abduction adduction, a rotation range of motion and a joint load. The set of parameters for hip joint includes a hip flexion-extension, an abduction adduction-rotation range of motion and a set of corresponding joint loads. The lower limb parameters include a range of motion-loading at hip, a knee-ankle joint, a muscle strength parameter like muscle activation, velocity and muscle torque(force), muscular stress parameter like muscle pinnation angle muscle mass and muscle force
[036] In an embodiment, the plurality of wearable devices comprises simple activity monitoring devices that include a smartwatch, IMU, Step-activities monitoring device, a device to capture speed, upper body gestures and a heart-rate monitoring device, instrumented insoles to monitor gait parameters along with information related to weight bearing at the joints.
[037] In an embodiment, the plurality of sensors are no-intrusive wearables and are bound around the knee joint as patch sensors like wearable Strain-gauge, EMG, to estimate muscle strength and muscular stress.
[038] At the next step 304 of the method (300), a second input is received via the second input module 204. The second input is associated with a digital record of the subject’s health information. The second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR).
[039] In an embodiment, the second input provide exhaustive details of demography and general morbidity information of the subject’s health. The digital record of the subject’s health information is largely structured and hence a list of valuable parameters is identified for creation of several different types of data-model using analytic techniques that include neural networks and a Monte Carlo based technique. Further second input also comprises of information from surgical data related to implant size and alignment of the knee. The details regarding the implant size and alignment are collected at different stages including before surgery and after surgery.
[040] At step 306 of the method (300), a musculoskeletal (MSK) model of the subject is generated via the musculoskeletal model generator 206. The MSK

model is generated using the first input and the second input based on a musculoskeletal model generation technique. The MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region.
[041] In an embodiment, the musculoskeletal model generation technique includes techniques for generation of the MSK model. The musculoskeletal model generation technique comprises of several steps that includes scaling a skeleton structure around knee joint, specifically incorporating hip, knee, ankle and foot section from the second input. Further muscle actuators are selected and defined in the skeleton structure with wrapper section at specific anatomical landmarks. The muscle actuators are designed for all relevant muscle around knee and hip and some for ankle and foot region. Further basic ligament structure is also linked with knee joint around the patellar region. From the solid model of the implant, the tray of the implant is modeled as affixed ‘body’ in the knee frame as per anatomical landmarks. Further moment of inertia axes and frames are defined for hip, knee, ankle as well as the implant structure and an integrated geometry is created consisting coupled skeletal, muscle actuator and implant model to finally generate the MSK model as shown in FIG.4. In an embodiment, the FIG.4 illustrates a free-body diagram of the knee joint along with the knee implant, along with different force components acting at the joint due to muscle and proximal and distal joints.
[042] At step 308 of the method (300), a set of multi-body dynamics is estimated for the MSK model in the multi-body dynamics estimator 208. The set of multi-body dynamics is estimated based on a multi-body dynamics estimation technique using the first inputs. The set of multi-body dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force .
[043] In an embodiment, the multi-body dynamics estimation technique comprises an inverse kinematics technique to estimate the joint kinematics, an inverse dynamics technique to estimate joint moments, a static optimization technique to estimate a muscle force optimization, and a joint reaction analysis technique to estimate the contact force .

[044] In an embodiment, the joint kinematics is estimated based on the inverse kinematics technique. The joint kinematics is estimated using the first inputs. The first inputs are associated with a plurality of physical parameters of the subject and are collected from the subject using a plurality of wearable devices and a plurality of sensors. The joint kinematics is estimated using an optical marker position and a joint orientation, wherein the optical marker position and the joint orientation is expressed in 3D as x, y and z component. The joint kinematics or joint position (�) is expressed as shown below :
� = arccos{(�2 +�2+ �2- �21 - �22)/(2�1�2)} (1)
�ℎ���, Ө �� � ����� ����� (ℎ��, ���� �� �����),
�,�,� ��� 3� �����������, and
�1��� �2 ��� �ℎ� ���� �����ℎ� ������� �ℎ� ����� ����� �����������.
[045] Further a joint velocity and a joint acceleration are calculated by successive differentiation of joint position (�,�).
[046] In an embodiment, the joint moment is estimated based on the inverse dynamic technique. The joint moment includes a medial contact force and a lateral contact force and is estimated using the first inputs using a set of implants force transducer data and is expressed using a regression technique as shown below :
FM = C1FAM + C2FAM + C3FAM + C4FAM
FL = (1- 1)FAM + (1 - C2)FPM+ (1 - C3)FAM + (1 - C4)FPL (2)
wherein
FM is the medial contact force
FL is the lateral contact force C1, C2, C3 and C4 are regression ca - effecients A, M, P and L represent anterior co-effecients .
[047] In an embodiment, the muscle force optimization is estimated based on the static optimization technique. The muscle force optimization is estimated using the first inputs associated with a plurality of physical parameters of the subject. The first inputs used for estimating muscle force optimization includes a

mass matrix, a Coriolis component and a gravity component for an external force and is expressed as shown below:

where
τ is a j oi n t t o r q u e ,
C i s the mass matrix ,
C is the Coriolis component,
G is the gravity component and
F i s an ext e r n a l f o r c e .
[048] In an embodiment, the contact force is estimated using the joint reaction analysis technique. The contact force is estimated using the first inputs ( muscle force calculated using equation (2) at ith joint, wherein the joint loading calculated using equation (1) and a reaction force component of the distal joint in contact to ground). The contact force is expressed as shown below:

where,
R i s t h e c o n t a c t f o r c e
M i s t h e m ass of a segment o f the s ubject 's body
a i s a n a c c e l e r a t i o n,
F is a forc e , and
i s a n i m p a c t c o n t ac t f or ce from ground..
[049] The terms contact force and tibiofemoral contact force are used interchangeably in the document.
[050] At step 310 of the method (300), an in-silico musculoskeletal lower limb model of the subject’s lower limb is generated, via the in-silico MSK module 210. The in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model generated by incorporating the set of multi-body dynamics in the MSK model.
[051] In an embodiment, the in-silico musculoskeletal lower limb model is digital twin of lower limb. Hence the in-silico musculoskeletal lower limb model

is also referred to as a digital twin of the lower limb of the subject. The encompassing neuro-musculoskeletal modeling computing multibody dynamics to obtain optimal motion. In-silico model integrates musculoskeletal structure of relevant section from imaging modality and scales as per anthropometric requirement, followed by inverse kinematics, inverse dynamics and muscle force optimization to generate motion pattern specific to activity under consideration. The terms in-silico musculoskeletal lower limb model and the in-silico musculoskeletal lower limb are used inter-changeably in the disclosure.
[052] A plurality of motions is generated using the in-silico musculoskeletal lower limb . An optimal motion along with the configuration of the lower limb for the optimal motion is identified from the plurality of motions using the in-silico musculoskeletal lower limb. The optimal motion along with the configuration of the lower limb for the optimal motion is identified based on optimization of the set of multi-body dynamics of the subject depending upon specific activity for a plurality of gaits.
[053] At step 312 of the method (300), an optimal motion configuration is identified for the subject’s lower limb via the optimal motion configurator 212. The optimal motion configuration is identified for a plurality of gaits using the in-silico musculoskeletal lower limb based on an optimal motion configuration technique.
[054] A plurality of motions is generated using the in-silico musculoskeletal lower limb or the in-silico musculoskeletal lower limb model . Among the plurality of motions generated using the in-silico musculoskeletal lower limb, an optimal motion configuration is identified. The optimal motion configuration is an optimized motion of the lower limb to suit the requirement of the subject for a specific activity of the subject for a plurality of gaits. In an example scenario, the requirement of the subject is rehabilitation post a TKA, the activity is walking for a pole assisted gait for which the optimal motion configuration is identified by optimizing the set of multi-body dynamics of the in-silico musculoskeletal lower limb .
[055] The identification of the optimal motion configuration includes optimizing the set of multi-body dynamics for particular motion type in a gait. The

identification of the optimal motion configuration is performed using an optimal motion configuration technique is explained using the method 500 as illustrated in the FIG.5. The optimal motion configuration technique comprises of several steps as described below:
[056] At step 502 of the method (500), a plurality of motion configurations is simulated of the in-silico musculoskeletal lower limb for each gait among the plurality of gaits. The plurality of motion configurations is associated with a corresponding motion speed of the subject.
[057] In an embodiment, the plurality of gaits comprises of a normal gait, an assisted gait, a long normal pole assisted gait, a long wide pole assisted gait, a short normal pole assisted gait, and a short wide pole assisted gait. A motion configuration is simulated of the in-silico musculoskeletal lower limb for each gait from among the plurality of gaits, wherein a motion configuration is simulated of the in-silico musculoskeletal lower limb with a normal gait, a motion configuration is simulated of the in-silico musculoskeletal lower limb with an assisted gait, a motion configuration is simulated of the in-silico musculoskeletal lower limb with a long normal pole assisted gait, a motion configuration is simulated of the in-silico musculoskeletal lower limb with a short normal pole assisted gait and a motion configuration is simulated of the in-silico musculoskeletal lower limb with a short wide pole assisted gait. Further each of the motion configurations simulated are associated with a corresponding motion speed of the subject, wherein the motion speed is a speed associated with every motion of the subject for that corresponding gait.
[058] At step 504 of the method (500), an effect of variation in the motion speed of each motion configuration of each gait is simulated. The variation in the motion speed is simulated by varying the set of multi-body dynamics of the in-silico musculoskeletal lower limb .
[059] In an embodiment, motion speed is varied for each motion configuration of each gait of step 502 and the effect of variation in the motion speed is simulated.

[060] At step 506 of the method (500), a set of metrics is computed for the motion speed varied on each motion configuration of each gait. The set of metrics are associated with a load distribution factor on the subject’s knee, hip, and muscles.
[061] In an embodiment, the set of metrics comprises a tibiofemoral contact load metric, a knee adduction metric, a patellofemoral adduction metric, a metric for a force associated with a vastus medialis, a metric for a force associated with a vastus lateralis, and a metric for loading at a hip flexion, a hip adduction, and a hip rotation.
[062] Speed variation effect on all the above-mentioned metrics were studied in-silico, the first simulation results are tuned using sensor data, but for speed 2, the complete set of metrics evaluation are from simulated workflow. Hence the speed variation for many possible gaits outlines the importance of a modeling framework, wherein, the in-silico musculoskeletal lower limb (MSK model) can be simulated with different conditions for predictive analysis even in absence of the subject.
[063] At step 508 of the method (500), a score is computed for each of motion configuration of every gait based on the set of metrics.
[064] In an embodiment, the score for each of motion configuration is computed based on the set of metrics and is expressed as: Score (I) = ∑ni-1(EL)2 (5) Where,
EL is a effective loading index expressed as; EL = ( a* TF) + (b * KL) + (C * HL) (6) where ,
TF Tibiofemoral contact force from the tibiofemoral contact load metric, KL is a knee load from the knee adduction metric , HL is a knee load from the metric for loading at hip flexion, and a, b and c are p r e - d e t e r m i n e d w e i g h t s sp e c i f i c t o t h e s u b j e c t

[065] At step 510 of the method (500), an optimal motion configuration is identified from the plurality of motion configurations. The optimal motion configuration is identified based on the score, for each of the plurality of gaits.
[066] In an embodiment, upon computation of the score, based on a user’s requirement the optimal motion configuration is identified, wherein based on the user the top score or first two top scores or first three top scores are identified.
[067] The optimal motion configuration that is identified for the lower limb based on the in-silico musculoskeletal lower limb is displayed/shared on an output module 216.
[068] EXPERIMENTS:
[069] The disclosed technique can be used for identification of optimal motion for a plurality of gaits on different types of subjects – including healthy persons and persons with issues in the MSK systems. Several experiments have been conducted to generate in-silico musculoskeletal lower limb s of the lower limb. For experimentation purpose, a subject with knee adduction moment (KAM) forage related Osteoarthritis (OA) is considered. All potential markers is considered to account for computation and analysis of the response of tibiofemoral contact force, knee adduction moment, patellofemoral adduction moment, hip flexion, adduction and rotation moment along with muscle force distribution for Vastus Medialis (VM) and Vastus Lateralis (VL) muscles throughout the gait cycle (0 to 100%) and specific loading phases (0 to 100% of stance phases) for all the five gait combinations under study, for two set of walking speed variation: S1: subject’s self-selected speed of 1.3 m/sec and S2 is the simulated speed of 2m/sec.
[070] The total tibiofemoral contact force is measured in-vivo Eq.1. and the tibiofemoral contact force is estimated using Eq.(2), ground reaction force, all scaled by body-weight (BW) of the subject and report the absolute force values computed at different loading phases of gait cycle, naming initial contact (0%), loading (25%), mid-stance (50%) and terminal stance (75%).The FIG.6 illustrates the estimated tibiofemoral (TF) contact force across the stance phase for all the gait types. Estimated TF contact force matches the profile of in-vivo load measured (vector summation of medial and lateral load), with offshoots beyond terminal

stance phase. Table I lists the stride time and absolute force at the contact point at different gait events along with the error percentage with respect to in-vivo measurement as shown below:

Gait Stride time (sec) 0% stance Error (%) 25% Stance Error (%) 50% Stance Error (%) 50% Stance Error <%)
S1LN 1.485 136.6 13.3 381.4 6.4 1870.2 4.1 2380 12.1
S1LW 1.435 231.2 16.1 1837.2 6.2 1612.2 4 877.2 16.3
S1 NG 1.277 188.4 17.3 1334.5 5.9 1853.6 5.1 2584 14.7
S1 SN 1.301 204.1 3.6 2060.1 4.7 1887.6 3.1 1143.2 11.2
S1 SW 1.247 176.2 11.7 1339.6 4.1 1717.4 3.5 332.8 14.3

S1 LN 0.858 150.2 - 1035.6 - 1346.2 - 2573.1 -
S1 LW 0.863 242.1 - 1315.7 - 1710.6 - 1016.1 -
S1 NG 0.768 236.6 - 2205.8 - 2101.7 - 2803.2 -
S1 SN 0.773 255.4 - 2087.1 - 1367.2 - 1327.2 -
S1 SW 0.764 135.2 - 1500.4 - 1840.1 - 1005.8 -
Table 1 : Stride time and absolute force at the contact point at different gait events [071] The errors are calculated to validate the capability of MSK model to estimate TF contact force. For the simulated speed behavior (S2), only the estimated TF value is reported. As seen from the tabulations of Table.1, estimated TF shows coherence with the measured load mostly in the initial loading and mid stance phases and this is true for all the gait variations. This reflection is due to the MSK model characterization for kinetic and kinematic constraints along with external actuator adjustments. The overall profile for all gait variations are comparable to early publication comparing estimated load with measured in vivo loads in terms of the error band. In terms of load variation due to gait modification, LW pole had the best reduction with respect to normal gait. In case of LW pole shows maximum load reduction in mid stance and terminal stance phases, followed by SW. Increased speed simulation (S2) showed an increase in load at all the phases with respect to their S1 counterpart, however, rate of increase of load is minimal in long pole configuration (8 to 10% increase with respect to S1) while normal gait had load increased by up to 23% and short pole configurations had load increased by around 16% This indicates that increase in speed is positively correlated with increase in load but gait supported by long poles will aid better as the gait speed increases.

[072] Further loading at knee is evaluated and documented as Table.2, with respect to the surrogate markers, naming knee adduction and patellofemoral adduction load computed through inverse dynamics and corresponding muscle force of Vastus Lateralis (VL), and Vastus medialis (VM) muscles associated with supporting knee during lateral and medial load distribution respectively. Similar to TF estimation, LW pole configuration showed maximum reduction in loading for all parameters under consideration, however, reduction around midstance is maximum. Early stance reduction with pole walking is negligible, but as the stance phase progresses, effect of pole walking in reducing joint loads become more prominent. As the moment at joints gets reduced due to pole assistance, muscle force required to support the load is also reduced.


Table 2: Loading at knee
[073] The hip joint loading dynamics with change in motion speed across different adaptive gaits is documented in the Table.3. The effect of increased speed is most prominent in hip joint loading, where flexion, adduction and rotation load all get tremendously increased with increase in speed. The effect of pole walking is more prominent in S2, a wider pole configuration (both in long and short pole) provides reduction in hip loading with respect tos normal gait. To bring out the variation with speed, mean value of the joint loads over 0 to 100% of gait cycle are shown in bar plot representation as illustrated in FIG.7. Bar plot distribution for hip loading clearly shows the increase in load at hip joints with change in speed,

whereas speed variation over the gait cycle does not show much variation for knee joints. These variations point out the need of adjacent joint analysis while treating knee related ailments.

Gait -ftp Flexion (TO Hip] 3 Adduction (N) Hip Rotation (N)

0% 25% 50% 75% 0% 25% 50% 75% 0% 25% 50% 75%
S1 LN 7.72 9.89 2.33 10.46 2.2 4.91 2.33 1.8 0.18 0.27 0.27 1.05
S1 LW 18.2 7.81 2.71 9.26 2.69 4.64 2.94 1.6 1.01 0.31 0.18 0.35
S1 NG 23 8.13 4.28 15.8 3.19 5.17 3.35 1.8 0.95 0.02 0.38 0.53
S1 SN 22.2 6.16 4.37 11.85 1.14 4.16 1.32 1.3 0.45 0.51 0.45 0.31
S1 SW 20.4 7.72 5.18 10.5 1.92 4.81 1.2 1.5 1.51 0.79 0.3 0.31

S1 LN 44.19 15.16 18.57 19.11 1.73 1.47 1.17 14.5 0.18 1.19 1.29 1.04
S1 LW 51.13 14.69 5.34 5.15 1.89 7.06 2.94 16.6 1.89 0.9 1.21 0.91
S1 NG 88.14 17.29 2.81 33.7 14.6 8.23 3.52 17.1 .01 1.2 1.01 1.28 1.23
S1 SN 76.14 13.26 8.38 10.13 1.73 12.33 2.96 12.1 1.24 0.94 1.25 1.31
S1 SW 70.17 15.13 13.16 23.23 19.5 13.41 1.21 18.7 1.04 1.03 1.3 1.41

Table 3: Hip joint loading dynamics with change in motion speed across different adaptive gaits.
[074] Overall analysis indicates Long wide pole configuration to be most effective in reducing contact load as well as knee adduction load and hip loads and the effect is most prominent in the mid stance phase, where load bearing is maximum at the joint, followed by short wide pole. Effect is negligible at initial contact and beyond terminal stance phase (beyond 75% to 100% stance cycle). In general, it has been observed that wide pole configuration is more effective in reducing loading effect while a longer pole length helps in negotiating increase in speed. Walking with poles transfers a portion of ground reaction force through the arm supports which effectively offloads the knee joint and can be beneficial in minimizing further damage to the particular surface for OA subject. Effect of increased speed is much more profound in hip loading compared to knee joint, pointing the importance of analyzing adjacent joint dynamics during any pathophysiological evaluation of a particular joint. The results, though insightful has the drawback of drawing conclusion based on a single subject data. Although

reported metrics are averaged over 5 gait cycles, multiple user data would help to confirm the observed trends.
[075] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[076] The disclosure provides a method and system for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model. A Musculoskeletal (MSK) system provides multiple functionalities such as support- movement and to understand the MSK system better, modelling the MSK system is a popular approach. The existing MSK modelling techniques are too generic framework neglecting the personalization effect, while few state-of-the-art techniques that generate a personalized effect are extremely complicated, time consuming and computationally extensive. The disclosure is an improvised approach for musculoskeletal model that is personalized as an in-silico musculoskeletal lower limb of a subject to determine the best most optimal motion configuration correlated with several different types of gaits. The in-silico musculoskeletal lower limb is generated based on an MSK model incorporated with a set of the multi-body dynamics to enable personalization of each subject’s lower limb .
[077] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means

like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[078] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[079] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be

noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[080] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[081] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor-implemented method for evaluation and identification of optimal motion using in-silico musculoskeletal lower limb model (300) comprising:
receiving a first input from a subject, via one or more hardware processors, wherein the first input is associated with a plurality of physical parameters of the subject and the first input is collected from the subject using a plurality of wearable devices and a plurality of sensors (302);
receiving a second input associated with a digital record of the subject’s health information, via the one or more hardware processors, wherein the second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR) (304);
generating a musculoskeletal (MSK) model of the subject using the first input and the second input based on a musculoskeletal model generation technique, via the one or more hardware processors, wherein the MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region (306);
estimating a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs, via the one or more hardware processors, wherein the set of multi-body dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force (308);
generating an in-silico musculoskeletal lower limb of the subject’s lower limb, via the one or more hardware processors, wherein the in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model generated by incorporating the set of multi-body dynamics in the MSK model (310);and
identifying an optimal motion configuration for the subject’s lower limb for a plurality of gaits, via the one or more hardware processors, using

the in-silico musculoskeletal lower limb based on an optimal motion configuration technique (312).
2. The method of claim 1, wherein the first input comprises a set of parameters for knee joint of the subject, a set of parameters for hip of the subject, a set of parameters for the lower limb of the subject, a weight bearing at the subject’s knee joints, a muscle strength of the subject, and a muscular stress of the subject.
3. The method of claim 1, wherein the multi-body dynamics estimation technique comprises an inverse kinematics technique to estimate the joint kinematics, an inverse dynamics technique to estimate joint moments, a static optimization technique to estimate a muscle force optimization, and a joint reaction analysis technique to estimate the contact force .
4. The method of claim 1, wherein the optimal motion configuration is an optimized motion of the lower limb for a specific activity of the subject correlated with a plurality of gaits, wherein the subject is one of a healthy person, a patient with a knee-limb injury, a patient who has undergone a total knee arthroplasty (TKA), a patient undergoing a lower limb-therapy, a person who requires to a gait correction and the activity comprises walking, running, jogging, swimming.
5. The method of claim 1, wherein the plurality of gaits comprises of a normal gait, an assisted gait, a long normal pole assisted gait, a long wide pole assisted gait, a short normal pole assisted gait, and a short wide pole assisted gait.
6. The method of claim 1, wherein the optimal motion configuration technique (500) comprises:

simulating a plurality of motion configurations of the in-silico musculoskeletal lower limb for each gait among the plurality of gaits, wherein each of the plurality of motion configurations is associated with a corresponding motion speed of the subject (502);
simulating an effect of variation in the motion speed of each motion configuration of each gait, wherein the variation in the motion speed is simulated by varying the set of multi-body dynamics of the in-silico musculoskeletal lower limb (504);
computing a set of metrics for the motion speed varied on each motion configuration of each gait, wherein the set of metrics are associated with a load distribution factor on the subject’s knee, hip, and muscles (506);
computing a score for each of motion configuration of every gait based on the set of metrics (508); and
identifying an optimal motion configuration from the plurality of motion configurations, based on the score, for each of the plurality of gaits (510).
7. The method of claim 6, wherein the set of metrics comprises a tibiofemoral contact load metric, a knee adduction metric, a patellofemoral adduction metric, a metric for a force associated with a vastus medialis, a metric for a force associated with a vastus lateralis, and a metric for loading at a hip flexion, a hip adduction, and a hip rotation.
8. The method of claim 6, wherein the score for each of motion configuration is computed based on the set of metrics and is expressed as:
Score (l) = ∑ni-1(EL)2 Where
EL is a ef f ective loading index expressed as ; EL = ( a* TF) + (b * KL) + (C * HL) where

TF is a Tibiofemoral contact force from the tibiofemoral contact load metric, KL is a knee load from the knee adduction metric , HL is a hip load from the metric for loading at hip flexion, and a ,b and c is pre
- det e r m i n e d w e i g h t s s p e c i f i c to t h e s u b j e c t
9. A system (100), comprising:
an input/output interface (106);
one or more memories (102); and
one or more hardware processors (104), the one or more memories (102) coupled to the one or more hardware processors (104), wherein the one or more hardware processors (104) are configured to execute programmed instructions stored in the one or more memories (102), to:
receive a first input from a subject, via one or more hardware processors, wherein the first input is associated with a plurality of physical parameters of the subject and the first input is collected from the subject using a plurality of wearable devices and a plurality of sensors;
receive a second input associated with a digital record of the subject’s health information, via the one or more hardware processors, wherein the second input comprises a radiology report, and an Electronic Health Record/Electronic Medical Record (EHR/EMR);
generate a musculoskeletal (MSK) model of the subject using the first input and the second input based on a musculoskeletal model generation technique, via the one or more hardware processors, wherein the MSK model is a digital bio-mechanical model of the subject’s lower limb, where the subject’s lower limb comprises a hip, a knee, an ankle, and a foot region;
estimate a set of multi-body dynamics for the MSK model based on a multi-body dynamics estimation technique using the first inputs, via the one or more hardware processors, wherein the set of multi-body dynamics comprises a joint kinematics, a joint moment, a muscle force optimization, a contact force;

generate an in-silico musculoskeletal lower limb of the subject’s lower limb, via the one or more hardware processors, wherein the in-silico musculoskeletal lower limb is a neuro-musculoskeletal (MSK) model is generated by incorporating the set of multi-body dynamics in the MSK model; and
identify an optimal motion configuration for the subject’s lower limb for a plurality of gaits, via the one or more hardware processors, using the in-silico musculoskeletal lower limb based on an optimal motion configuration technique.
10. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to perform the optimal motion configuration technique comprises:
simulating a plurality of motion configurations of the in-silico musculoskeletal lower limb for each gait among the plurality of gaits, wherein each of the plurality of motion configurations is associated with a corresponding motion speed of the subject;
simulating an effect of variation in the motion speed of each motion configuration of each gait, wherein the variation in the motion speed is simulated by varying the set of multi-body dynamics of the in-silico musculoskeletal lower limb ;
computing a set of metrics for the motion speed varied on each motion configuration of each gait, wherein the set of metrics are associated with a load distribution factor on the subject’s knee, hip, and muscles;
computing a score for each of motion configuration of every gait based on the set of metrics; and
identifying an optimal motion configuration from the plurality of motion configurations, based on the score, for each of the plurality of gaits.

11. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to estimate the multi-body dynamics based on the multi-body dynamics estimation technique that comprises an inverse kinematics technique to estimate the joint kinematics, an inverse dynamics technique to estimate joint moments, a static optimization technique to estimate a muscle force optimization, and a joint reaction analysis technique to estimate the contact force .
12. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to compute the set of metrics that comprises a tibiofemoral contact load metric, a knee adduction metric, a patellofemoral adduction metric, a metric for a force associated with a vastus medialis, a metric for a force associated with a vastus lateralis, and a metric for loading at a hip flexion, a hip adduction, and a hip rotation.
13. The system of claim 9, wherein the one or more hardware processors are configured by the instructions to perform the optimal motion configuration technique comprises:
simulating a plurality of motion configurations of the in-silico musculoskeletal lower limb for each gait among the plurality of gaits, wherein each of the plurality of motion configurations is associated with a corresponding motion speed of the subject;
simulating an effect of variation in the motion speed of each motion configuration of each gait, wherein the variation in the motion speed is simulated by varying the set of multi-body dynamics of the in-silico musculoskeletal lower limb;
computing a set of metrics for the motion speed varied on each motion configuration of each gait, wherein the set of metrics are associated with a load distribution factor on the subject’s knee, hip, and muscles;

computing a score for each of motion configuration of every gait based on the set of metrics; and
identifying an optimal motion configuration from the plurality of motion configurations, based on the score, for each of the plurality of gaits.
14. The system of claim 13, wherein the one or more hardware processors are configured by the instructions to compute the score for each of motion configuration based on the set of metrics and is expressed as: Score (l ) = ∑ni-1(EL)2 Where EL is a effective loading index expressed as;
EL = ( a * TF) + (b * KL ) + (c * HL) where
TF is a Tibiofemoral contact force from the tibiofemoral contact load metric, KL is a knee load from the knee adduction metric , HL is a hip load from the metric for loading at hip flexion, and a,b and c is pre
- determined weighis specific to the subject