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 PERSONALIZED AND OPTIMAL
SELECTION OF ANKLE FOOT ORTHOSIS
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
The following specification particularly describes the invention and the manner in
which it is to be performed.
2
TECHNICAL FIELD
[001] The disclosure herein generally relates to an Ankle Foot Orthosis
(AFO), and, more particularly, a method and system for personalized and optimal
selection of AFO.
5
BACKGROUND
[002] Crouch gait is a pathological movement involving abnormal kinematics
and muscle activation leading to inefficient and high energetic cost of walking, and
commonly seen in children suffering from Cerebral Palsy (CP). The crouch gait is
10 characterized by excessive flexion of hip, knee, and ankle during a stance phase of a
gait. The cause and manifestation of the crouch gait are often multifactorial, arising
because of muscle spasticity, impaired selective motor control, and contracture leading
to weakness in ankle planter flexor and knee extensor muscles. The crouch gait usually
progresses with age and growth, making treatment and disease management
15 challenging. Treatment for the crouch gait involves selective muscle strengthening,
physical therapy, spasticity reduction, use of an Ankle Foot Orthoses (AFOs), and
surgical intervention to enable an efficient walking pattern.
[003] Out of conventional treatment approaches to improve the crouch gait,
use of passive AFOs remains one of most common interventions. The passive AFOs
20 are prescribed to patients having weak plantar flexor or dorsiflexor muscles due to
disorders like stroke, CP, spinal cord injury, and thereof. In CP children, passive AFOs
are known to assist ankle dynamics and improve gait kinematics that prevent bone
deformity and reduce the energy cost of walking. The passive AFOs provides ankle
stabilization by allowing heel contact with the ground during the stance phase to
25 maintain a stable posture and improves gait by preventing foot drop in the stance phase.
The passive AFOs that resist ankle dorsiflexion are the most prescribed orthoses for
children with the CP. Generation of a torque in the passive AFO is dependent on ankle
kinematics and properties of the passive AFOs like stiffness and equilibrium angle.
3
Judicious designing of the passive AFOs with optimal stiffness can potentially reduce
the energetic cost of walking during the gait by modulating passive AFOs storage and
release of mechanical energy in a gait cycle.
[004] There are multiple variants of the passive AFOs comprising a solid
AFO, a dynamic AFO, a hinged AFO, and thereof. 5 The solid AFO counteract excessive
knee flexion during the stance phase of gait and are known to normalize knee
kinematics and kinetics effectively. However, the solid AFO is generally ineffective in
reducing push-off power. A spring-like AFO, like leaf spring may enhance the pushoff
power but with limited reduction in knee flexion. Optimizing the trade-off between
10 push-off power and flexion angle maximizes efficiency of the gait. Further the response
of the subject in terms of the crouch gait improvement depends on selecting a correct
AFO as well as optimal AFO setting. However, this is challenging as it is difficult to
predict how a specific design of the AFO will impact muscle action and reduce the
energy cost of walking for individual subjects.
15
SUMMARY
[005] 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,
20 a method for personalized and optimal selection of AFO is provided. The method
includes receiving a motion-captured crouch gait data, a height, a body weight, and a
severity of a crouch gait pertaining to a Cerebral Palsy (CP) subject. The method further
includes computing a joint ankle angle kinematics comprising a plurality of joint ankle
angles, from the motion-captured crouch gait data, at a plurality of three-dimensional
25 (3D) ankle joint locations of the CP subject using an inverse kinematics pipeline. The
method further includes feeding the plurality joint ankle angles, the height, and the
body weight of the CP subject, to a plurality of Ankle Foot Orthosis (AFO) controllers,
wherein each of the plurality of AFO controllers are programmed with associated
4
mechanical behavior. The method further includes computing a corresponding AFO
torque, by each of the plurality of AFO controllers in accordance with associated
mechanical behavior. The method further includes integrating the generated AFO
torque, and the plurality joint ankle angles of each of the plurality of AFO controllers
with a musculoskeletal human 5 lower limb model (MHLLM) comprising a human
skeleton and a plurality of lower limb muscles, to generate an associated AFO
integrated MHLLM, corresponding to each of the plurality of AFO controllers. The
method further includes performing an inverse dynamics mechanism on the associated
AFO integrated MHLLM, to compute an additional joint torque at an ankle joint of the
10 human skeleton, corresponding to each of the plurality of AFO controllers. The method
further includes computing a plurality of muscle forces corresponding to the plurality
of lower limb muscles of the associated AFO integrated MHLLM, for a gait cycle of
the CP subject, using a static optimization framework, corresponding to each of the
plurality of AFO controllers. The method further includes computing a plurality of
15 muscle response metrics comprising a muscle impulse, a muscle yank, a muscle coactivation,
and an energetic cost of walking, from the plurality of muscle forces and
the additional joint torque, corresponding to each of the plurality of AFO controllers.
The method further includes combining the plurality of muscle response metrics, to
generate an AFO selector score, corresponding to each of the plurality of AFO
20 controllers. The method further includes ranking the plurality of AFO controllers in
increasing order based on the AFO selector scores. The method further includes
selecting a top ranked AFO controller as a personalized optimal AFO controller from
among the plurality of AFO controllers for the CP subject.
[006] In another aspect, a system for personalized and optimal selection of
25 AFO is provided. The system comprising: 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 motion-captured
5
crouch gait data, a height, a body weight, and a severity of a crouch gait pertaining to
a Cerebral Palsy (CP) subject; computing a joint ankle angle kinematics comprising a
plurality of joint ankle angles, from the motion-captured crouch gait data, at a plurality
of three-dimensional (3D) ankle joint locations of the CP subject using an inverse
kinematics pipeline; feed the plurality joint ankle angles, 5 the height, and the body
weight of the CP subject, to a plurality of Ankle Foot Orthosis (AFO) controllers,
wherein each of the plurality of AFO controllers are programmed with associated
mechanical behavior; compute a corresponding AFO torque, by each of the plurality
of AFO controllers in accordance with associated mechanical behavior; integrate the
10 generated AFO torque, and the plurality joint ankle angles of each of the plurality of
AFO controllers with a musculoskeletal human lower limb model (MHLLM)
comprising a human skeleton and a plurality of lower limb muscles, to generate an
associated AFO integrated MHLLM, corresponding to each of the plurality of AFO
controllers; perform an inverse dynamics mechanism on the associated AFO integrated
15 MHLLM, to compute an additional joint torque at an ankle joint of the human skeleton,
corresponding to each of the plurality of AFO controllers; compute a plurality of
muscle forces corresponding to the plurality of lower limb muscles of the associated
AFO integrated MHLLM, for a gait cycle of the CP subject, using a static optimization
framework, corresponding to each of the plurality of AFO controllers; compute a
20 plurality of muscle response metrics comprising a muscle impulse, a muscle yank, a
muscle co-activation, and an energetic cost of walking, from the plurality of muscle
forces and the additional joint torque, corresponding to each of the plurality of AFO
controllers; combine the plurality of muscle response metrics, to generate an AFO
selector score, corresponding to each of the plurality of AFO controllers; rank the
25 plurality of AFO controllers in increasing order based on the AFO selector scores; and
select a top ranked AFO controller as a personalized optimal AFO controller from
among the plurality of AFO controllers for the CP subject based on the ranking.
6
[007] In yet another aspect, there are provided one or more non-transitory
machine-readable information storage mediums comprising one or more instructions,
which when executed by one or more hardware processors causes a method for
personalized and optimal selection of AFO is provided. The method includes receiving
a motion-captured crouch gait data, a height, a body 5 weight, and a severity of a crouch
gait pertaining to a Cerebral Palsy (CP) subject. The method further includes
computing a joint ankle angle kinematics comprising a plurality of joint ankle angles,
from the motion-captured crouch gait data, at a plurality of three-dimensional (3D)
ankle joint locations of the CP subject using an inverse kinematics pipeline. The
10 method further includes feeding the plurality joint ankle angles, the height, and the
body weight of the CP subject, to a plurality of Ankle Foot Orthosis (AFO) controllers,
wherein each of the plurality of AFO controllers are programmed with associated
mechanical behavior. The method further includes computing a corresponding AFO
torque, by each of the plurality of AFO controllers in accordance with associated
15 mechanical behavior. The method further includes integrating the generated AFO
torque, and the plurality joint ankle angles of each of the plurality of AFO controllers
with a musculoskeletal human lower limb model (MHLLM) comprising a human
skeleton and a plurality of lower limb muscles, to generate an associated AFO
integrated MHLLM, corresponding to each of the plurality of AFO controllers. The
20 method further includes performing an inverse dynamics mechanism on the associated
AFO integrated MHLLM, to compute an additional joint torque at an ankle joint of the
human skeleton, corresponding to each of the plurality of AFO controllers. The method
further includes computing a plurality of muscle forces corresponding to the plurality
of lower limb muscles of the associated AFO integrated MHLLM, for a gait cycle of
25 the CP subject, using a static optimization framework, corresponding to each of the
plurality of AFO controllers. The method further includes computing a plurality of
muscle response metrics comprising a muscle impulse, a muscle yank, a muscle coactivation,
and an energetic cost of walking, from the plurality of muscle forces and
7
the additional joint torque, corresponding to each of the plurality of AFO controllers.
The method further includes combining the plurality of muscle response metrics, to
generate an AFO selector score, corresponding to each of the plurality of AFO
controllers. The method further includes ranking the plurality of AFO controllers in
increasing order based on 5 the AFO selector scores. The method further includes
selecting a top ranked AFO controller as a personalized optimal AFO controller from
among the plurality of AFO controllers for the CP subject.
[008] It is to be understood that both the foregoing general description and the
following detailed description are exemplary and explanatory only and are not
10 restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[009] The accompanying drawings, which are incorporated in and constitute
a part of this disclosure, illustrate exemplary embodiments and, together with the
15 description, serve to explain the disclosed principles:
[010] FIG. 1 illustrates an exemplary system for a personalized and optimal
selection of Ankle Foot Orthosis (AFO), in accordance with some embodiments of the
present disclosure.
[011] FIG. 2 is a functional architecture depicting process flow of the system
20 for the personalized and the optimal selection of the AFO, in accordance with some
embodiments of the present disclosure.
[012] FIGS. 3A, and 3B depict a flow diagram of a method for the personalized
and the optimal selection of the AFO, using the system of FIG. 1, in accordance with
some embodiments of the present disclosure.
25 [013] FIGS. 4A, 4B, and 4C depict a plurality of muscle forces across a
severity of a crouch gait for an assisted walking and an unassisted walking, in
accordance with some embodiments of the present disclosure.
8
[014] FIG. 5 depicts a muscle impulse calculated for a plurality of muscles
across the severity of the crouch gait, in accordance with some embodiments of the
present disclosure.
[015] FIG. 6 depicts a muscle yank calculated for the plurality of muscles
across the severity of the crouch gait, in accordance 5 with some embodiments of the
present disclosure.
[016] FIG. 7 depicts a muscle co-activation calculated for the plurality of
muscles across the severity of crouch gait, in accordance with some embodiments of
the present disclosure.
10 [017] FIG. 8 depicts an energy cost of walking calculated for the plurality of
muscles across the severity of crouch gait, in accordance with some embodiments of
the present disclosure.
[018] It should be appreciated by those skilled in the art that any block
diagrams herein represent conceptual views of illustrative systems and devices
15 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.
20 DETAILED DESCRIPTION OF EMBODIMENTS
[019] 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
25 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.
9
[020] An Ankle Foot Orthosis (AFO) is commonly prescribed for correcting
a crouch gait in children with cerebral palsy (CP). There are multiple AFO variants,
however selecting an optimal AFO for a CP subject is often challenging.
[021] Embodiments herein provide a method and system for personalized and
optimal selection of the AFO. The AFO is also referred 5 to as an AFO controller, in
accordance with some embodiments of the present disclosure. The disclosed method
focusses on selecting the personalized and the optimal selection of an AFO controller
using an AFO torque, and a plurality joint ankle angles of each of a plurality of AFO
controllers integrated with a musculoskeletal human lower limb model (MHLLM). A
10 plurality of muscle forces is computed using the MHLLM for each of the plurality of
AFO controllers. Further the method computes a plurality of muscle response metrics
comprising a muscle impulse, a muscle yank, a muscle co-activation, and an energetic
cost of walking, from the plurality of muscle forces and an additional joint torque for
each of the AFO controllers. The plurality of muscle response metrics is combined to
15 obtain a personalized optimal AFO controller among the plurality of AFO controllers
of the CP subject.
[022] Referring now to the drawings, and more particularly to FIG. 1 through
FIG. 8, where similar reference characters denote corresponding features consistently
throughout the figures, there are shown preferred embodiments, and these
20 embodiments are described in the context of the following exemplary system and/or
method.
[023] FIG. 1 is a functional block diagram of a system 100 for the personalized
and the optimal selection of the AFO, in accordance with some embodiments of the
present disclosure. In an embodiment, the system 100 includes one or more hardware
25 processors 104, communication interface device(s) or input/output (I/O) interface(s)
106 (also referred as interface(s)), and one or more data storage devices or memory
102 operatively coupled to the one or more hardware processors 104. The one or more
10
processors 104 may be one or more software processing components and/or hardware
processors.
[024] Referring to the components of the system 100, in an embodiment, the
processor (s) 104 can be the one or more hardware processors 104. In an embodiment,
the one or more hardware processors 5 104 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 processor(s)
104 is/are configured to fetch and execute computer-readable instructions stored in the
10 memory. In an embodiment, the system 100 can be implemented in a variety of
computing systems, such as laptop computers, notebooks, hand-held devices (e.g.,
smartphones, tablet phones, mobile communication devices, and the like),
workstations, mainframe computers, servers, a network cloud, and the like.
[025] The I/O interface(s) 106 can include a variety of software and hardware
15 interfaces, for example, a web interface, a graphical user interface, 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 to one another
20 or to another server.
[026] 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
25 memories, hard disks, optical disks, and magnetic tapes. 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, a database 108 is comprised in the memory 102, wherein the database
11
108 comprises information on a motion-captured crouch gait data, a height, a body
weight, and a severity of the crouch gait. The memory 102 further comprises a plurality
of modules (not shown) for various technique(s) such as inverse kinematics pipeline,
the MHLLM, a static optimization framework and thereof. The above-mentioned
technique(s) are implemented as at least one of a 5 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 (e.g., hardware processor 104 or
memory 102) that when executed perform the method described herein.
10 [027] The memory 102 further comprises (or may further comprise)
information pertaining to input(s)/output(s) of each step performed by the systems and
methods of the present disclosure. In other words, input(s) fed at each step and
output(s) generated at each step are comprised in the memory 102 and can be utilized
in further processing and analysis.
15 [028] FIG. 2 is a functional architecture depicting process flow of the system
100 for the personalized and the optimal selection of the AFO, in accordance with some
embodiments of the present disclosure. The system 100 in FIG. 2 is configured to
receive the motion-captured crouch gait data, the height, and the body weight
pertaining to the CP subject and is fed to the plurality of AFO controllers. The plurality
20 of AFO controllers refers to various AFO devices available in a market. The AFO
controllers block is configured to compute a joint ankle angle kinematics comprising a
plurality of joint ankle angles, and the AFO torque for each of the AFO controllers.
The AFO torque is a force exerted by rotational movements. The plurality of joint ankle
angles and the AFO torque of each of the plurality of AFO controllers are integrated
25 with the MHLLM to generate an integrated MHLLM for each of the plurality of AFO
controllers. A joint loading block is configured to perform inverse dynamics on the
associated AFO integrated MHLLM to compute the additional joint torque at the
plurality of joint angles corresponding to each of the plurality of AFO controllers. A
12
muscle loading block is configured to compute the plurality of muscle forces
corresponding to a plurality of lower limb muscles of the AFO integrated MHLLM, for
a gait cycle of the CP subject, using the static optimization framework, corresponding
to each of the plurality of AFO controllers. One gait cycle is defined as a heel strike to
next the heel strike of a same leg. A muscle 5 response block is configured to the plurality
of muscle response metrics comprising the muscle impulse, the muscle yank, and the
muscle co-activation, and the energetic cost of walking, from the plurality of muscle
forces and the additional joint torque, corresponding to each of the plurality of AFO
controllers. An AFO selector scorer block is configured to combine the plurality of
10 muscle response metrics, to generate an AFO selector score, corresponding to each of
the plurality of AFO controllers. A subject specific AFO block is configured to select
the personalized optimal AFO controller from among the plurality of AFO controllers
for the CP subject based on the ranking.
[029] FIGS. 3A, and 3B depict a flow diagram of a method 300 for the
15 personalized and the optimal selection of the AFO, using the system of FIG. 1, in
accordance with some embodiments of the present disclosure.
[030] In an embodiment, the system 100 comprises one or more data storage
devices or the memory 102 operatively coupled to the processor(s) 104 and is
configured to store instructions for execution of steps of the method 300 by the
20 processor(s) 104. The steps of the method 300 of the present disclosure will now be
explained with reference to the components or blocks of the system 100 as depicted in
FIG. 1, the functional architecture depicted in FIG. 2, and the steps of flow diagram as
depicted in FIGS. 3A, and 3B. Although process steps, method steps, techniques or the
like may be described in a sequential order, such processes, methods and techniques
25 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
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.
13
[031] Referring to steps of FIG. 3A, at step 302 of the method 300, the one or
more hardware processors are configured to receive the motion-captured crouch gait
data, the height, the body weight, and the severity of the crouch gait pertaining to the
CP subject. The motion-captured crouch gait data is used from a repository, generated
from motion analysis data. The motion-captured 5 crouch gait comprises kinematics and
ground reaction force data for the CP subject. The severity of the crouch gait is based
on knee flexion angle (KFA) during a stance phase of the gait. The severity of the
crouch gait comprises one of (i) a normal gait, (ii) a mild crouch gait, (iii) a moderate
crouch gait, and (iv) a severe crouch gait.
10 [032] At step 304 of the method 300, the one or more hardware processors
are configured to compute the joint ankle angle kinematics comprising the plurality of
joint ankle angles, from the motion-captured crouch gait data, at a plurality of threedimensional
(3D) ankle joint locations of the CP subject using inverse kinematics
pipeline. The inverse kinematics pipeline takes the plurality of 3D ankle joint locations
15 and returns the joint ankle angle kinematics comprising the plurality of joint ankle
angles. This is computed by a weighted least square optimization that minimizes error
of optical markers used to record the plurality of 3D ankle joint locations. An inverse
dynamics tool of the inverse kinematics pipeline goes through each time step frame of
motion and computes generalized coordinate values which positions the MHLLM in
20 a pose that best matches experimental marker and the coordinate values for that time
step. Mathematically, the best match is expressed as a weighted least squares problem,
whose solution aims to minimize both the marker and errors of the coordinate values.
[033] At step 306 of the method 300, the one or more hardware processors
are configured to feed the plurality joint ankle angles, the height, and the body weight
25 of the CP subject, to the plurality of AFO controllers, wherein each of the plurality of
AFO controllers are programmed with associated mechanical behavior. The
mechanical behavior of the AFO controllers refers to a torque generation principle of
the AFO controller and is defined by combination of an optimal stiffness value and an
14
AFO equilibrium angle. Therefore, the mechanical behavior of each of the plurality of
AFO controllers is composed as combination of the optimal stiffness value, and the
AFO equilibrium angle, for generating the corresponding AFO torque.
[034] At step 308 of the method 300, the one or more hardware processors are
configured to compute the corresponding AFO torque, 5 by each of the plurality of AFO
controllers in accordance with the associated mechanical behavior. The AFO torque
generated by each of the plurality of AFO controllers are modelled and guide the joint
ankle kinematics of the CP subject along with the optimal stiffness value (𝐾) and the
AFO equilibrium angle (𝜃𝑒𝑞). The AFO equilibrium angle is an angle between a foot
10 plate of the AFO controller and a shank plate at which the AFO controller starts
generating the AFO torque. The plurality of AFO controllers comprises a Ground
Reaction AFO (GRAFO), and Leaf Spring AFO (LSAFO), in accordance with some
embodiments of the present disclosure. The AFO torque generated by the GRAFO is
given by:
𝜏𝐺𝑅𝐴𝐹𝑂 = {
𝑘(𝜃𝑎𝑛𝑘−𝜃𝑒𝑞) 𝑖𝑓 𝜃𝑎𝑛𝑘 > 𝜃𝑒𝑞
0 𝑜𝑡ℎ𝑒𝑟 𝑤𝑖𝑠𝑒
15 (1)
[035] where 𝑘 is the optimal stiffness value, 𝜃𝑒𝑞is the AFO equilibrium angle,
and 𝜃𝑎𝑛𝑘 is the plurality of joint ankle angles. The AFO torque is generated during
dorsiflexion only. In a planter flexion, the j the plurality of joint ankle angles is kept
fixed. The optimal value of stiffness is used as 𝑘 = 3.66𝑁/𝑚, and the equilibrium
20 angle 𝜃𝑒𝑞 = 6𝑑𝑒𝑔).
[036] The AFO torque generated by the LSAFO has two components, a
planter flexion (PF) torque, and a dorsiflexion (DF) torque.
𝜏𝐿𝑆𝐴𝐹𝑂 = {
𝑘𝑠((𝑈𝐿−𝜃𝑒𝑞) − 𝜃𝑎𝑛𝑘) + 𝑘𝑑
𝑤𝑑
𝑖𝑓 𝑃𝐹
𝑘𝑠 ((𝐿𝐿+𝜃𝑒𝑞) − 𝜃𝑎𝑛𝑘) +
𝑘𝑑
𝑤𝑑
𝑖𝑓 𝐷𝐹
(2)
[037] The term
𝑘𝑑
𝑤𝑑
represents a damping AFO torque, 𝑘𝑑 = 0.2N/m is a
25 damping coefficient, 𝑤𝑑 is a joint velocity. Upper Limit (UL) is the joint ankle angle
15
of the plurality of joint ankle angles above which the LSAFO generates the PF torque,
Lower Limit (LL) is the joint ankle angle of the plurality of joint ankle angles above
which the LSAFO generates the DF torque. The UL and the LL are tunable parameters
that are adjusted from the CP subject specific joint ankle angle of the plurality of joint
ankle angles during the gait. The optimal stiffness 5 value and the equilibrium angle used
are: 𝑘𝑠 = 2.7N/m, 𝜃𝑒𝑞 = 4deg.
[038] At step 310 of the method 300, the one or more hardware processors
are configured to integrate the generated AFO torque, and the plurality joint ankle
angles of each of the plurality of AFO controllers with the MHLLM comprising a
10 human skeleton and the plurality of lower limb muscles, to generate the associated
AFO integrated MHLLM, corresponding to each of the plurality of AFO controllers.
The MHLLM is designed based on the severity of the crouch gait.
[039] At step 312 of the method 300, the one or more hardware processors are
configured to perform an inverse dynamics mechanism on the associated AFO
15 integrated MHLLM, to compute the additional joint torque at the plurality of joint
ankle angles of the human skeleton, corresponding to each of the plurality of AFO
controllers. The additional joint torque is computed based on the AFO equilibrium
angle, the optimal stiffness value, and the plurality of joint ankle angles of the CP
subject.
20 [040] The additional joint torque for each of the plurality of AFO controllers
are computed as:
𝜏 = 𝑀(𝑞)𝑞̈ + 𝐶(𝑞, 𝑞̇) + 𝐺(𝑞) + 𝐹
where, 𝑞̈, 𝑞̇, and q, represents an acceleration, a velocity, and a position due to
the AFO torque τ,
25 M is a mass matrix,
C and G are a Coriolis component and gravity component, and
F is any external force applied to the MHLLM.
16
[041] At step 314 of the method 300, the one or more hardware processors
are configured to compute the plurality of muscle forces corresponding to the plurality
of lower limb muscles of the associated AFO integrated MHLLM, for the gait cycle of
the subject CP subject, using the static optimization framework, corresponding to each
of the plurality of AFO controllers. The static 5 optimization framework uses the known
motion of the MHLLM to solve the equations of motion for the unknown generalized
force of joint torques at respective joints like hip, knee, joint ankle angles, and thereof.
[042] The plurality of muscle forces generated in the AFO integrated
MHLLM are based on a Thelen muscle actuator, where the plurality of muscle forces
10 or a AFO torque component(𝜏𝑚 ) is calculated as 𝜏𝑚 = [𝑅(𝑞)] 𝑓 (𝑎, 𝑙, 𝑖). [𝑅(𝑞)] is a
moment arm, 𝑎 is an activation value, and 𝑙 is a normalized length of muscle unit. The
plurality of muscle forces over one gait cycle is estimated using the static optimization
framework. The plurality of muscle forces is estimated by minimizing sum of squared
muscle activations, generated using a Thelen muscle model that is required to drive
15 experimentally captured kinematics and ground reaction force at each time instance.
[043] At step 316 of the method 300, the one or more hardware processors are
configured to compute the plurality of muscle response metrics comprising the muscle
impulse, the muscle yank, the muscle co-activation, and the energetic cost of walking,
from the plurality of muscle forces and the additional joint torque, corresponding to
20 each of the plurality of AFO controllers. A plurality of muscles considered for
computing the plurality of muscle response metrics comprises Gluteus muscle (GM)
(combination of medius, maximus and minimus), Illiopsas (ILL), Rectus Femoris (RF),
Vastus group (VAS), Hamstring group (HAM) comprising bicep femoris,
semimembranosus and semitendinosus muscles, Soleus (SOL), Tibialis anterior (TA)
25 and Gastrocnemius (GAS).
[044] The muscle impulse is computed as a sum of the plurality muscle forces
for each of the plurality of muscles integrated over the stance phase of the gait cycle,
and is expressed as:
17
𝑚𝑢𝑠𝑐𝑙𝑒 𝑖𝑚𝑝𝑢𝑙𝑠𝑒 =
1
𝐵𝑊
Σ 𝐹 𝑑𝑡
[045] Where BW is the body weight, F is the muscle force, and t is a duration
of the stance phase of the gait cycle. This is equivalent to a mechanical work rate of
the plurality of muscles and is a major component of metabolic cost.
[046] The muscle yank, also referred to as 5 Rate of force development (RFD)
is an important biomechanics metric that correlates with different responses linked with
sensori-motor system. The muscle yank is a derivative of force with respect to time
that can be used in measurement of time variation of propulsive force during
movements ranging from locomotion to responses of sensory organs that are used in
10 motor reflexes. Athletic performance and recovery during rehabilitation are shown to
be closely related to improvement in the RFD. The RFD or the muscle yank parameter
evaluation for the plurality of muscles provides intuitive information about efficiency
in selection of the AFO. The muscle yank is expressed as: 𝑦𝑎𝑛𝑘 =
𝜕𝑓
𝜕𝑡
, where 𝐹 is the
plurality of muscle forces, t is the duration of the stance phase of the gait cycle.
15 [047] The muscle co-activation is a ratio of agonist-antagonist muscle pair,
which is an important metric in movement mechanics and dictates joint stability,
stiffness, rate of movement, and thereof. The muscle co-activation is evaluated around
the plurality of joint ankle angles, considering Soleus and Tibialis anterior muscles as
the agonist-antagonist muscle pair to compare changes in the muscle co-activation.
20 [048] A key metric in for evaluating the performance of the plurality of AFO
controllers is the energetic cost of walking. Metabolic cost calculation is
conventionally measured using indirect calorimetry, by measuring the volume of
oxygen-inspired and carbon dioxide expired. The MHLLM can be alternately used to
estimate an energy expenditure based on a metabolic energy expenditure model which
25 uses several surrogate markers like muscle and joint power and energy, computed from
changes in muscle length and the muscle co-activation, in accordance with some
embodiments of the present disclosure. The energetic cost of walking is expressed as:
18
e𝑛𝑒𝑟𝑔𝑒𝑡𝑖𝑐 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑤𝑎𝑙𝑘𝑖𝑛𝑔 =
1
𝑇𝑚𝑣
∫ Σ𝐸̇𝑖 𝑑𝑡
𝑁
𝑖=1
𝑇
0
[049] Where 𝑇 is the duration of the stance phase of the gait cycle, 𝑚𝑣 is a
mass and speed of the CP subject, 𝑁 is the plurality of muscles, 𝐸̇𝑖 is energy rate of
muscle 𝑖.
[050] At step 318 of the method 5 300, the one or more hardware processors
are configured to combine the plurality of muscle response metrics, to generate the
AFO selector score, corresponding to each of the plurality of AFO controllers. The
plurality of response metric comprising the muscle impulse, the muscle yank, the
muscle co-activation, and the energetic cost of walking combined to obtain the score
10 that could guide towards selection of the personalized optimal AFO controller. A cost
function for the selection of the personalized optimal AFO controller is defined as:
𝐽 = 𝐸𝑛 ∗ 𝑦𝑛 ′ ∗ 𝐼𝑛 ′ ∗ 𝐶𝑛
[051] Where 𝐸𝑛 is a normalized energetic cost of walking, 𝑦𝑛 ′ is a normalized
ratio of the muscle yank computed between the soleus and the TA muscle, 𝐼𝑛 ′ is a
15 normalized ratio of the muscle impulse computed between the soleus and the TA
muscle, 𝐶𝑛 is a normalized muscle co-activation.
[052] At step 320 of the method 300, the one or more hardware processors
are configured to rank the plurality of AFO controllers based on the AFO selector
scores in increasing order of the cost function.
20 [053] At step 322 of the method 300, the one or more hardware processors are
configured to select a top ranked AFO controller as the personalized optimal AFO
controller from among the plurality of AFO controllers for the CP subject based on the
ranking. The selection of the personalized optimal AFO controller based on the ranking
that returns minimum value of J.
25 Experimental Details
[054] The motion-captured crouch gait data is used from a repository,
generated from motion analysis data captured at Gillette Childrens Specialty
19
Healthcare. It consists of kinematics and ground reaction force data of three typically
developing (TD) children and nine children with diplegic CP and crouch gait with
varying severity. Severity grading was based on knee flexion angle (KFA) during the
stance phase of the gait. The subjects were asked to walk barefoot overground at selfselected
speed. Out of total of 12 subject data, 5 1 TD subject and 3 CP subjects,
representing the mild crouch gait, the moderate crouch gait, a severe crouch gait.
Subject metadata are provided in Table.I
Subject Parameters
Severity
Age
(years)
Weight(kg) Height(m) Speed(m/sec)
KFA
(degree)
TD 10.2 41.1 1.45 1.01 1.4
Mild 9.4 28.2 1.31 0.94 15.5
Moderate 8.7 21.1 1.31 0.9 33.1
Severe 13.2 35.9 1.44 0.8 60.4
Table. I
[055] The MHLLM is used for analysis of the crouch gait that was adapted
10 from a pre-existing full-body model in Opensim consisting of 92 muscle actuators and
19 degrees of freedom in torso and lower extremities. Arm motion was not considered
for analysis of the crouch gait. The MHLLM is scaled as per anthropomorphic data of
each child under assessment.
[056] Inverse kinematics (IK) pipeline was used to convert the motion15
captured crouch data to the plurality of joint ankle angles. Residual forces were
adjusted using a residual reduction algorithm for missing upper body arm motion and
the torso mass centre was re-adjusted based on body segment accelerations and
measured ground reaction forces. The additional Joint torques are calculated using the
inverse dynamics mechanism, using joint trajectory generated from the inverse
20 kinematics pipeline and the measured ground reaction force. The plurality of muscle
forces generated in the MHLLM are based on a Thelen muscle actuator.
20
[057] Ground or floor reaction AFOs are plastic moulded custom-fabricated
solid AFO (SAFO), providing ankle support and stability by reducing ankle
dorsiflexion and excessive knee flexion in the stance phase of gait. The GRAFO is
generally effective in controlling tibial advancement over the foot in midstance phase,
improving knee extension utilizing 5 the plantarflexion or knee extension couple
concept. The GRAFO can be readily used for CP, flat foot correction, posterior tibial
tendon dysfunction, osteoarthritis, spinal cord injuries, and thereof. Posterior leaf
spring orthosis (LSAFO) is a modified SAFO, with a characteristic trimline located
behind the ankle and a leaf-shaped corrugation or creases near the ankle. The creases
10 act like spring, allowing slight dorsiflexion in the mid and terminal stance. Energy
returned from spring assist in push-off in terminal stance. The LSAFO are generally
used for motor weakness in ankle dorsiflexors due to CP or stroke. The foot plate and
the shank plate support of the GRAFO and the LSAFO were designed in Solidworks
utilizing standard dimensions and weights. The design was imported in the Opensim
15 and scaled for specific subjects under analysis. The AFO foot plate was fixed to the
calcaneous and the shank plate was fixed to tibia of the MHLLM model. The AFO
Torque generated by the plurality of AFO were modelled, guided by the subjectspecific
joint ankle angle kinematics along with the optimal stiffness value and the
equilibrium angle, which is the angle between the AFO footplate and the shank plate
20 at which the AFO starts generating the AFO torque. once the AFO is fixed to the joint
ankle angle of the plurality of joint ankle angles, an AFO angle is considered same as
the sagittal plane ankle motion throughout the gait cycle, derived from IK pipeline in
the MHLLM model. The AFO torque generated by the plurality of AFOs were
integrated in Opensim model dynamics.
25 [058] Unassisted walking for the TD subject, and assisted walking with the
GRAFO and the LSAFO as well as unassisted walking for subjects with crouch gait
were simulated and analyzed for one gait cycle. The plurality of muscle forces and the
plurality muscle response metrics are computed only for the stance phase of the gait
21
cycle. FIGS. 4A, 4B, and 4C depict a plurality of muscle force across the severity of
the crouch gait the assisted walking and the unassisted walking, in accordance with
some embodiments of the present disclosure. More specifically Fig.4 depicts a mean
muscle force variation through crouch severity, scaled with respect to the body weight
for the assisted walking with the GRAFO, 5 the LSAFO, and the unassisted walking.
Table. II shows the plurality of muscle response metrics muscle parameters for the TD
child as a reference.
Muscle response metrics for TD subject
Muscles GM HAM RF ILL VAS GAS SOL TA
Muscle Force (N) 0.97 0.38 0.5 2.5 0.88 0.56 3.5 1.2
Muscle Impulse (Nsec) 0.66 0.25 0.2 1.9 0.25 0.3 2.8 0.3
Muscle Yank (N/sec) 0.03 0.13 0.4 0.4 1.68 2.7 0.57 2.1
Table.II
[059] Compared to the normal gait (TD), subjects walking in the crouch gait
10 utilized higher muscle forces, and recruitment force increased as severity increased. As
knee flexion increases during the crouch gait, there is a greater demand for the plurality
of muscle forces to support and propel the body. This is most prominent in quadriceps
group (combination of RF and VAS muscle group). The difference between the assisted
walking and the unassisted walking was mostly reflected in the planter flexor (SOL,
15 GAS) and dorsiflexor muscles (TA), indicating that the use of the AFO, both solid and
spring type does not affect knee or hip muscles directly. The AFO torque generated by
the GRAFO and the LSAFO mostly translates in controlling Soleus (PF) and TA (DF)
muscle pair, by increasing DF force and decreasing PF force. The GRAFO produced
high TA force compared to LSAFO. GRAFO torque operates only during DF phase,
20 hence its effect on DF muscle is more prominent than the PF counterpart. Soleus
muscle control is better in LSAFO, providing reduction in muscle force across crouch
severity. However, muscle behavior across severity for the GRAFO and the LSAFO
are not consistent, following different trends for the mild crouch gait, the moderate
crouch gait, and the severe crouch gait.
22
[060] It is noticed that changes in plurality of muscle forces are prominent in
PF-DF muscle pair. FIG. 5 depicts the muscle impulse for the plurality of muscles
across the severity of crouch gait, in accordance with some embodiments of the present
disclosure. More specifically FIG. 5 depicts the muscle impulse for the GAS, the SOL,
and the TA across the severity of crouch gait, in 5 accordance with some embodiments
of the present disclosure.
[061] FIG. 6 depicts muscle yank for a plurality of muscles across the severity
of crouch gait, in accordance with some embodiments of the present disclosure. More
specifically FIG. 6 depicts the muscle yank for the GAS, the SOL, and the TA across
10 the severity of crouch gait, in accordance with some embodiments of the present
disclosure.
[062] The values of the muscle impulse and the muscle yank during the stance
phase, are scaled with respect to the BW for the TD subject for all the plurality of
muscles provided in Table II. The muscle impulse follows an increasing trend across
15 the severity of the crouch gait. Compared to the unassisted walking, both types of
assisted walking reduced PF muscle impulse and increased DF (TA) impulse. For mild
gait crouch, GAS impulse was almost similar for the GRAFO and the LSAFO, but for
the moderate crouch gait and the severe crouch gait, the GRAFO decreased GAS
impulse to a greater extent. But for Soleus muscle, in all the three severity groups, the
20 LSAFO reduced impulse to a greater extent compared to the GRAFO. For TA, the
assistance by the GRAFO provided increased muscle impulse compared to the LSAFO.
[063] The muscle yank shows a decreasing trend for the PF muscles in
assisted walking compared to the unassisted and an increasing trend for the DF
muscles. An increase in magnitude of the muscle yank refers to a high rate of force
25 development, associated with activities where takeoff velocity needs to be maximized
like jumping or leaping. Higher value of the muscle yank supports higher speed of
walking or running and the high muscle yank value in the DF muscles with the AFO
could better stabilize the ankle with increase in speed. In between severity groups, the
23
GRAFO reported lower rate of force development for the SOL muscle and the GAS
muscle. For the TA muscle, the GRAFO produced a higher muscle yank value for the
moderate crouch gait while the LSAFO performed better in the severe crouch gait.
[064] FIG. 7 depicts co-activation calculated for the plurality of muscles
across the severity of crouch gait, in accordance with 5 some embodiments of the present
disclosure. More specifically FIG. 7 depicts the muscle co-activation calculated for
ankle joint movement considering the SOL and the TA muscle pair for GRAFO,
LSAFO and unassisted walking across varying severity. The muscle co-activation is
reduced with the use of the GRAFO, the LSAFO compared to unassisted walking.
10 Lower muscle co-activation refers to lower joint stability but an improved rate of
motion due to decreased stiffness. FIG. 8 depicts an energy cost of walking calculated
for the plurality of muscles across the severity of crouch gait, in accordance with some
embodiments of the present disclosure. The crouch gait is a result of biomechanical
alteration to decrease the energetic cost of walking. As reduction in the energetic cost
15 of walking is of primal importance compared to joint stability, a decrease in the muscle
co-activation is desired. For the mild crouch gait and severe crouch gait, the LSAFO
performance was better compared to the GRAFO. The energetic cost of walking
increases with the severity of the crouch gait. Between the GRAFO and the LSAFO,
the energetic cost of walking is least in the LSAFO across the crouch severity due to
20 the additional spring-induced push-off power generation.
[065] Analysis of the plurality of various muscle response metrics for the
plurality of AFO controllers of AF the GRAFO and the LSAFO across the severity of
the crouch gait indicates the effectiveness of using AFO in modifying crouch dynamics.
In terms of energetic cost of walking, the LSAFO showed maximum efficacy, while
25 for the other muscle response metrics, the LSAFO and the GRAFO performance varied
with change in severity of the crouch gait. Results suggest that the personalized optimal
AFO controller, dictated by the crouch gait and muscle dynamics of the CP subject
could provide the best performance. Understanding of muscle behavior could also aid
24
in prescribing muscle-strengthening activities that could help CP subject to walk in a
more upright posture.
[066] The experimental results indicate improvement in the energetic cost of
walking using the LSAFO across the severity of the crouch gait, whereas indices like
the muscle impulse, the muscle yank, and the muscle co-5 activation were better with the
GRAFO in some cases and LSAFO in other cases. The analysis of muscle response
metrics could aid in optimizing selection of the AFO that results in lower muscle
demand and metabolic cost based on subject-specific gait dynamics. The disclosed
method apart from selecting the personalized optimal AFO can also aid in identifying
10 potential muscles for training and strengthening that could reduce muscle spasticity,
stiffness and weakness associated with the CP or other musculoskeletal disorders.
[067] 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
15 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.
[068] Embodiments herein provide a method and system for personalized and
20 optimal selection of AFO. There are multiple AFO variants, however selecting the
optimal AFO for a CP subject is often challenging. The disclosed method focusses on
selecting personalized and optimal selection of the AFO controller using the AFO
torque, and the plurality joint ankle angles of each of the plurality of AFO controllers
integrated with the MHLLM. The plurality of muscle forces is computed using the
25 MHLLM for each of the plurality of AFO controllers. Further the method computes
the plurality of muscle response metrics comprising the muscle impulse, the muscle
yank, the muscle co-activation, and the energetic cost of walking, from the plurality of
muscle forces and the additional joint torque for each of the AFO controllers. The
25
plurality of muscle response metrics is combined which enables the selection of the
personalized optimal AFO controller among the plurality of AFO controllers of the CP
subject.
[069] It is to be understood that the scope of the protection is extended to such
a program and in addition to a computer-readable 5 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
10 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 applicationspecific
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
15 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.
[070] The embodiments herein can comprise hardware and software
20 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 computerusable
or computer readable medium can be any apparatus that can comprise, store,
25 communicate, propagate, or transport the program for use by or in connection with the
instruction execution system, apparatus, or device.
[071] The illustrated steps are set out to explain the exemplary embodiments
shown, and it should be anticipated that ongoing technological development will
26
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 5 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
10 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.
15 [072] 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,
20 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.
[073] 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 (300), the method comprising:
receiving (302), via a one or more hardware processors, a motion-captured crouch gait data, a height, a body weight, and a severity of a crouch gait pertaining to a Cerebral Palsy (CP) subject;
computing (304), via the one or more hardware processors, a joint ankle angle kinematics comprising a plurality of joint ankle angles, from the motion-captured crouch gait data, at a plurality of three-dimensional (3D) ankle joint locations of the CP subject using an inverse kinematics pipeline;
feeding (306), via the one or more hardware processors, the plurality joint ankle angles, the height, and the body weight of the CP subject, to a plurality of Ankle Foot Orthosis (AFO) controllers, wherein each of the plurality of AFO controllers are programmed with associated mechanical behavior;
computing (308), by the one or more hardware processors, a corresponding AFO torque, by each of the plurality of AFO controllers in accordance with the associated mechanical behavior;
integrating (310), by the one or more hardware processors, the generated AFO torque, and the plurality joint ankle angles of each of the plurality of AFO controllers with a musculoskeletal human lower limb model (MHLLM) comprising a human skeleton and a plurality of lower limb muscles, to generate an associated AFO integrated MHLLM, corresponding to each of the plurality of AFO controllers;
performing (312), by the one or more hardware processors, an inverse dynamics mechanism on the associated AFO integrated MHLLM, to compute an additional joint torque at the plurality of ankle joint angles of the human skeleton, corresponding to each of the plurality of AFO controllers;

computing (314), by the one or more hardware processors, a plurality of muscle forces corresponding to the plurality of lower limb muscles of the associated AFO integrated MHLLM, for a gait cycle of the CP subject, using a static optimization framework, corresponding to each of the plurality of AFO controllers;
computing (316), by the one or more hardware processors, a plurality of muscle response metrics comprising a muscle impulse, a muscle yank, a muscle co-activation, and an energetic cost of walking, from the plurality of muscle forces and the additional joint torque, corresponding to each of the plurality of AFO controllers;
combining (318), by the one or more hardware processors, the plurality of muscle response metrics, to generate an AFO selector score, corresponding to each of the plurality of AFO controllers;
ranking (320), by the one or more hardware processors, the plurality of AFO controllers in increasing order based on the AFO selector scores; and
selecting (322), by the one or more hardware processors, a top ranked AFO controller as a personalized optimal AFO controller from among the plurality of AFO controllers for the CP subject.
2. The processor implemented method as claimed in claim 1, wherein the mechanical behavior of each of the plurality of AFO controllers is composed as combination of an optimal stiffness value, and an AFO equilibrium angle, for generating the corresponding AFO torque.
3. The processor implemented method as claimed in claim 1, wherein the additional joint torque is computed based on the AFO equilibrium angle, the optimal stiffness value, and the plurality of joint ankle angles of the CP subject.

4. The processor implemented method as claimed in claim 1, wherein the MHLLM is designed based on the severity of the crouch gait.
5. The processor implemented method as claimed in claim 1, wherein the severity of the crouch gait comprises one of (i) a normal gait, (ii) a mild crouch gait, (iii) a moderate crouch gait, and (iv) a severe crouch gait.
6. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
receive a motion-captured crouch gait data, a height, a body weight, and a severity of a crouch gait pertaining to a Cerebral Palsy (CP) subject;
computing a joint ankle angle kinematics comprising a plurality of joint ankle angles, from the motion-captured crouch gait data, at a plurality of three-dimensional (3D) ankle joint locations of the CP subject using an inverse kinematics pipeline;
feed the plurality joint ankle angles, the height, and the body weight of the CP subject, to a plurality of Ankle Foot Orthosis (AFO) controllers, wherein each of the plurality of AFO controllers are programmed with associated mechanical behaviour;
compute a corresponding AFO torque, by each of the plurality of AFO controllers in accordance with the associated mechanical behaviour;
integrate the generated AFO torque, and the plurality joint ankle angles of each of the plurality of AFO controllers with a musculoskeletal human lower

limb model (MHLLM) comprising a human skeleton and a plurality of lower limb muscles, to generate an associated AFO integrated MHLLM, corresponding to each of the plurality of AFO controllers;
perform an inverse dynamics mechanism on the associated AFO integrated MHLLM, to compute an additional joint torque at an ankle joint of the human skeleton, corresponding to each of the plurality of AFO controllers;
compute a plurality of muscle forces corresponding to the plurality of lower limb muscles of the associated AFO integrated MHLLM, for a gait cycle of the CP subject, using a static optimization framework, corresponding to each of the plurality of AFO controllers;
compute a plurality of muscle response metrics comprising a muscle impulse, a muscle yank, a muscle co-activation, and an energetic cost of walking, from the plurality of muscle forces and the additional joint torque, corresponding to each of the plurality of AFO controllers;
combine the plurality of muscle response metrics, to generate an AFO selector score, corresponding to each of the plurality of AFO controllers;
rank the plurality of AFO controllers in increasing order based on the AFO selector scores; and
select a top ranked AFO controller as a personalized optimal AFO controller from among the plurality of AFO controllers for the CP subject based on the ranking.
7. The system as claimed in claim 6, wherein the mechanical behavior of each of the plurality of AFO controllers is composed as combination of an optimal stiffness value, and an AFO equilibrium angle, for generating the corresponding AFO torque.

8. The system as claimed in claim 6, wherein the additional joint torque is computed based on the AFO equilibrium angle, the optimal stiffness value, and the plurality of joint ankle angles of the CP subject.
9. The system as claimed in claim 6, wherein the MHLLM is designed based on the severity of the crouch gait.
10. The system as claimed in claim 6, wherein the severity of the crouch gait
comprises one of (i) a normal gait, (ii) a mild crouch gait, (iii) a moderate crouch gait, and (iv) a severe crouch gait.