Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR BIOMECHANICAL DESIGN OPTIMIZATION OF PASSIVE EXOSKELETON

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
The disclosure herein generally relates to the field of exoskeletons and more particularly, to a method and system for optimizing a biomechanical design of a passive exoskeleton.

BACKGROUND
Exoskeletons are a class of wearable robotic devices intended to augment the human body’s capabilities. In industrial settings, the exoskeletons are designed to increase a user’s load carrying capacity and prevent injuries due to repetitive stress or fatigue. With demographic shifts towards aging populations in industrialized nations, health and safety requirements are being introduced to reduce physical loads on workers in industries such as manufacturing, construction, and warehouse logistics.
Current passive exoskeleton technologies are being tested and deployed in these industries. However, the adoption of these devices has been slow, partly due to the feedback received from workers which highlight the limitations of these devices. They are considered to be cumbersome, not ergonomic, and pose a hindrance in other movements aside from the tasks for which they were specifically designed. The reason behind this mismatch in designs and outcomes is that most exoskeletons are designed in laboratory settings and tested in limited task scenarios, without taking into account the effects of muscle fatigue during tasks, and the device’s effects on ambulatory motions.

SUMMARY
Embodiments of the 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 system for optimizing a biomechanical design of a passive exoskeleton is provided.
In one aspect, a processor-implemented method for optimizing a biomechanical design of a passive exoskeleton. The processor-implemented method comprising receiving a plurality of motion capture data frames, extracting physical and geometric design parameters of an exoskeleton, computing one or more moments exerted by the exoskeleton at a corresponding shoulder joint of each arm, computing one or more joint angles through an inverse kinematics procedure from the plurality of motion capture data frames, determining joint torque of the human body from the computed one or more joint angles using a rigid body inverse dynamics procedure, and predicting muscle forces and activations from the joint angles, joint torques, and exoskeleton moments using an ensemble regression with bootstrap aggregation.
Further, the processor-implemented method comprising calculating an effect of the exoskeleton on the human body by subtracting the generated one or more moments from the exoskeleton due to arm movement on each side from the determined joint torque, determining the muscle effort rate for each muscle from the muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models. Further, the effect of joint torques, muscle fiber forces and muscle activations are determined by applying a mathematical model of the exoskeleton to a biomechanical model of the human body.
Furthermore, a cost function is computed based on the extracted geometric and physical design parameters of the exoskeleton, at least one optimal design parameter is determined using a pareto local search to minimize the computed cost function using the determined at least one optimal design parameter.
In another aspect, a system for optimizing a biomechanical design of a passive exoskeleton is provided. The system includes an input/output interface configured to receive a plurality of motion capture data frames, one or more hardware processors and at least one memory storing a plurality of instructions, wherein the one or more hardware processors are configured to execute the plurality of instructions stored in the at least one memory. Further, the system extracts physical and geometric design parameters of an exoskeleton, computes one or more joint angles through an inverse kinematics procedure from motion capture data frames and determines joint torque of each human body from the computed one or more joint angles using a rigid body inverse dynamics procedure.
Furthermore, the system is configured to predict muscle forces and activations from the joint angles, joint torques, and exoskeleton moments using an ensemble regression with bootstrap aggregation, calculate an effect of the exoskeleton on the human body by subtracting the generated one or more moments from the exoskeleton due to arm movements on each side from the determined joint torque and determine the muscle effort rate for each muscle from muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models.
Further, the system determines effect of joint torques, muscle fiber forces and muscle activations by applying a mathematical model of the exoskeleton to a human bio mechanical model, compute a cost function based on the extracted geometric and physical design parameters of the exoskeleton, determine at least one optimal design parameter using a pareto local search, and minimize the computed cost function using the determined at least one optimal design parameter.
In yet another aspect, one or more non-transitory machine-readable information storage mediums are provided comprising one or more instructions, which when executed by one or more hardware processors causes a method for optimizing a biomechanical design of a passive exoskeleton is provided. The processor-implemented method comprising receiving a plurality of motion capture data frames, extracting physical and geometric design parameters of an exoskeleton, computing one or more moments exerted by the exoskeleton at a corresponding shoulder joint of each arm, computing one or more joint angles through an inverse kinematics procedure from the plurality of motion capture data frames, determining joint torque of the human body from the computed one or more joint angles using a rigid body inverse dynamics procedure, and predicting muscle forces and activations from the joint angles, joint torques, and exoskeleton moments using an ensemble regression with bootstrap aggregation.
Further, the processor-implemented method comprising calculating an effect of the exoskeleton on the human body by subtracting the generated one or more moments from the exoskeleton due to arm movement on each side from the determined joint torque, determining the muscle effort rate for each muscle from the muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models. Further, the effect of joint torques, muscle fiber forces and muscle activations are determined by applying a mathematical model of the exoskeleton to a biomechanical model of the human body.
Furthermore, a cost function is computed based on the extracted geometric and physical design parameters of the exoskeleton, at least one optimal design parameter is determined using a pareto local search to minimize the computed cost function using the determined at least one optimal design parameter.
It is to be understood that the foregoing general descriptions and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
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:
FIG. 1 illustrates a block diagram of an exemplary system for optimizing a biomechanical design of a passive exoskeleton, in accordance with some embodiments of the present disclosure.
FIG. 2 is a schematic diagram to illustrate the exoskeleton applied to both arms, in accordance with some embodiments of the present disclosure.
FIG. 3 is a block diagram to illustrate a biomechanics model which is replaced with a fast approximate surrogate model for design optimization, in accordance with some embodiments of the present disclosure.
FIGS. 4(a) and 4(b) collectively referred as FIG. 4 is a flow chart to illustrate a method for optimizing a biomechanical design of a passive exoskeleton, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
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.
The embodiments herein provide a method and system for optimizing a biomechanical design of a passive exoskeleton. Herein, the passive exoskeleton is considered as an arm support device in overhead tasks, has numerous designs in the form of commercial products. The biomechanical design of the passive exoskeleton typically involves the spring and cable driven systems with mechanism for providing adjustable assistance by engaging and disengaging based on the human body configuration. The passive exoskeleton provides enhancements in terms of biomechanical efforts and task performance. However, most biomechanical designs are evaluated within laboratory settings, resulting in mismatches between their projected assistance and real-world effectiveness during field trials. Additionally, most passive exoskeletons consider quasi-static overhead tasks while evaluating muscle activation.
An application of a data driven design optimization is disclosed herein to mitigate shortcomings in evaluating the passive exoskeletons. In this approach attempts being made to find one or more design parameters of the passive exoskeleton that reduces muscle efforts not just in agonist muscles, but also in the antagonist one during realistic task scenarios. Usually, during the design stage, the objective is to minimize shoulder elevation joint torques and provide gravity compensation instead of trying to minimize muscle effort rates, which may be a more direct indicator of long-term effects.
In addition to this, a high-fidelity biomechanical model of the upper body is used to generate a cost function based on the one or more design parameters of the exoskeleton. However, it is found that the biomechanical simulations are highly computation intensive tasks for the design parameter search through a multi-objective optimization that requires numerous evaluations of the system’s dynamics. Especially for design optimization, it is often sufficient to accurately model the trends between system inputs and outputs rather than accuracy from the ground truth. Therefore, surrogate models are used for human arm muscles to compute muscle efforts in an approximate but rapid manner.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 4, 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.
FIG. 1 illustrates a block diagram of an exemplary system (100) for optimizing a biomechanical design of a passive exoskeleton, in accordance with an example embodiment. Although the present disclosure is explained considering that the system (100) is implemented on a server, it may be understood that the system (100) may comprise one or more computing devices (102), such as a laptop computer, a desktop computer, a notebook, a workstation, a cloud-based computing environment and the like. It will be understood that the system (100) may be accessed through one or more input/output interfaces 104-1, 104-2... 104-N, collectively referred to as I/O interface (104). Examples of the I/O interface (104) may include, but are not limited to, a user interface, a portable computer, a personal digital assistant, a handheld device, a smartphone, a tablet computer, a workstation, and the like. The I/O interface (104) are communicatively coupled to the system (100) through a network (106).
In an embodiment, the network (106) may be a wireless or a wired network, or a combination thereof. In an example, the network (106) can be implemented as a computer network, as one of the different types of networks, such as virtual private network (VPN), intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network (106) may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other. Further, the network (106) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network (106) may interact with the system (100) through communication links.
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. Further, the system (100) comprises at least one memory (110) with a plurality of instructions, one or more databases (112), and one or more hardware processors (108) which are communicatively coupled with the at least one memory (110) to execute a plurality of instructions therein. The components and functionalities of the system (100) are described further in detail.
In one embodiment, the one or more I/O interfaces (104) of the system (100) are configured to receive a plurality of motion capture data frames. The plurality of motion capture data frames comprising annotated human motions in an industry like activities, captured using an optical marker and an inertial sensing suit. The plurality of motion capture data frames include data collected during tasks such as screwing bolts at various body configurations (i.e., standing, kneeling, crouching), fine manipulation of a tabletop, and carrying weights (pick-and-place). It is assumed that in overhead assembly scenarios a weight of the tool held during a task is small compared to the gravitational forces from the weight of the body links.
In another embodiment, the system (100) conducts biomechanics analyses on a human body to determine one or more biomechanical effects of the passive exoskeleton on the human body in the overhead assembly tasks.
Referring FIG. 2, a schematic diagram (200) to illustrate a symmetric setup of the passive exoskeleton for assistance to both arms of the human body, in accordance with some embodiments of the present disclosure. The system (100) extracts one or more physical and geometric design parameters of the passive exoskeleton applied on the human body and computes one or more moments exerted by the passive exoskeleton at a corresponding shoulder joint of each arm of the human body.
It is to be noted that a detailed effect of mounting points and harnessing on the back are not analyzed, since only the effect on the human arms and shoulders are studied. The symmetric setup of the passive exoskeleton comprises of springs, cams, and a cable drive that transmits forces to the upper arm via a cuff worn over the bicep. The cam is attached to a constant radius wheel of the moment arm (r_w). The cam has a parameterized sinusoidal profile (r_c) based on the wheel rotation angle (?).
r_(c?) (?)?=?A?sin?(B??)? (1)
wherein, A and B are parameters that determine shape of the cam profile (r_c) by varying the parameters the system (100) can get different geometric shape profiles for the cam. The wheel rotation angle (?) is directly proportional to the shoulder elevation angle (a):
??=?r_s/r_w a (2)
In yet another embodiment. the system (100) is configured to compute one or more moments exerted by the exoskeleton at the shoulder joints as a function of the shoulder elevation angle (a), stiffness of the spring, spring deflection at zero shoulder elevation, moment arm of the exoskeletons about the shoulder joint (r_s), (r_(c?)), and r_(w?)). For each arm, the one or more moments exerted by the corresponding side of the passive exoskeleton about the shoulder joint is given by:
M_exo?=?r_s/r_w ?r_c (a)[k_(e?) (x?-??_0^a¦?r_c (a) ??da)] (3)
wherein, the exoskeleton model comprises of a mathematical model of the mapping between torques exerted by the exoskeleton at the human joints that it is in contact with as a function of the angles of those joints. For the passive exoskeleton, the exoskeleton model depends on the geometric and physical properties of the elements used in the biomechanical design of the passive exoskeleton, such as springs, cables, and pulleys.
It would be appreciated that during the design stage, reducing the gravitational torque was the goal. However, minimizing muscle effort rates might be more beneficial in terms of long-term effects during repeated usage, leading to better outcomes in the field. Thus, the one or more moments assisting the human body by compensating for gravitational torques, generated during shoulder elevation. Further, joint torques are determined from joint angles using a rigid body inverse dynamics procedure.
Further, the one or more moments generated from the exoskeleton due to the arm movements on each side are subtracted from the shoulder elevation joint moments. The muscle activations and forces required to generate these joint movements and torques are estimated using numerical models for Hill-type muscles in the upper extremities using a predefined simulation framework. A cost function for design optimization is dependent on the muscle forces and activations during each task motion.
The one or more parameter vectors of the design for the passive exoskeleton T comprising geometrical and physical quantities as follows:
T?=?[r_s?,?r_w?,?A,?B,?X,?K] (4)
wherein, A and B are parameters that determine shape of the cam profile (r_c) and stiffness of the spring (K), spring deflection at zero shoulder elevation (X) and the optimization objective can be summarized as follows:
T^*?=?arg?min-T???G?(T)? (5)
where the cost function G?(T) depends on the muscle forces (F) and activations (a), both functions of the joint angles (q), joint torques (M), and the one or more moments of the passive exoskeleton.
The key metric for optimization is a muscle effort rate (E_m) for each muscle in each trial from the dataset, as it allows for direct comparison between motions of varying duration.
(E_m ) ??=?(?_(t=t_i)^(t_f)¦?a^2 (t)F(t) ?)/(t_f?-?t_i )? (6)
Referring FIG. 3, a block diagram (300) to illustrate a biomechanics model which is replaced with a fast approximate surrogate biomechanics model for design optimization, in accordance with some embodiments of the present disclosure. The surrogate biomechanics model maps joint angles and joint torques to biomechanical quantities induced in the human body such as muscle fiber forces, muscle activations and tendon forces. The surrogate biomechanics model is empirically determined model that follow Hill-type muscle behavior and validated on clinical data. Biomechanical models such as those built in OpenSim or CusToM require extensive computational resources to execute a single iteration, making them unsuitable as a step in an optimization routine. As a result, the high-fidelity biomechanics model is replaced with a lightweight surrogate biomechanics model that maps joint angles and torques to muscle forces and muscle activations using the ensemble regression with bootstrap aggregation to act as proxies in the optimization process.
Even though the surrogate biomechanics model is not as accurate as the biomechanical model, the surrogate biomechanics model serves the purpose of optimization as it accurately maps the trends between the input and output variables, joint angles and torques as inputs, and muscle forces and muscle activations as outputs. The surrogate biomechanics model may not be necessary if more computationally efficient biomechanics models are developed in the future.
(F_i ) ^?=?f(q_j?,?M_j )?,?(a_i ) ^?=?g(q_(j?),?M_j ) (7)
The surrogate biomechanics model is an ensemble regression with a bootstrap aggregation. The regression problem in this case is to predict muscle forces and activations as a function of joint angles and torques (including exoskeleton torques). To further speed up the computations, for the ith muscle, the angles (qj) and moments (Mj) of only the joints that it crosses are included in the regression models.
In yet another embodiment, the system (100) is configured to use the output of either biomechanical or surrogate biomechanics model to compute a relevant vector cost function that depends on the geometric and physical design parameters of the passive exoskeleton.
Measuring the assistance provided by the passive exoskeleton with the optimized design parameters compared to initial parameters, the muscle effort rates computed for both of these designs relative to the condition with no exoskeleton for the same task motions.
E_i^0?=?((E_(m,i)^exo ) ??(T_0 ))/(E_(m,i)^(no?exo) ) ? ?,?E_i^*?=?((E_(m,i)^exo ) ??(T^* ))/(E_(m,i)^(no?exo) ) ? (8)
wherein, E_i^0 for the initial design parameters, and E_i^* for the optimized ones.
Referring FIG. 4, to illustrate a processor-implemented method (400) for optimizing a biomechanical design of a passive exoskeleton
Initially, at step (402), the one or more hardware processors (108), receive, via an input/output interface, a plurality of motion capture data frames, wherein the plurality of motion capture data frames comprising annotated human motions in an industry like activities captured using an optical marker and an inertial sensing suit.
At the next step (404), extracting one or more physical and geometric design parameters of the passive exoskeleton applied on a human body.
At the next step (406), computing one or more moments exerted by the passive exoskeleton at a corresponding shoulder joint of each arm of the human body.
At the next step (408), computing one or more joint angles through an inverse kinematics procedure from a predefined motion capture data frames.
At the next step (410), determining joint torque of each human participant from the computed one or more joint angles using a rigid body inverse dynamics procedure.
At the next step (412), predicting muscle forces and activations from joint angles, joint torques, and exoskeleton moments using ensemble regression with bootstrap aggregation.
At the next step (414), calculating an effect of the exoskeleton on a human body by subtracting the generated one or more moments from the exoskeleton due to arm movements on each side from the determined joint torque.
At the next step (416), determining the muscle effort rate for each muscle from muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models.
At the next step (418), determining the effect of joint torques, muscle fiber forces and muscle activations by applying a mathematical model of the exoskeleton to a human bio mechanical model.
At the next step (420), computing a cost function based on the extracted geometric and physical design parameters of the exoskeleton.
At the next step (422), determining at least one optimal design parameter using a pareto local search. The determined at least one optimal design parameter is iteratively optimized to minimize the cost function.
At the next step (424), minimizing the computed cost function using the determined at least one optimal design parameter.

Experiment:
The means and standard deviations for the relative muscle effort rates for the two sets of design parameters are listed in a below table 1 for each of the 18 relevant muscles (prefixes L:left, R:right):

Default Parameters Optimized Parameters

Muscle Mean Std Dev. Mean Std. Dev.
RBL 0.583 0.212 0.809 0.139
RBS 0.997 0.225 0.951 0.110
RTlg 0.709 0.195 0.753 0.077
RDa 0.954 0.217 0.902 0.111
RDm 1.350 0.152 0.876 0.103
RDp 0.862 0.154 0.797 0.069
RP1 1.040 0.170 0.828 0.052
RP2 1.204 0.168 0.837 0.125
RP3 0.758 0.217 0.749 0.118
LBL 0.426 0.138 0.908 0.135
LBS 0.7751 0.243 0.969 0.147
LTlg 0.875 0.231 0.880 0.118
LDa 1.034 0.234 1.019 0.153
LDm 1.380 0.145 0.933 0.144
LDp 0.876 0.219 0.822 0.127
LP1 0.871 0.182 0.861 0.083
LP2 1.055 0.175 0.825 0.099
LP3 0.575 0.168 0.678 0.102
Table 1
Across all muscles, the mean improvement for the optimized exoskeleton parameters was 0.855 over no exoskeleton (14.5%), and the peak improvement was by 32.2%. For the design with default parameters the mean improvement over the no exoskeleton condition was 0.907, a reduction of 9.3%, with a peak improvement by 57.4%. Consequently, the design optimization procedure yields an average improvement of 5.73% and peak improvement of 35.1% over the default exoskeleton parameters.
While an exoskeleton with optimized parameters may not outperform the design with default parameters for every muscle of interest, it consistently reduces the muscle effort rates below the condition with no exoskeleton (except for LDa) for the trials in the test set. It may also be observed that for some muscles, the default parameters may increase the muscle effort rate compared to not wearing an exoskeleton in the test trial motions, possibly due to antagonistic effects where the device actually restricts a certain type of arm motion.
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.
The embodiments of present disclosure herein address the problem of biomechanical effects in terms of muscle fatigues and other parameters during realistic activities performed by workers. The reason behind this mismatch in designs and outcomes is that most exoskeletons are designed in laboratory settings and tested in limited task scenarios, without taking into account the effects of muscle fatigue during tasks, and the device’s effects on ambulatory motions. Embodiments herein provide a system and method for a design optimization of a passive exoskeleton that accounts for long-term biomechanical effects of the device in realistic settings in a data-driven manner, instead of relying on limited laboratory-based evaluation as is the norm.
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 modules 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.
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 modules described herein may be implemented in other modules or combinations of other modules. 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.
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.
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.
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.
, Claims:WE CLAIM:
1. A processor-implemented method (400) for optimizing a biomechanical design of a passive exoskeleton comprising steps of:
receiving (402), via an input/output interface, a plurality of motion capture data frames, wherein the plurality of motion capture data frames comprising annotated human motions in an industry like activities captured using an optical marker and an inertial sensing suit;
extracting (404), via the one or more hardware processors, one or more physical and geometric design parameters of the passive exoskeleton, wherein the passive exoskeleton is considered as an arm support device in overhead tasks;
computing (406), via the one or more hardware processors, one or more moments exerted by the passive exoskeleton at a corresponding shoulder joint of each arm of the human body;
computing (408), via the one or more hardware processors, one or more joint angles through an inverse kinematics procedure from a predefined motion capture data frames;
determining (410), via the one or more hardware processors, joint torque of the human body from the computed one or more joint angles using a rigid body inverse dynamics procedure;
predicting (412), via the one or more hardware processors, muscle forces and activations from joint angles, joint torques, and exoskeleton moments using an ensemble regression with bootstrap aggregation;
calculating (414), via the one or more hardware processors, an effect of the exoskeleton on the human body by subtracting the generated one or more moments from the exoskeleton due to arm movements on each side from the determined joint torque;
determining (416), via the one or more hardware processors, the muscle effort rate for each muscle from the muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models;
determining (418), via the one or more hardware processors, the effect of joint torques, muscle fiber forces and muscle activations by applying a mathematical model of the exoskeleton to a human bio mechanical model;
computing (420), via the one or more hardware processors, a cost function based on the extracted geometric and physical design parameters of the exoskeleton;
determining (422), via the one or more hardware processors, at least one optimal design parameter using a pareto local search; and
minimizing (424), via the one or more hardware processors, the computed cost function using the determined at least one optimal design parameter.
2. The processor-implemented method as claimed in claim 1, wherein the biomechanical design of the passive exoskeleton involves a spring and a cable driven mechanism for providing an adjustable assistance by engaging and disengaging based on the human body configuration.
3. The processor-implemented method as claimed in claim 1, wherein the passive exoskeleton provides enhancements in terms of biomechanical efforts and task performance.
4. The processor-implemented method as claimed in claim 1, wherein the one or more moments are computed as a function of a shoulder elevation angle, stiffness of a spring, spring deflection at zero shoulder elevation, moment arm of the exoskeletons about the shoulder joint.
5. The processor-implemented method as claimed in claim 1, wherein a human motion dataset is used to obtain the motion capture data frames of human participants performing realistic industrial tasks.
6. The processor-implemented method as claimed in claim 1, wherein muscle forces and activations are functions of the of joint angles and torques.
7. A system for optimizing a biomechanical design of a passive exoskeleton comprising:
an input/output interface to receive a plurality of motion capture data frames, wherein the plurality of motion capture data frames comprising annotated human motions in an industry like activities captured using an optical marker and an inertial sensing suit;
a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to;
extract physical and geometric design parameters of an exoskeleton;
compute one or more moments exerted by the exoskeleton at a corresponding shoulder joint of each arm;
compute one or more joint angles through an inverse kinematics procedure from motion capture data frames;
determine joint torque of each human participant from the computed one or more joint angles using a rigid body inverse dynamics procedure;
predict muscle forces and activations from joint angles, joint torques, and exoskeleton moments using ensemble regression with bootstrap aggregation;
calculate an effect of the exoskeleton on a human body by subtracting the generated one or more moments from the exoskeleton due to arm movements on each side from the determined joint torque;
determine the muscle effort rate for each muscle from muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models;
determine effect of joint torques, muscle fiber forces and muscle activations by applying a mathematical model of the exoskeleton to a human bio mechanical model;
compute a cost function based on the extracted geometric and physical design parameters of the exoskeleton;
determine at least one optimal design parameter using a pareto local search; and
minimize the computed cost function using the determined at least one optimal design parameter.
8. The system as claimed in claim 7, wherein the biomechanical design of the passive exoskeleton involves a spring and a cable driven mechanism for providing an adjustable assistance by engaging and disengaging based on the human body configuration.
9. The system as claimed in claim 7, wherein the one or more moments are computed as a function of a shoulder elevation angle, stiffness of a spring, spring deflection at zero shoulder elevation, moment arm of the exoskeletons about the shoulder joint.
10. The system as claimed in claim 7, wherein a human motion dataset is used to obtain the motion capture data frames of human participants performing realistic industrial tasks.
11. The system as claimed in claim 7, wherein muscle forces and activations are functions of the of joint angles and torques.
12. A non-transitory computer readable medium storing one or more instructions which when executed by one or more processors on a system, cause the one or more processors to perform method comprising:
receiving, via an input/output interface, a plurality of motion capture data frames, wherein the plurality of motion capture data frames comprising annotated human motions in an industry like activities captured using an optical marker and an inertial sensing suit;
extracting, via one or more hardware processors, one or more physical and geometric design parameters of the passive exoskeleton applied on a human body;
computing, via the one or more hardware processors, one or more moments exerted by the passive exoskeleton at a corresponding shoulder joint of each arm of the human body;
computing, via the one or more hardware processors, one or more joint angles through an inverse kinematics procedure from a predefined motion capture data frames;
determining, via the one or more hardware processors, joint torque of the human body from the computed one or more joint angles using a rigid body inverse dynamics procedure;
predicting, via the one or more hardware processors, muscle forces and activations from joint angles, joint torques, and exoskeleton moments using an ensemble regression with bootstrap aggregation;
calculating, via the one or more hardware processors, an effect of the exoskeleton on a human body by subtracting the generated one or more moments from the exoskeleton due to arm movements on each side from the determined joint torque;
determining, via the one or more hardware processors, the muscle effort rate for each muscle from muscle forces and activations, both functions of the joint angles, joint torques, and exoskeleton moments using surrogate regression models;
determining, via the one or more hardware processors, the effect of joint torques, muscle fiber forces and muscle activations by applying a mathematical model of the exoskeleton to a human bio mechanical model;
computing, via the one or more hardware processors, a cost function based on the extracted geometric and physical design parameters of the exoskeleton;
determining, via the one or more hardware processors, at least one optimal design parameter using a pareto local search; and
minimizing, via the one or more hardware processors, the computed cost function using the determined at least one optimal design parameter.

Dated this 19sth Day of October 2022

Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086