Claims:1. A system (100) to facilitate detecting and analyzing movement deviation, the system (100) comprising:
a flexible device (102) adapted to be worn by an entity at a pre-determined part of the entity , the flexible device (102) comprising:
a first set of sensors (104) configured at a pre-determined position of the flexible device (102) and configured to sense force exerted by the pre-determined part of the entity on the flexible device upon movement and correspondingly generate a first set of signals;
a second set of sensors (106) configured to sense posture and movement of the pre-determined part of the entity and correspondingly generate a second set of signals, and
a third set of sensors (108) configured to sense one or more muscles response of the pre-determined part of the entity based on the movement of the one or more muscles and correspondingly generate a third set of signals,
an input interface (110) configured to receive one or more input credentials from the entity and correspondingly generate a set of input signals, and
a controller (112) operatively coupled to the first set of sensors (104), the second set of sensors (106), and the third set of sensors (108), the input interface (110), wherein the controller (112) including one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors configured to:
receive the first set of signals, the second set of signals, the third set of signals and the set of input signals;
extract a pressure and force value from the first set of signals, an acceleration value from the second set of signals , neuromuscular deviation from the third set of signals;
update and train a training and testing dataset based on the extracted pressure and force value , the acceleration value , the neuromuscular deviation and the received set of input signals, wherein the pressure and force value, the acceleration value, and the neuromuscular deviation pertains to movement parameters;
analyze the training and testing dataset;
classify the analyzed training and training dataset into a first section and a second section, wherein the first section pertains to deviated movement parameters based on the analyzed training and testing dataset, and wherein the second section pertains to non deviated movement parameters based on the analyzed training and testing dataset, and enables detecting and analyzing the deviation in movement, and
transmit a set of alert signals to an alert unit (114) when the classified training and testing dataset is in the first section, wherein the set of alert signals facilitate alerting the entity for deviation in movement and enables detection of the deviation in movement.
2. The system (100) as claimed in claim 1, wherein the system (100) includes an alert unit (114) operatively coupled to the controller (112), wherein the alert unit (114) is configured to receive the set of alert signals and activated based on the received set of alert signals, and wherein the alert unit (114) includes any or a combination of light emitting diode, alarm, buzzer, and vibrator.
3. The system (100) as claimed in claim 1, wherein the first set of sensors (104) include any or a combination of pressure sensor, force sensor, piezoelectric sensor, and wherein the second set of sensors (106) include any or a combination of accelerometer, gyroscope, and wherein the third set of sensors (108) include any or a combination of pulse sensor, and electromyography sensor.
4. The system (100) as claimed in claim 1, wherein the controller (112) is in communication with one or more mobile computing device through a communication module, wherein the one or more mobile computing devices are configured to receive the set of alert signals and enables in analyzing the deviation of movement associated with the entity.
5. The system (100) as claimed in claim 1, wherein the input interface (110) includes any or a combination of display with key, screen with key, wherein the key enables entering the one or more input credentials.
6. The system (100) as claimed in claim 5, wherein the one or more input credentials include any or a combination of age, gender, family history, head injury history, stoke history, and neuromuscular disease history.
7. The system (100) as claimed in claim 1, wherein the pre-determined part associated with the entity includes any or a combination of foot, leg, hand, arm, thigh, and calf.
8. The system (100) as claimed in clam 1, wherein the flexible device (102) includes any or a combination of splint, foot drop splint, belt, and brace.

Description:TECHNICAL FIELD
[0001] The present disclosure relates generally to health and medical field. More particularly, the present disclosure provides a system to facilitate detecting and analyzing movement deviation related to gait, but not limited to the likes.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Spasticity condition in which muscles stiffen or tighten, preventing normal fluid movement. The muscles remain contracted and resist being stretched, thus affecting movement, speech and gait. Diversity of gait deviations observed in children has led to repeated efforts to develop a valid and reliable gait classification system to assist in the diagnostic process, clinical decision making and the communication of a child’s presentation between clinicians. Gait classification allocates walking patterns into groups that can be identified and differentiated from one another based on a set of defined variables.. Hence, many of research on gait classification has focused on children who present predominantly with spasticity.
[0004] Existing solutions can include surgeries, medicinal treatment with help of injections, electroencephalogram (EEG) scans etc which are expensive and are not economical. Also, the existing solution cannot be used on larger scale and chances of correct diagnoses of disorders can be uncertain. The existing solution does not discloses about early diagnosis of gait or spasticity related disorders.
[0005] There is a need to overcome above mentioned problems of prior art by bringing a solution that help in early diagnosis of gait or spasticity related disorder and facilitate distinguishing between a normal healthy person and a disorder affected person with help of deep leaning based classifiers. Also, the solution enables in saving money as the solution eliminates need of costly sugery, medicnal treatment and other means for treating the gait or spasticity related disorder and is cost effective. The solution analyzes and monitors gait of person and helps in determining disorder.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] It is an object of the present disclosure to provide a system that helps in detecting gait related disorder like cerebral palsy and the likes with help of deep learning algorithm, where the depp learning algorithm classifiers enables distinguishing a normal healthy person from a person affected with disorder.
[0008] It is an object of the present disclosure to provide a system that facilitate providing aid to parents for analyzing gait of children and assessing the children on basis of gait.
[0009] It is an object of the present disclosure to provide a system that enables correct diagnosis of disease related to gait and help in ruling out other disorders thereby avoiding unnecessary lab tests.
[0010] It is an object of the present disclosure to provide a system that is economical and can be used on larger scale.
[0011] It is an object of the present disclosure to provide a system that helps in avoiding expensive surgery, medicinal treatment and other means for treating disorder related to gait thereby saving money.

SUMMARY
[0012] The present disclosure relates generally to health and medical field. More particularly, the present disclosure provides a system to facilitate detecting and analyzing movement deviation related to gait, but not limited to the likes.
[0013] An aspect of the present disclosure pertains to a system to facilitate detecting and analyzing movement deviation. The system may comprise a flexible device adapted to be worn by an entity at a pre-determined part of the entity. The flexible device may comprise a first set of sensors, a second set of sensors, a third set of sensors, an input interface, and a controller. The first set of sensors may be configured at a pre-determined position of the flexible device and configured to sense force exerted by the pre-determined part of the entity on the flexible device upon movement and correspondingly generate a first set of signals. The second set of sensors may be configured to sense posture and movement of the pre-determined part of the entity and correspondingly generate a second set of signals and the third set of sensors may be configured to sense one or more muscles response of the pre-determined part of the entity based on the movement of the one or more muscles and correspondingly generate a third set of signals. The input interface may be configured to receive one or more input credentials from the entity and correspondingly generate a set of input signals. The controller may be operatively coupled to the first set of sensors, the second set of sensors, the third set of sensors, and the input interface. The controller may include one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors. The controller may be configured to receive the first set of signals, the second set of signals, the third set of signals and the set of input signals, extract a pressure and force value from the first set of signals, an acceleration value from the second set of signals , neuromuscular deviation from the third set of signals. The controller may be configured to update and train a training and testing dataset based on the extracted pressure and force value , the acceleration value , electrical signals and the received set of input signals, where the pressure and force value, the acceleration value, and the neuromuscular deviation may pertain to movement parameters. The controller may be configured to analyze the training and testing dataset. The controller may be configured to classify the analyzed training and training dataset into a first section and a second section, where the first section can pertains to deviated movement parameters based on the analyzed training and testing dataset, and where the second section may pertain to non deviated movement parameters based on the analyzed training and testing dataset, and enables detecting and analyzing the deviation in movement. The controller may be configured to transmit a set of alert signals to an alert unit when the classified training and testing dataset is in the first section, where the set of alert signals may facilitate alerting the entity for deviation in movement and enables detection of the deviation in movement.
[0014] In an aspect, the system may include an alert unit operatively coupled to the controller, where the alert unit may be configured to receive the set of alert signals and activated based on the received set of alert signals, and where the alert unit may include any or a combination of light emitting diode, alarm, buzzer, and vibrator.
[0015] In an aspect, the first set of sensors may include any or a combination of pressure sensor, and force sensor, and where the second set of sensors may include any or a combination of accelerometer, and gyroscope, and where the third set of sensors may include any or a combination of pulse sensor, and electromyography sensor.
[0016] In an aspect, the controller may be in communication with one or more mobile computing device through a communication module, where the one or more mobile computing devices may be configured to receive the set of alert signals and enables analyzing the deviation of movement associated with the entity.
[0017] In an aspect, the input interface may include any or a combination of display, and screen.
[0018] In an aspect, the input interface may be configured to receive one or more input credentials from the entity, where the one or more input credentials may include any or a combination of age, gender, family history, head injury history, stoke history, and neuromuscular disease history.
[0019] In an aspect, the pre-determined part associated with the entity may include any or a combination of foot, leg, hand, arm, thigh, and calf.
[0020] In an aspect, the flexible device may include any or a combination of splint, foot drop splint, belt, and brace.

BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0022] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0023] FIG. 1 illustrates a block diagram of proposed system to facilitate detecting and analyzing movement deviation, in accordance with an embodiment of the present disclosure.
[0024] FIG. 2 illustrates exemplary functional components of a controller of the proposed system to facilitate detecting and analyzing movement deviation aerial system, in accordance with an embodiment of the present disclosure.
[0025] FIG. 3 illustrates an exemplary view of the proposed system to facilitate detecting and analyzing movement deviation, in accordance with an embodiment of the present disclosure.

DETAIL DESCRIPTION
[0026] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0027] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.
[0028] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] While embodiments of the present invention have been illustrated and described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claim.
[0031] The present disclosure relates generally to health and medical field. More particularly, the present disclosure provides a system to facilitate detecting and analyzing movement deviation related to gait, but not limited to the likes.
[0032] FIG. 1 illustrates a block diagram of proposed system to facilitate detecting and analyzing movement deviation, in accordance with an embodiment of the present disclosure.
[0033] As illustrated in FIG. 1, the proposed system (100) (also referred to as system (100), herein) can include a flexible device (102), where the flexible device (102) can include a first set of sensors (104), a second set of sensors (106), and a third set of sensors (108). The system (100) can include an input interface (110), and a controller (112). In an illustrative embodiment, the system (100) can facilitate detecting and analyzing movement deviation, and enable alerting an entity for the movement deviation. In another embodiment, the first set of sensors (104), the second set of sensors (106), the third set of sensors (108), and the input display (110) can be operatively coupled to the controller (112).
[0034] In an embodiment, the flexible device (102) can be adapted to be worn by the entity at a pre-determined part of the entity. In an illustrative embodiment, the flexible device (102) can include any or a combination of splint, foot drop splint, belt, brace, and the likes. In another illustrative embodiment, the pre-determined part associated with the entity can include any or a combination of foot, leg, hand, arm, thigh, calf, and the likes.
[0035] In an embodiment, the first set of sensors (104) can be configured at a pre-determined position of the flexible device (102) and configured to sense force exerted by the pre-determined part of the entity on the flexible device (102) upon movement and correspondingly generate a first set of signals. In an illustrative embodiment, the first set of sensors (104) can include any or a combination of pressure sensor, force sensor, piezoelectric sensor, and the likes. In another illustrative embodiment, the generated first set of signals can be in electrical form, where the first set of signals can be transmitted to the controller (112).
[0036] In an embodiment, the second set of sensors (106) can be configured to sense posture and movement of the pre-determined part of the entity and correspondingly generate a second set of signals. In an illustrative embodiment, the second set of sensors (106) can include any or a combination of accelerometer, gyroscope, and the likes. In another illustrative embodiment, the generated second set of signals can be in electrical form, where the second set of signals in electrical form can be transmitted to the controller (112).
[0037] In an embodiment, the third set of sensors (108) can be configured to sense one or more muscles response of the pre-determined part of the entity based on the movement of the one or more muscles and correspondingly generate a third set of signals. In an illustrative embodiment, the third set of sensors (108) can include any or a combination of pulse sensor, electromyography sensor, and the likes. In another illustrative embodiment, the generated third set of signals can be in electrical form, where the third set of signals in electrical form can be transmitted to the controller (112).
[0038] In an embodiment, the input interface (110) can be configured to receive one or more input credentials from the entity and correspondingly generate a set of input signals. In an illustrative embodiment, the input interface (110) can include any or a combination of screen with key, display with key, and the likes, where the key enables entering the one or more input credentials. In another illustrative embodiment, the one or more input credentials can include any or a combination of age, gender, family history, head injury history, stoke history, neuromuscular disease history, but not limited to the likes. In yet another illustrative embodiment, the set of input signals generated by the input interface (110) can be in electrical form, where the set of input signals can be transmitted to the controller (112).
[0039] In an embodiment, the controller (112) can include one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors. The controller (1120 can be configured to receive the first set of signals, the second set of signals, the third set of signals and the set of input signals in electrical form. In another embodiment, the controller (112) can be configured to extract a pressure and force value from the first set of signals, an acceleration value from the second set of signals, neuromuscular deviation from the third set of signals. The controller (112) can be configured to update and train a training and testing dataset based on the extracted pressure and force value, the acceleration value , neuromuscular deviation and the received set of input signals, where the pressure and force value, the acceleration value, and the neuromuscular deviation can pertain to movement parameters.
[0040] In an embodiment, the controller (112) can be configured to analyze the training and testing dataset, classify the analyzed training and training dataset into a first section and a second section. In another embodiment, the first section can pertain to deviated movement parameters based on the analyzed training and testing dataset, and where the second section can pertain to non deviated movement parameters based on the analyzed training and testing dataset, and where the classified training and testing enables detecting and analyzing the deviation in movement. In yet another embodiment, the controller (112) can be configured to transmit a set of alert signals to an alert unit when the classified training and testing dataset is in the first section, where the set of alert signals can facilitate alerting the entity for deviation in movement and enables detection of the deviation in movement. In an illustrative embodiment, the controller (112) can be any or a combination of microprocessor, microcontroller, Arduino Uno, At mega 328, Bluetooth controller, other similar processing unit, and the likes.
[0041] In an illustrative embodiment, the system (100) can include an alert unit (114) operatively coupled to the controller (112), where the alert unit (114) can be configured to receive the set of alert signals and activated based on the received set of alert signals. In another illustrative embodiment, the alert unit (112) can include any or a combination of light emitting diode, alarm, buzzer, vibrator, and the likes.
[0042] In an illustrative embodiment, the controller (112) can be in communication with one or more mobile computing device through a communication module, where the one or more mobile computing devices can be configured to receive the set of alert signals and enables in analyzing the deviation of movement associated with the entity. In another illustrative embodiment, the one or more mobile computing devices can include any or a combination of cell phone, portable digital hand held device, laptop, digital assistant, and the likes. In another illustrative embodiment, the communication module can include any or a combination of Wireless Fidelity (Wi-Fi) module , Bluetooth module, Li-Fi module, optical fiber, Wireless Local Area Network (WLAN), ZigBee module, and the likes.
[0043] In an illustrative embodiment, the system (100) can include a power source operatively coupled to the input interface (110), and the controller (112), where the power source can facilitate providing electric power to the input interface (110), and the controller (112), and where the power source includes any or a combination of cell, battery, capacitor bank, inductor, electric power line, and the likes.
[0044] In an illustrative embodiment, the system (100) can enable detecting and analyzing movement deviation associated with gait, but not limited to the likes. In another illustrative embodiment, the system (100) can facilitate detecting disorder related to movement deviation like cerebral palsy, and the likes in early stage.
[0045] FIG. 2 illustrates exemplary functional components of a controller of the proposed system to facilitate detecting and analyzing movement deviation aerial system, in accordance with an embodiment of the present disclosure.
[0046] As illustrated in an embodiment, the controller (112) can include one or more processor(s) (202). The one or more processor(s) (202) can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) are configured to fetch and execute computer-readable instructions stored in a memory (204) of the controller (112). The memory (204) can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0047] In an embodiment, the controller (112) can also include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the controller (112) with various devices coupled to the controller (112). The interface(s) (206) may also provide a communication pathway for one or more components of controller (112). Examples of such components include, but are not limited to, processing engine(s) (208) and database (210).
[0048] In an embodiment, the processing engine(s) (208) can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the controller (112) can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to controller (112) and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry. A database (210) can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (208).
[0049] In an embodiment, the processing engine(s) (208) can include an extraction unit (212), updating and training unit (214), analyzing unit (216), classification unit (218), a signal generation unit (220), and other unit(s) (222). The other unit(s) (222) can implement functionalities that supplement applications or functions performed by the system 100 or the processing engine(s) (208).
[0050] The database (210) can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (208).
[0051] It would be appreciated that units being described are only exemplary units and any other unit or sub-unit may be included as part of the system (100). These units too may be merged or divided into super- units or sub-units as may be configured.
[0052] As illustrated in FIG. 2, the controller (112) can be configured to receive a first set of signals from a first set of sensors (104), a second set of signals from a second set of sensors (106), a third set of signals from a third set of sensors (108), and a set of input signals from an input interface (110) in electrical form. In an embodiment, the controller (112) can be configured to extract a pressure and force value from the first set of signals, an acceleration value from the second set of signals, and neuromuscular deviation from the third set of signals with help of the extraction unit (212). In another embodiment, the controller (112) can be configured to update and train a training and testing dataset based on the extracted pressure and force value, the acceleration value , the neuromuscular deviation and the received set of input signals with help of the updating and training unit (214), where the pressure and force value, the acceleration value, and the neuromuscular deviation can pertain to movement parameters.
[0053] In an embodiment, the controller (112) can be configured to analyze the training and testing dataset with help of the analyzing unit (216). In another embodiment, the controller (112) can be configured to classify the analyzed training and training dataset into a first section and a second section, with help of the analyzing unit (216). The first section can pertain to deviated movement parameters based on the analyzed training and testing dataset, and where the second section can pertains to non deviated movement parameters based on the analyzed training and testing dataset, where the analyzed training and testing dataset can enable detecting and analyzing the deviation in movement.
[0054] In an embodiment, the controller (112) can be configured to transmit a set of alert signals to an alert unit with help of the signal generation unit (218), when the classified training and testing dataset is in the first section, where the set of alert signals can facilitate alerting the entity for deviation in movement and enables detection of the deviation in movement. In an illustrative embodiment, the first set of sensors (104) can include any or a combination of pressure sensor, force sensor, piezoelectric sensor, and the likes, the second set of sensors (106) can include any or a combination of accelerometer, gyroscope, and the likes, and where the third set of sensors (108) can include any or a combination of pulse sensor, electromyography sensor, and the likes.
[0055] In an illustrative embodiment, the extraction unit (212) can be configured to receive the first set of signals, the second set of signals and the third set of signals in electrical form, where the extraction unit (212) can be configured to extract the pressure and force value , the acceleration value , and the neuromuscular deviation in machine readable form or binary form, where the extracted pressure and force value , the acceleration value , the neuromuscular deviation, and the received set of input signals can be transmitted to the updating and training unit (214).
[0056] In an illustrative embodiment, the updating and training unit (214) can be configured to receive the extracted pressure and force value, the acceleration value, and the neuromuscular deviation from the extraction unit (212) and the set of input signals in machine readable form or binary form and update and train the training and testing dataset based on the extracted pressure and force value , the acceleration value , and the neuromuscular deviation and the received set of input signals. In another illustrative embodiment, the updated and trained training and testing dataset can be transmitted to the analyzing unit (216).
[0057] In an illustrative embodiment, the training and testing dataset can be trained and updated based on the received set of input signals, pressure and force value, the acceleration value, and the neuromuscular deviation. In another illustrative embodiment, a deep leaning model can be trained based on the received set of input signals, pressure and force value, the acceleration value , and the neuromuscular deviation, where the deep leaning model can be stored in the database (210). In another illustrative embodiment, once the training and testing dataset is trained correctly, a deep learning algorithm can be configured perform repetitive, and routine tasks within a shorter period of time
[0058] In an illustrative embodiment, the analyzing unit (216) can be configured to receive the updated and trained training and testing dataset in machine readable form or binary form, where the analyzing unit (216) can be configured to analyze the updated and trained training and testing dataset, with help of deep learning algorithms, where analyzed training and testing dataset can be transmitted to the classification unit (218).
[0059] In an illustrative embodiment, the classification unit (218) can be configured to receive the analyzed training and testing dataset in machine readable form or binary form, where the classification unit (218) can be configured to classify the analyzed training and training dataset into a first section and a second section, where the first section can pertain to deviated movement parameters based on the analyzed training and testing dataset, and where the second section can pertain to non deviated movement parameters based on the analyzed training and testing dataset, where the classified first section and the second section can enable detecting and analyzing the deviation in movement.
[0060] In an illustrative embodiment, the classification unit (218) can include deep learning based classifiers, where the deep learning based classifiers can facilitate distinguishing an entity with movement deviation and without movement deviation. In an illustrative embodiment, the deep learning classifiers can enable classification of first section and the second section.
[0061] In an illustrative embodiment, the signal generation unit (220) can be configured to receive the classified first section and the second section in machine readable form or in form of deep learning algorithm model, where the signal generation unit (220) can be configured to transmit the set of alert signals to the alert unit when the classified training and testing dataset is in the first section, where the set of alert signals can facilitate alerting the entity for deviation in movement and enables detection of the deviation in movement.
[0062] FIG. 3 illustrates an exemplary view of the proposed system to facilitate detecting and analyzing movement deviation, in accordance with an embodiment of the present disclosure.
[0063] As illustrated in FIG. 3, the proposed system (100) can include a flexible device (102), a first set of sensors (104), a second set of sensors (106), and a third set of sensors (108). The system (100) can include an input interface (110), and a controller (112). In an embodiment, the controller (112) can be operatively coupled to the first set of sensors (104), the second set of sensors (106), the third set of sensors (108), and the input interface (110). In another embodiment, the system (100) can enable detecting and analyzing movement deviation associated with gait, but not limited to the likes. In another illustrative embodiment, the system (100) can facilitate detecting disorder related to movement deviation like cerebral palsy, and the likes in early stage.
[0064] In an illustrative embodiment, the first set of sensors (104) can include any or a combination of pressure sensor, force sensor, piezoelectric sensor, and the likes, and where the second set of sensors (106) can include any or a combination of accelerometer, gyroscope, and the likes, and where the third set of sensors (108) can include any or a combination of pulse sensor, electromyography sensor, and the likes.
[0065] In an illustrative embodiment, the flexible device (102) can be adapted to be worn by the entity at a pre-determined part of the entity , the flexible device (102) can include the first set of sensors (104) configured at a pre-determined position of the flexible device (102) and configured to sense force exerted by the pre-determined part of the entity on the flexible device (102) upon movement and correspondingly generate a first set of signals. In another illustrative embodiment, the second set of sensors (106) can be configured to sense posture and movement of the pre-determined part of the entity and correspondingly generate a second set of signals, and the third set of sensors (108) can be configured to sense one or more muscles response of the pre-determined part of the entity based on the movement of the one or more muscles and correspondingly generate a third set of signals.
[0066] In an illustrative embodiment, the input interface (110) can be configured to receive one or more input credentials from the entity and correspondingly generate a set of input signals. In another illustrative embodiment, the input interface (110) can include any or a combination of display with key, screen with key, where the key can enable entering the one or more input credentials. In yet another illustrative embodiment, the one or more input credentials can include any or a combination of age, gender, family history, head injury history, stoke history, neuromuscular disease history, but not limited to the likes.
[0067] In an illustrative embodiment, the pressure sensor (104) can be configured to detect the pressure or force value upon movement of an entity or when the entity moves. In another illustrative embodiment, the accelerometer (106-1) can be configured to analyze the gait, and the gyroscope sensor (106-2) can be configured to sense analyze motion and posture of feet or legs of the entity by measuring angular rate. In another illustrative embodiment, the electromyography sensor can be configured to measure electrical activity of one or more muscles of the entity and pulse sensor can be configured to measure pulse rate.
[0068] In an illustrative embodiment, the controller (112) can be configured to receive the first set of signals, the second set of signals, the third set of signals and the set of input signals, extract a pressure and force value, an acceleration value, a neuromuscular deviation and classify the entity with movement deviation related to disorder and without the movement deviation related disorder. In another illustrative embodiment, a cloud server (302) can be configured with the system (100), where the cloud server (302) can be configured to stores the extracted pressure and force value , the acceleration value , the neuromuscular deviation and the received set of input signals for further analysis. In yet another illustrative embodiment, storage unit (210) can be configured with the controller (112), where the storage unit (210) can facilitate training deep learning model by the extracted pressure and force value, the acceleration value , the neuromuscular deviation and the received set of input signals.
[0069] In an illustrative embodiment, the controller (112) can be in communication with the flexible device (102) and the input interface (110) through a communication module, where the communication module can include any or a combination of Wireless Fidelity (Wi-Fi) module, Bluetooth module, Li-Fi module, optical fiber, Wireless Local Area Network (WLAN), and ZigBee module and the likes. In another illustrative embodiment, the controller (112) can be in in communication with one or more mobile computing device through a communication module, where the one or more mobile computing devices can be configured to receive the set of alert signals and enables analyzing the deviation of movement associated with the entity.
[0070] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0071] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, ` components, or steps that are not expressly referenced.
[0072] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0073] The present disclosure provides a system that helps in detecting gait related disorder like cerebral palsy and the likes with help of deep learning algorithm, where the deep learning algorithm classifiers enables distinguishing a normal healthy person from a person affected with disorder.
[0074] The present disclosure provides a system that facilitate providing aid to parents for analyzing gait of children and assessing the children on basis of gait.
[0075] The present disclosure provides a system that enables correct diagnosis of disease related to gait and help in ruling out other disorders thereby avoiding unnecessary lab tests.
[0076] The present disclosure provides a system that is economical and can be used on larger scale.
[0077] The present disclosure provides a system that helps in avoiding expensive surgery, medicinal treatment and other means for treating disorder related to gait thereby saving money.