A DIAGNOSTIC SYSTEM FOR DIFFERENTIATING TREMORS AND A METHOD THEREOF

Abstract

ABSTRACT A DIAGNOSTIC SYSTEM FOR DIFFERENTIATING TREMORS AND A METHOD THEREOF The present disclosure discloses a diagnostic system (100) for the differentiation of tremors and a method (200) thereof configured for using advanced sensor technologies. The system (100) incorporates a Time-of-Flight (TOF) sensor (102) to measure spatial displacement, frequency, amplitude, and trajectory of tremor-associated movements. Additionally, a plurality of Inertial Measurement Unit (IMU) sensors (104) is integrated into a wearable device (106) to capture multidirectional hand movements, including acceleration and angular velocity. A data processing unit (108) receives and processes the sensor data through an analysis module (110) that differentiates tremor characteristics among rest tremors, essential tremors, and cerebellar tremors. A synchronization module (114) coordinates real-time data from both sensors to enhance diagnostic accuracy. This integrated approach enables simultaneous data capture, offering a comprehensive analysis of various tremor types and facilitating improved diagnosis and monitoring of tremor-related conditions in clinical settings.

Specification

Description:FIELD
The present disclosure relates to a healthcare domain. More particularly focuses on differentiating tremors.
DEFINITION
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicates otherwise.
Tremor: The term "tremor" refers to an involuntary, rhythmic shaking or trembling of a body part, often occurring in the hands or arms. It can be caused by various neurological conditions and can affect daily activities.
Inertial Measurement Unit (IMU): The term "inertial measurement unit" (IMU) refers to a device that combines accelerometers and gyroscopes to measure acceleration and angular velocity. IMUs are used to capture motion in multiple directions and are essential for analyzing movement patterns.
Time-of-Flight (TOF) Sensor: The term "time-of-flight sensor" (TOF) refers to a device that measures the time it takes for a light pulse, usually infrared, to travel to an object and return. This measurement enables the calculation of distance and is useful for detecting spatial displacement.
Essential Tremor (ET): The term "essential tremor" (ET) refers to a common neurological disorder characterized by involuntary shaking, primarily affecting the hands and arms during voluntary movements. It is distinct from other types of tremors, such as those seen in Parkinson's disease.
Cerebellar Tremor: The term "cerebellar tremor" refers to a type of tremor resulting from dysfunction in the cerebellum, the area of the brain responsible for coordination and balance. This tremor is typically evident during intentional movements.
Rest Tremor: The term "rest tremor" refers to a type of tremor that occurs when a body part is at rest and not engaged in any voluntary movement. It is commonly associated with conditions like Parkinson's disease.
Machine Learning Module: The term "machine learning module" refers to a component of a diagnostic system that uses techniques to analyze data, learn from it, and improve its performance over time. In the context of tremor analysis, it can classify different types of tremors based on input data.
Synchronization Module: The term "synchronization module" refers to a component that coordinates the data collection from various sensors in real-time, ensuring that the information is accurate and useful for analysis.
Diagnostic Report Generation Module: The term "diagnostic report generation module" refers to a system component that automatically compiles and formats diagnostic information into reports for healthcare professionals, aiding in patient monitoring and treatment decisions.
Pose Estimation Module: The term "pose estimation module" refers to a system component that tracks and analyzes a subject's body posture and movements, often using computer vision technology, to assess conditions like tremors or movement disorders.
The above definitions are in addition to those expressed in the art.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
Movement disorders, including various types of tremors, are a significant challenge in clinical diagnostics and patient monitoring. These disorders often require continuous observation and analysis of motor function to accurately diagnose and treat underlying conditions. Tremors, which may occur during rest, action, or postural movements, can vary significantly in frequency, amplitude, and type, complicating the diagnostic process.
Traditional methods for assessing tremors typically involve manual observation by healthcare professionals or the use of simple mechanical devices, both of which can be subject to inaccuracies and variability. Furthermore, current solutions are often limited to static environments or do not account for the dynamic nature of the patient's movements in various postures. As a result, there is a growing need for systems that can provide real-time, objective, and reliable data on tremor characteristics across a range of conditions and body postures.
Therefore, there is a need for a diagnostic system for differentiating tremors and a method thereof that alleviates the aforementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a diagnostic system for differentiating tremors.
Another object of the present disclosure is to provide a system that facilitates the accurate monitoring and analysis of movement disorders.
Still another object of the present disclosure is to provide a system that enables real-time tracking of motor functions without reliance on subjective observation.
Yet another object of the present disclosure is to provide a system that provides a solution capable of analyzing motor functions across different postures and conditions.
Still another object of the present disclosure is to provide a system that reduces the variability and error commonly associated with traditional tremor assessment methods.
Yet another object of the present disclosure is to provide a system that offers a system that can be used both in clinical settings and for long-term monitoring of patients.
Still another object of the present disclosure is to provide a system that improves early diagnosis and tracking of movement disorder progression, enabling timely medical interventions.
Yet another object of the present disclosure is to provide a system that enhances the efficiency and accuracy of tremor-related assessments.
Yet another object of the present disclosure is to provide a method for differentiating tremors.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure provides a diagnostic system for differentiating tremor, comprising: a Time-of-Flight (TOF) sensor, a plurality of Inertial Measurement Unit (IMU) sensors, a data processing unit, and a synchronization module.
The Time-of-Flight (TOF) sensor is configured to detect spatial displacement of a subject's body parts over time, for measuring the frequency, amplitude, and trajectory of movements associated with tremors.
The plurality of Inertial Measurement Unit (IMU) sensors, integrated into a wearable device attached to the subject's hands, is configured to capture multidirectional hand movements, including acceleration, angular velocity, and orientation.
The data processing unit is configured to receive and process data from the TOF sensor and IMU sensors, wherein the data processing unit includes: an analysis module.
The analysis module is configured to compute and differentiate tremor characteristics, including frequency, amplitude, and movement patterns of rest tremors, essential tremors, and cerebellar tremors.
The synchronization module is configured to coordinate data from the TOF sensor and the IMU sensors in real-time to improve tremor detection accuracy.
The TOF sensor and the IMU sensors are configured to function simultaneously, capturing complementary data sets to provide a comprehensive analysis of different tremor types.
In an embodiment, the IMU sensors are embedded within the wearable device worn by the subject, such that the IMU sensors capture precise hand movements in multiple axes, for facilitating the detection of fine tremor characteristics in all directions of motion.
In an embodiment, the system further comprises a machine learning module within the data processing unit, wherein the machine learning module is trained to classify tremor types based on the processed data, enabling improved differentiation between sporadic rest tremors and continuous action tremors.
In an embodiment, the machine learning module is configured to adaptively update its classification model based on real-time data inputs from the TOF sensor and the IMU sensors, enhancing diagnostic precision over time.
In an embodiment, the synchronization module integrates data from the TOF sensor and IMU sensors under varying postural conditions, including standing, sitting, and during dynamic activities, thereby facilitating continuous tremor monitoring in real-world environments.
In an embodiment, the system further comprises a visualization module configured to display tremor analysis results on a graphical interface, enabling healthcare professionals to view tremor frequency, amplitude, and classification in real-time.
In an embodiment, the TOF sensor is configured to operate at a range of up to 50 cm, providing high-resolution depth data that complements the IMU sensor data for enhanced spatial tremor analysis.
In an embodiment, the system further comprises a feedback module configured to generate diagnostic feedback in the form of alerts or notifications based on the classification of tremor severity and progression, assisting healthcare professionals in tracking disease stages.
In an embodiment, the synchronization module is configured to adjust data acquisition rates from the TOF sensor and IMU sensors based on the subject's movement state, optimizing data collection efficiency and reducing power consumption.
In an embodiment, the system further comprises a diagnostic report generation module that is configured to automatically generate detailed diagnostic reports based on the captured and processed tremor data, aiding in long-term patient monitoring.
The present disclosure provides a method for differentiating tremor, the method comprising:
• capturing, by a TOF sensor, spatial displacement data detecting movement trajectories and distances associated with tremors;
• capturing, by IMU sensors, multidirectional movement data embedded in a wearable device to measure hand movements in multiple axes;
• processing, by a data processing unit, the captured data, the processing including:
o analysis, by an analysis module, tremor characteristics to classify the tremors as rest tremors, essential tremors, or cerebellar tremors;
• synchronizing, by a synchronization module, the data from the TOF sensor and IMU sensors in real-time; and
• providing, by a visualization module, a real-time display of the results on a graphical interface.
In an embodiment, the method includes the machine learning module and continuously improves its classification accuracy by updating its models based on real-time data inputs from the TOF sensor and IMU sensors.
In an embodiment, the method includes the system further comprises a pose estimation module based on the CVZone library and Mediapipe framework, configured to track the subject's body posture and estimate tremor amplitudes based on critical body points, including face mesh, body mesh, and finger positions.
In an embodiment, the method includes the tremor differentiation categorizing tremors based on their frequency range, amplitude, and occurrence patterns, thereby distinguishing between early-stage and advanced-stage tremors in subjects diagnosed with Parkinson's disease.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A diagnostic system for differentiating tremors and a method thereof, of the present disclosure will now be described with the help of the accompanying drawing in which:
Figure 1 illustrates a block diagram of a diagnostic system for differentiating tremors in accordance with the present disclosure;
Figures 2A and 2B illustrate a flowchart of a method for differentiating tremors in accordance with the present disclosure;
Figure 3 depicts an experimental setup of the diagnostic system for differentiating tremors in accordance with the present disclosure; and
Figure 4 illustrates a flow chart of the logic associated the diagnostic system for differentiating tremors, in accordance with the present disclosure.
LIST OF REFERENCE NUMERALS
100 System
102 Time-of-Flight (TOF) Sensor
104 Inertial Measurement Unit (IMU) Sensors
106 Wearable Device
108 Data Processing Unit
110 Analysis Technique
114 Synchronization Module
116 Machine Learning Module
118 Visualization Module
120 Pose Estimation Module
122 Feedback Module
124 Diagnostic Report Generation Module
200-212 Method and Methos steps

DETAILED DESCRIPTION
The present disclosure relates generally to diagnostic systems and methods in the field of medical technology. More specifically, it pertains to systems used for monitoring and analyzing human motor functions, particularly in relation to movement disorders and tremor-related conditions.
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," "including," and "having," are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof.
When an element is referred to as being "engaged to," "connected to," or "coupled to" another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
Therefore, the present disclosure envisages a diagnostic system for differentiating tremor and a method thereof (hereinafter referred to as system (100) and method (200)). The present disclosure is explained in Figure 1- Figure 8.
Referring to Figure 1, The present disclosure provides a diagnostic system (100) for differentiating tremor, comprising: a Time-of-Flight (TOF) sensor (102), a plurality of Inertial Measurement Unit (IMU) sensors (104), a data processing unit (108), an analysis module (110), and a synchronization module (114).
The Time-of-Flight (TOF) sensor (102) is configured to detect spatial displacement of a subject's body parts over time, for measuring the frequency, amplitude, and trajectory of movements associated with tremors;
The plurality of Inertial Measurement Unit (IMU) sensors (104), integrated into a wearable device (106) attached to the subject's hands, configured to capture multidirectional hand movements, including acceleration, angular velocity, and orientation;
The data processing unit (108) is configured to receive and process data from the TOF sensor (102) and IMU sensors (104), wherein the data processing unit (108) includes:
The analysis module (110) is configured to compute and differentiate tremor characteristics, including frequency, amplitude, and movement patterns of rest tremors, essential tremors, and cerebellar tremors;
The synchronization module (114) is configured to coordinate data from the TOF sensor (102) and the IMU sensors (104) in real-time to improve tremor detection accuracy; and
The TOF sensor (102) and the IMU sensors (104) are configured to function simultaneously, capturing complementary data sets to provide a comprehensive analysis of different tremor types.
In an embodiment, the IMU sensors (104) are embedded within the wearable device (106) worn by the subject, such that the IMU sensors (104) capture precise hand movements in multiple axes, for facilitating the detection of fine tremor characteristics in all directions of motion.
In an embodiment, the system (100) further comprises a machine learning module (116) within the data processing unit (108), wherein the machine learning module (116) is trained to classify tremor types based on the processed data, enabling improved differentiation between sporadic rest tremors and continuous action tremors.
In an embodiment, the machine learning module (116) is configured to adaptively update its classification model based on real-time data inputs from the TOF sensor (102) and the IMU sensors (104), enhancing diagnostic precision over time.
In an embodiment, the synchronization module (114) integrates data from the TOF sensor (102) and IMU sensors (104) under varying postural conditions, including standing, sitting, and during dynamic activities, thereby facilitating continuous tremor monitoring in real-world environments.
In an embodiment, the system (100) further comprises a visualization module (118) configured to display tremor analysis results on a graphical interface, enabling healthcare professionals to view tremor frequency, amplitude, and classification in real time.
In an embodiment, the TOF sensor (102) is configured to operate at a range of up to 50 cm, providing high-resolution depth data that complements the IMU sensor data for enhanced spatial tremor analysis.
In an embodiment, the system (100) further comprises a feedback module (122) configured to generate diagnostic feedback in the form of alerts or notifications based on the classification of tremor severity and progression, assisting healthcare professionals in tracking disease stages.
In an embodiment, the synchronization module (114) is configured to adjust data acquisition rates from the TOF sensor (102) and IMU sensors (104) based on the subject's movement state, optimizing data collection efficiency and reducing power consumption.
In an embodiment, the system (100) further comprises a diagnostic report generation module (124) configured to automatically generate detailed diagnostic reports based on the captured and processed tremor data, aiding in long-term patient monitoring.
Figures 2A and 2B illustrate a flowchart that includes the steps involved in a method (200) for differentiating tremor and a method thereof, in accordance with an embodiment of the present disclosure. The order in which method (200) is described is not intended to be construed as a limitation, and any number of the described method (200) steps may be combined in any order to implement method (200), or an alternative method. Furthermore, method (200) may be implemented by processing resource or electronic device(s) through any suitable hardware, non-transitory machine-readable medium/instructions, or a combination thereof. The method (200) comprises the following steps:
At step (202), the method (200), includes capturing, by a TOF sensor (102), spatial displacement data detecting movement trajectories and distances associated with tremors.
At step (204), the method (200), includes capturing, by IMU sensors (104), multidirectional movement data embedded in a wearable device (106) to measure hand movements in multiple axes.
At step (206), the method (200), includes processing, by a data processing unit (108), the captured data.
At step (208), the method (200), includes analysis, by an analysis module (110), tremor characteristics to classify the tremors as rest tremors, essential tremors, or cerebellar tremors.
At step (210), the method (200), includes synchronizing, by a synchronization module (114), the data from the TOF sensor (102) and IMU sensors (104) in real-time.
At step (212), the method (200), includes providing, by a visualization module (118), a real-time display of the results on a graphical interface.
In an embodiment, the method (200) includes the machine learning module (116) and continuously improves its classification accuracy by updating its models based on real-time data inputs from the TOF sensor (102) and IMU sensors (104).
In an embodiment, the method (200) includes the system (100) further comprises a pose estimation module (120) based on the CVZone library and Mediapipe framework, configured to track the subject's body posture and estimate tremor amplitudes based on critical body points, including face mesh, body mesh, and finger positions.
In an embodiment, the method (200) includes the tremor differentiation categorizing tremors based on their frequency range, amplitude, and occurrence patterns, thereby distinguishing between early-stage and advanced-stage tremors in subjects diagnosed with Parkinson's disease.
Figure 3 describes a diagnostic setup of the diagnostic system (100) for tremor detection that integrates computer vision and IMU sensors. The top section (Figure 1) shows the computer vision experimental setup (102), where a camera (104) is mounted on a tripod at a height of 140 cm and positioned 150 cm from a stool holding an object (106). The camera is angled at 30 degrees to capture movements between the object (106) and a chair (108) placed 50 cm away, configured to monitor motion accurately. The bottom part of the figure depicts a hand (110) equipped with IMU sensors (112), which are strategically attached to different parts of the hand to capture multidirectional hand movements for tremor detection. The flowchart on the right demonstrates the integration of the data from both the computer vision setup (102) and the IMU sensor setup (114), which is then processed for diagnosis (116) of tremors or movement disorders. This comprehensive system enables precise monitoring and analysis of hand tremors.
Figure 4 shows the flowchart of the process for diagnosing and differentiating between types of tremors based on video analysis and poses. The process starts with recording a video for 10 seconds, which is then analysed to determine the subject's pose and hand movements. The first decision point evaluates whether the subject's pose corresponds to essential tremor (ET). If the pose matches ET and the tremor intensity is greater than zero, the system outputs tremor details. If the pose is not ET, the process continues to check if the pose corresponds to cerebellar tremor (CT). Similarly, if the subject is in the CT pose and the tremor intensity is greater than zero, the tremor details are output. If neither ET nor CT is detected, the system checks if the subject's pose corresponds to rest tremor (RT). If the RT pose is identified and tremor intensity is greater than zero, the tremor details are output. Throughout the flowchart, the system systematically evaluates poses and tremor intensity, outputting relevant tremor details at each appropriate step. If no tremors are detected for any pose, the process loops back to start and repeat the analysis.
In an operative configuration, the diagnostic system (100) for differentiating tremors is configured to operate seamlessly by integrating multiple advanced sensor technologies. At its core, the Time-of-Flight (TOF) sensor (102) detects spatial displacement, measuring the frequency, amplitude, and trajectory of tremor movements. In conjunction, the Inertial Measurement Unit (IMU) sensors (104) are strategically positioned within a wearable device (106) to monitor multidirectional hand movements, capturing data on acceleration, angular velocity, and orientation. The data processing unit (108) receives and processes information from both the TOF and IMU sensors, employing an analysis technique (110) that differentiates between various types of tremors, including essential tremors, cerebellar tremors, and rest tremors. The synchronization module (114) coordinates data from the two sensor types in real time, ensuring that the data collected is accurate and comprehensive. Additionally, the machine learning module (116) is incorporated to adaptively update its classification model based on real-time data inputs, enhancing diagnostic precision over time.
The advantages of this integrated system are manifold. First, the system (100) provides a high-resolution analysis of tremor dynamics through the complementary data sets captured by the TOF sensor (102) and IMU sensors (104), allowing for a comprehensive assessment of tremor characteristics. Real-time monitoring capabilities facilitate immediate feedback, enabling healthcare professionals to make timely decisions regarding patient care. The visualization module (118) displays the results on an intuitive graphical interface, allowing for easy interpretation of tremor frequency, amplitude, and classification. Furthermore, the feedback module (122) generates alerts or notifications based on tremor severity, assisting clinicians in tracking disease progression effectively. Overall, this diagnostic system significantly improves diagnostic accuracy, enhances patient outcomes in tremor-related conditions, and allows for continuous monitoring of patients in real-world environments.
The functions described herein may be implemented in hardware, executed by a processor, firmware, or any combination thereof. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. The nature of the disclosure, can be implemented by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described hereinabove has several technical advantages including, but not limited to, a diagnostic system for differentiating tremors and a method thereof, which:
• facilitates the accurate monitoring and analysis of movement disorders;
• enables real-time tracking of motor functions without reliance on subjective observation;
• provides a solution capable of analyzing motor functions across different postures and conditions;
• reduces the variability and error commonly associated with traditional tremor assessment methods;
• offers a system that can be used both in clinical settings and for long-term monitoring of patients;
• improves early diagnosis and tracking of movement disorder progression, enabling timely medical interventions; and
• enhances the efficiency and accuracy of tremor-related assessments.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Any discussion of devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:WE CLAIM:
1. A diagnostic system (100) for differentiating tremor, comprising:
• a Time-of-Flight (TOF) sensor (102) configured to detect spatial displacement of a subject's body parts over time, for measuring the frequency, amplitude, and trajectory of movements associated with tremors;
• a plurality of Inertial Measurement Unit (IMU) sensors (104), integrated into a wearable device (106) attached to the subject's hands, configured to capture multidirectional hand movements, including acceleration, angular velocity, and orientation;
• a data processing unit (108) configured to receive and process data from said TOF sensor (102) and IMU sensors (104), wherein said data processing unit (108) includes:
o an analysis module (110) configured to compute and differentiate tremor characteristics, including frequency, amplitude, and movement patterns of rest tremors, essential tremors, and cerebellar tremors;
• a synchronization module (114) configured to coordinate data from said TOF sensor (102) and said IMU sensors (104) in real-time to improve tremor detection accuracy; and
• wherein said TOF sensor (102) and said IMU sensors (104) are configured to function simultaneously, capturing complementary data sets to provide a comprehensive analysis of different tremor types.
2. The system (100) as claimed in claim 1, wherein said IMU sensors (104) are embedded within the wearable device (106) worn by the subject, such that said IMU sensors (104) capture precise hand movements in multiple axes, for facilitating the detection of fine tremor characteristics in all directions of motion.
3. The system (100) as claimed in claim 1, wherein said system (100) further comprises a machine learning module (116) within the data processing unit (108), wherein said machine learning module (116) is trained to classify tremor types based on the processed data, enabling improved differentiation between sporadic rest tremors and continuous action tremors.
4. The system (100) as claimed in claim 3, wherein said machine learning module (116) is configured to adaptively update its classification model based on real-time data inputs from said TOF sensor (102) and said IMU sensors (104), enhancing diagnostic precision over time.
5. The system (100) as claimed in claim 1, wherein said synchronization module (114) integrates data from the TOF sensor (102) and IMU sensors (104) under varying postural conditions, including standing, sitting, and during dynamic activities, thereby facilitating continuous tremor monitoring in real-world environments.
6. The system (100) as claimed in claim 1, wherein said system (100) further comprises a visualization module (118) configured to display tremor analysis results on a graphical interface, enabling healthcare professionals to view tremor frequency, amplitude, and classification in real-time.
7. The system (100) as claimed in claim 1, wherein said TOF sensor (102) is configured to operate at a range of up to 50 cm, providing high-resolution depth data that complements the IMU sensor data for enhanced spatial tremor analysis.
8. The system (100) as claimed in claim 1, wherein said system (100) further comprises a feedback module (122) configured to generate diagnostic feedback in the form of alerts or notifications based on the classification of tremor severity and progression, assisting healthcare professionals in tracking disease stages.
9. The system (100) as claimed in claim 1, wherein the synchronization module (114) is configured to adjust data acquisition rates from the TOF sensor (102) and IMU sensors (104) based on the subject's movement state, optimizing data collection efficiency and reducing power consumption.
10. The system (100) as claimed in claim 1, further comprises a diagnostic report generation module (124) configured to automatically generate detailed diagnostic reports based on the captured and processed tremor data, aiding in long-term patient monitoring.
11. A method (200) for differentiating tremor, said method (200) comprising:
• capturing, by a TOF sensor (102), spatial displacement data detecting movement trajectories and distances associated with tremors;
• capturing, by IMU sensors (104), multidirectional movement data embedded in a wearable device (106) to measure hand movements in multiple axes;
• processing, by a data processing unit (108), the captured data, said processing including:
o analysis, by an analysis module (110), tremor characteristics to classify the tremors as rest tremors, essential tremors, or cerebellar tremors;
• synchronizing, by a synchronization module (114), the data from the TOF sensor (102) and IMU sensors (104) in real-time; and
• providing, by a visualization module (118), a real-time display of the results on a graphical interface.
12. The method (200) as claimed in claim 12, wherein the machine learning module (116) continuously improves its classification accuracy by updating its models based on real-time data inputs from the TOF sensor (102) and IMU sensors (104).
13. The method (200) as claimed in claim 12, wherein said system (100) further comprises a pose estimation module (120) based on the CVZone library and Mediapipe framework, configured to track the subject's body posture and estimate tremor amplitudes based on critical body points, including face mesh, body mesh, and finger positions.
14. The method (200) as claimed in claim 12, wherein said tremor differentiation includes categorizing tremors based on their frequency range, amplitude, and occurrence patterns, thereby distinguishing between early-stage and advanced-stage tremors in subjects diagnosed with Parkinson's disease.
Dated this 01st day of October, 2024

_______________________________
MOHAN RAJKUMAR DEWAN, IN/PA - 25
of R.K. DEWAN & CO.
Authorized Agent of Applicant

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