Description:TITLE: EMG Signal-Controlled Haptic Intelligent Robotic Arm
FIELD OF INVENTION: The present invention relates to the field of assistive robotics and prosthetic technologies. More particularly, the present invention pertains to a haptic robotic arm controlled using Electromyography (EMG) signals for precise finger and hand gesture recognition. The invention is aimed at providing enhanced functionality for individuals requiring prosthetic solutions by integrating machine learning-based gesture classification, real-time feedback, and intuitive control mechanisms.
BACKGROUND OF THE INVENTION:
Electromyography (EMG) signals have been extensively used in controlling prosthetic devices, enabling natural and intuitive control through muscle-generated electrical impulses. Existing solutions rely on limited gesture recognition, offer suboptimal response times, and are often inaccessible due to high costs.
Prior Art:
1. Prior Art 1: A thumb-controlled low-cost robotic arm utilizes servo motors and Arduino for basic movements but lacks advanced gesture recognition and haptic feedback.
2. Prior Art 2: A surface-EMG-based robotic hand provides some degree of freedom but suffers from accuracy limitations due to noise in EMG signals.
3. Prior Art 3: Neural network-driven bionic hand prostheses focus on gesture classification but lack robust real-time performance for multiple gestures.
4. Prior Art 4: EOG-based robotic systems enable control through eye movements but are not directly applicable to muscle-driven prosthetics.
From the prior art descriptions, it is understood that the current solutions fail to provide an affordable, real-time, multi-gesture prosthetic solution with robust signal processing and classification capabilities.

OBJECT OF THE PRESENT INVENTION
Accordingly, the primary objective of the present invention is to provide an

1. To provide an EMG signal-controlled robotic arm with precise and real-time control of multiple hand and finger gestures.
2. To incorporate advanced filtering techniques such as Butterworth filters for reliable EMG signal processing.
3. To integrate machine learning models like CNN and XGBoost for high-accuracy gesture classification.
4. To offer a cost-effective prosthetic solution with robust hardware and software integration.
5. To facilitate intuitive user interaction through real-time feedback and visual monitoring.

SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

The present invention introduces an EMG signal-controlled haptic robotic arm that mimics human hand and finger movements with high precision. The system uses EMG sensors to capture muscle signals, which are processed and classified using advanced machine learning algorithms, including CNN and XGBoost. Filtered and amplified signals are sent to an Arduino Uno microcontroller, which controls servo motors for the robotic arm's movements.
The invention incorporates a Butterworth filter to enhance signal reliability by reducing noise and preserving true physiological signals. The system also features real-time visualization of EMG signals, aiding in diagnosis and user training. This haptic robotic arm represents a significant advancement in prosthetic technologies, providing affordable, natural, and intuitive control.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

The embodiment of the present invention is illustrated with the help of an accompanying drawing.
1. Figure 1: Block diagram of the proposed EMG-controlled robotic arm system.
2. Figure 2: Schematic of the servo motor connection with the EMG sensor.
3. Figure 3: Graphical representation of EMG signals with an envelope detector.

DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING DRAWINGS
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.

System Architecture (Figure 1)
The system includes the following components:
• EMG Sensors: Placed near the ulnar nerve to capture electrical signals from muscle contractions.
• Signal Processing Unit: Amplifies and filters raw EMG signals using a Butterworth filter.
• Arduino Uno Microcontroller: Processes the filtered signals and generates Pulse Width Modulation (PWM) signals for servo motor control.
• Servo Motors: Enable finger and hand movements based on processed signals.
Working Mechanism
1. Signal Acquisition:
EMG sensors capture muscle activity and transmit the signals to the processing unit.
2. Signal Processing:
The Butterworth filter removes noise while preserving the signal's integrity.
3. Gesture Classification:
Machine learning models such as CNN and XGBoost classify the processed signals into predefined gestures.
4. Movement Execution:
The Arduino generates PWM signals to drive the servo motors, replicating human hand movements.
5. Visualization:
The processed signals are visualized in real-time, aiding in diagnosis and training.

While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment 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
I/We Claim
1. A system for controlling a haptic robotic arm using EMG signals, comprising:
o EMG sensors for muscle signal acquisition.
o A signal processing unit utilizing Butterworth filters for noise reduction.
o A microcontroller for processing and gesture classification.
o Servo motors for executing robotic arm movements.
2. The system of claim 1, wherein machine learning models are employed for high-accuracy gesture classification.
3. The system of claim 1, wherein real-time visualization of EMG signals facilitates user interaction and training.
4. The system of claim 1, characterized by its affordability and adaptability for various prosthetic applications.
5. The system of claim 1, wherein the integration of haptic feedback enhances the user experience.

ABSTRACT OF THE INVENTION
Title: EMG Signal-Controlled Haptic Intelligent Robotic Arm
The invention relates to an advanced prosthetic arm controlled by EMG signals, featuring real-time gesture recognition and haptic feedback. The system employs machine learning algorithms, such as CNN and XGBoost, for precise classification of finger and hand gestures. A Butterworth filter ensures signal reliability, while an Arduino microcontroller enables intuitive and natural control. This cost-effective solution significantly enhances the functionality and accessibility of prosthetic technologies.

, Claims:Claims
I/We Claim
1. A system for controlling a haptic robotic arm using EMG signals, comprising:
o EMG sensors for muscle signal acquisition.
o A signal processing unit utilizing Butterworth filters for noise reduction.
o A microcontroller for processing and gesture classification.
o Servo motors for executing robotic arm movements.
2. The system of claim 1, wherein machine learning models are employed for high-accuracy gesture classification.
3. The system of claim 1, wherein real-time visualization of EMG signals facilitates user interaction and training.
4. The system of claim 1, characterized by its affordability and adaptability for various prosthetic applications.
5. The system of claim 1, wherein the integration of haptic feedback enhances the user experience.