Introduction
The Intelligent Prosthetic Arm project represents a significant leap forward in assistive technology. We aim to create a prosthetic arm that seamlessly integrates with the user’s nervous system, responding directly to brain signals (EEG) and providing a level of dexterity and adaptability comparable with a real arm. This project brings together neuroscience, robotics, and computer vision to develop a truly intelligent prosthetic limb. This project is divided in three core sub-projects: a Neuroscience section, aimed at EEG signal processing and decoding, a Computer Vision section, aimed at scene and object understanding, and a Mechatronics section, which involves Robotics and electronics, to create the physical arm, integrating the sensors.
Project Goals and Motivation
Current prosthetic limbs, while functional, often lack the intuitive control and adaptability of natural limbs. Users frequently struggle with complex tasks requiring fine motor skills and responsive interaction with the environment. Our project seeks to address these limitations by directly tapping into the user’s intent through brain signals and providing real-time sensory feedback and environmental awareness through computer vision.
The project’s core objectives are threefold:
EEG Signal Decoding: Develop robust algorithms to accurately translate brainwave patterns, captured via an EEG headset, into motor commands for the prosthetic arm. This involves sophisticated signal processing, noise reduction, and machine learning techniques to classify different intended movements (e.g., grasping, releasing, pointing).
Computer Vision for Environmental Awareness: Implement a computer vision system that allows the prosthetic arm to “see” and understand its surroundings. This includes object detection, recognition, and depth estimation, enabling the arm to adapt its grip and movement based on the specific object and its context.
Mechanical Design and Sensor Integration: Create a multi-degree-of-freedom prosthetic arm with integrated sensors for temperature, pressure, and position. This will allow the arm to interact safely and effectively with a wide range of objects, including delicate or potentially hazardous ones.
Neuroscience Part: Decoding the Language of the Brain
The neuroscience component focuses on establishing a reliable communication channel between the user’s brain and the prosthetic arm. This involves:
Data Acquisition: Using pre-existing or collecting EEG data using a non-invasive headset while participants imagine or attempt specific hand movements. This data will form the basis for training our machine learning models.
Signal Preprocessing: Cleaning the raw EEG data to remove artifacts (e.g., muscle movements, eye blinks) and enhance the signal-to-noise ratio. Techniques like Canonical Correlation Analysis (CCA), independent component analysis (ICA), Regression Analysis, and Blind Source Separation (BSS) will be employed.
Feature Extraction: Identifying and extracting relevant features from the preprocessed EEG signals that are indicative of different motor intentions. This may involve time-frequency analysis (e.g., using wavelets), power spectral density estimation, and common spatial patterns (CSP).
Machine Learning for Classification: Training machine learning models, such as Support Vector Machines (SVMs) and Neural Networks, to classify the extracted features into distinct movement commands. Model performance will be rigorously evaluated using metrics like accuracy, precision, recall, and F1-score.
Real-time Control: Implementing a real-time system that translates the classified brain signals into control signals for the prosthetic arm’s motors, enabling smooth and intuitive movement.
Computer Vision Part: Giving Sight to the Prosthetic Arm
The computer vision component empowers the prosthetic arm with the ability to perceive and interact with its environment intelligently. This involves:
Object Detection: Utilizing state-of-the-art object detection models, such as YOLO (You Only Look Once) or RT-DETR (Real-Time Detection Transformer), to identify and locate objects within the camera’s field of view.
Scene Understanding: Employing semantic segmentation techniques (e.g., DeepLabV3) to understand the context of the scene, differentiating between objects, surfaces, and background elements.
Grasp Point Detection: Developing algorithms to identify optimal grasp points on detected objects, considering factors like object shape, orientation, and stability.
Hand Landmark Detection: Mediapip Hands is used to perform Hand Landmark Detection.
Depth Estimation: Using techniques like monocular depth estimation or stereo vision (with RGB-D cameras) to determine the distance between the prosthetic arm and the target object, enabling precise reaching and grasping.
Mechanical/Electronics Part: Building the Physical Embodiment
The mechanical and electronics component focuses on creating the physical prosthetic arm and integrating the necessary sensors and actuators. This involves:
Prosthetic Arm Design: Designing a multi-degree-of-freedom arm using CAD software (e.g., Fusion 360) and 3D printing for rapid prototyping. The design will prioritize lightweight materials, robust construction, and a range of motion that mimics the human arm.
Motor Integration: Selecting and integrating appropriate servo motors to control the movement of the arm’s joints, ensuring smooth and precise actuation.
Sensor Integration: Incorporating sensors for temperature and pressure to provide feedback on the environment and enhance object handling.
Control System: Developing a control system (using Arduino or Raspberry Pi) to process inputs from the EEG decoding and computer vision modules, and generate the appropriate motor commands.
Simulation and Integration
Before hardware implementation, simulations will be conducted using ROS2 (Robot Operating System) and Gazebo. This will allow us to test the integrated system in a virtual environment, refine control algorithms, and identify potential issues before moving to real-world trials.
Expected Outcomes and Impact
This project has the potential to significantly improve the lives of individuals with limb loss by providing a more intuitive, responsive, and adaptable prosthetic arm. The integration of brain-computer interface technology and computer vision represents a major step towards creating truly intelligent assistive devices. The successful development and implementation of this technology would not only represent a major leap forward in prosthetics but also advance our understanding of the complex interplay between the brain, the body, and the environment.
