Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning
Organizations: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan · Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan
Abstract
Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer interaction. Myo-electric prosthetics analyze and classify the electrical signals recorded from the residual limb. The classified output is then used to control the position of motors in a robotic hand and a movement is produced. The aim of this project is to develop a low-cost and effective myo-electric prosthetic hand that would meet the needs of amputees in developing countries. The proposed prosthetic hand should be able to accurately classify five different patterns (gestures) using EMG recordings from three muscles and control a robotic hand accordingly. The robotic hand is composed of two servo motors allowing for two degrees of freedom. After establishing an efficient signal acquisition and amplification system, EMG signals were thoroughly analyzed in the frequency and time domains. Features were extracted from both domains and a shallow neural network was trained on the two sets of data. Results yielded an average classification accuracy of 94.8% and 93.2% for the time and frequency domains respectively. Furthermore, results showed a faster computation and response for the time domain analysis; hence, it was adopted for the classification system. A wrist rotation mechanism was designed and tested to add significant functionality to the prosthetic. The mechanism is controlled by two of the five gestures, one for each direction. Which added a third degree of freedom to the overall design. Finally, a tactile sensory feedback system which uses force sensors and vibration motors was developed to enable sensation of the force inflicted on the hand for the user.
Figures & tables
| Parameter | Frequency Domain | Time Domain |
|---|---|---|
| Average Accuracy | 93.2% | 94.8% |
| Algorithm Complexity | O(nlogn) | O(n) |
| Feature Extraction Time/Window (ms) | 198 | 26 |
| Training Epochs | 70 | 99 |
| Training Time (Seconds) | 1 | 33 |
| Unit | Quantity | Cost Per One Unit (USD) | Total Cost (USD) |
|---|---|---|---|
| Arduino Mega 2560 | 1 | 38.50 | 38.50 |
| AD620 | 3 | 12.60 | 37.80 |
| AD8618 | 3 | 6.12 | 18.36 |
| GWS MICRO 2BBMG | 3 | 15.85 | 47.55 |
| 3D Printed Parts (PLA) | 1 | 80 | 80 |
| Steel Ball Bearing | 1 | 2.00 | 2.00 |
Assistive Robotics
Assistive robotics develops robots and wearable devices that support mobility, daily activities, rehabilitation, and care.
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