Deep learning approach to control of prosthetic hands with electromyography signals

by   Mohsen Jafarzadeh, et al.

Natural muscles provide mobility in response to nerve impulses. Electromyography (EMG) measures the electrical activity of muscles in response to a nerve's stimulation. In the past few decades, EMG signals have been used extensively in the identification of user intention to potentially control assistive devices such as smart wheelchairs, exoskeletons, and prosthetic devices. In the design of conventional assistive devices, developers optimize multiple subsystems independently. Feature extraction and feature description are essential subsystems of this approach. Therefore, researchers proposed various hand-crafted features to interpret EMG signals. However, the performance of conventional assistive devices is still unsatisfactory. In this paper, we propose a deep learning approach to control prosthetic hands with raw EMG signals. We use a novel deep convolutional neural network to eschew the feature-engineering step. Removing the feature extraction and feature description is an important step toward the paradigm of end-to-end optimization. Fine-tuning and personalization are additional advantages of our approach. The proposed approach is implemented in Python with TensorFlow deep learning library, and it runs in real-time in general-purpose graphics processing units of NVIDIA Jetson TX2 developer kit. Our results demonstrate the ability of our system to predict fingers position from raw EMG signals. We anticipate our EMG-based control system to be a starting point to design more sophisticated prosthetic hands. For example, a pressure measurement unit can be added to transfer the perception of the environment to the user. Furthermore, our system can be modified for other prosthetic devices.



There are no comments yet.


page 8

page 9


Convolutional Neural Networks for Speech Controlled Prosthetic Hands

Speech recognition is one of the key topics in artificial intelligence, ...

End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands

Speech is one of the most common forms of communication in humans. Speec...

End-to-end User Recognition using Touchscreen Biometrics

We study the touchscreen data as behavioural biometrics. The goal was to...

EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

In recent years, real-time control of prosthetic hands has gained a grea...

An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks

Respiratory rate (RR) is a clinical sign representing ventilation. An ab...

BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data

The increasing popularity of smartwatches as affordable and longitudinal...

A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization

We investigate nonlinear prediction in an online setting and introduce a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.