8,439 research outputs found
Deep learning approach to control of prosthetic hands with electromyography signals
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.Comment: Conference. Houston, Texas, USA. September, 201
Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative Robotics
Upper limb movement classification, which maps input signals to the target
activities, is a key building block in the control of rehabilitative robotics.
Classifiers are trained for the rehabilitative system to comprehend the desires
of the patient whose upper limbs do not function properly. Electromyography
(EMG) signals and Electroencephalography (EEG) signals are used widely for
upper limb movement classification. By analysing the classification results of
the real-time EEG and EMG signals, the system can understand the intention of
the user and predict the events that one would like to carry out. Accordingly,
it will provide external help to the user. However, the noise in the real-time
EEG and EMG data collection process contaminates the effectiveness of the data,
which undermines classification performance. Moreover, not all patients process
strong EMG signals due to muscle damage and neuromuscular disorder. To address
these issues, this paper explores different feature extraction techniques and
machine learning and deep learning models for EEG and EMG signals
classification and proposes a novel decision-level multisensor fusion technique
to integrate EEG signals with EMG signals. This system retrieves effective
information from both sources to understand and predict the desire of the user,
and thus aid. By testing out the proposed technique on a publicly available
WAY-EEG-GAL dataset, which contains EEG and EMG signals that were recorded
simultaneously, we manage to conclude the feasibility and effectiveness of the
novel system.Comment: 20 pages, 11 figures, 2 tables; Thesis for Undergraduate Research
Project in Computing, NUS; Accepted by Future of Information and
Communication Conference 2023, San Francisc
The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface
http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition
EMG Biofeedback Videogame System for the Gait Rehabilitation of Hemiparetic Individuals
Gemstone Team CHIPWe report a novel approach to electromyographic (EMG) biofeedback for post-stroke hemiparetic gait rehabilitation, using a videogame. An integrated hardware/software system facilitates gameplay of Tiger Woods PGA Tour 2004 in driving range mode by performing rehabilitation exercises. Real-time visual EMG biofeedback is provided as the patient performs exercises. Custom-built bioamplifiers and software collect, amplify, and filter the surface EMG signals from six lower-limb muscles, and score them by feature extraction. The ball is driven a distance proportional to each score. Exercises are scored by comparing the patient's EMG activation with target profiles. The user-friendly system is controlled by prompts on a personal computer. We envision two major benefits from this system. First, the biofeedback is offered in real-time, in a clear, intuitive form, and coupled with task-specific motions. Second, we hypothesize that adopting rehabilitation exercises to control a fun videogame will lead to greater adherence to the exercise regime, with accompanying improvements in gait
Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning.
Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps
Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms
Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPytho
Feature Analysis for Classification of Physical Actions using surface EMG Data
Based on recent health statistics, there are several thousands of people with
limb disability and gait disorders that require a medical assistance. A robot
assisted rehabilitation therapy can help them recover and return to a normal
life. In this scenario, a successful methodology is to use the EMG signal based
information to control the support robotics. For this mechanism to function
properly, the EMG signal from the muscles has to be sensed and then the
biological motor intention has to be decoded and finally the resulting
information has to be communicated to the controller of the robot. An accurate
detection of the motor intention requires a pattern recognition based
categorical identification. Hence in this paper, we propose an improved
classification framework by identification of the relevant features that drive
the pattern recognition algorithm. Major contributions include a set of
modified spectral moment based features and another relevant inter-channel
correlation feature that contribute to an improved classification performance.
Next, we conducted a sensitivity analysis of the classification algorithm to
different EMG channels. Finally, the classifier performance is compared to that
of the other state-of the art algorithm
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