scholarly journals Recognition of additional myo armband gestures for myoelectric prosthetic applications

2020 ◽  
Vol 10 (6) ◽  
pp. 5694
Author(s):  
Jabbar Salman Hussain ◽  
Ahmed Al-Khazzar ◽  
Mithaq Nama Raheema
Keyword(s):  
Recognition Accuracy ◽  
Hand Gestures ◽  
Myoelectric Prosthesis ◽  
Myoelectric Prostheses ◽  
Viable Solution ◽  
Average Accuracy ◽  
Myo Armband ◽  
Three Body ◽  
Recognition Technique ◽  
Real Hand

Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.

scholarly journals Electromyogram-based hand gesture recognition robust to various arm postures

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Kiwon Rhee ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Practical Applications ◽  
Electrode Positioning ◽  
Recognition Technique

In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

2012 ◽  
Vol 6 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Michael R Dawson ◽  
Farbod Fahimi ◽  
Jason P Carey
Keyword(s):  
Degrees Of Freedom ◽  
Learning Task ◽  
Robotic Arm ◽  
Signal Acquisition ◽  
Myoelectric Prosthesis ◽  
Training Tool ◽  
Myoelectric Prostheses ◽  
Training Systems ◽  
Targeted Muscle Reinnervation

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

scholarly journals Fabric Vest Socket with Embroidered Electrodes for Control of Myoelectric Prosthesis

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1196 ◽  
Author(s):  
Seulah Lee ◽  
Babar Jamil ◽  
Sunhong Kim ◽  
Youngjin Choi
Keyword(s):  
Signal To Noise Ratio ◽  
Cost Effective ◽  
Electrode Impedance ◽  
Rigid Support ◽  
Daily Lives ◽  
Myoelectric Prosthesis ◽  
Myoelectric Prostheses ◽  
Average Classification Accuracy ◽  
High Level ◽  
Semg Signals

Myoelectric prostheses assist users to live their daily lives. However, the majority of users are primarily confined to forearm amputees because the surface electromyography (sEMG) that understands the motion intents should be acquired from a residual limb for control of the myoelectric prosthesis. This study proposes a novel fabric vest socket that includes embroidered electrodes suitable for a high-level upper amputee, especially for shoulder disarticulation. The fabric vest socket consists of rigid support and a fabric vest with embroidered electrodes. Several experiments were conducted to verify the practicality of the developed vest socket with embroidered electrodes. The sEMG signals were measured using commercial Ag/AgCl electrodes for a comparison to verify the performance of the embroidered electrodes in terms of signal amplitudes, the skin-electrode impedance, and signal-to-noise ratio (SNR). These results showed that the embroidered electrodes were as effective as the commercial electrodes. Then, posture classification was carried out by able-bodied subjects for the usability of the developed vest socket. The average classification accuracy for each subject reached 97.92%, and for all the subjects it was 93.2%. In other words, the fabric vest socket with the embroidered electrodes could measure sEMG signals with high accuracy. Therefore, it is expected that it can be readily worn by high-level amputees to control their myoelectric prostheses, as well as it is cost effective for fabrication as compared with the traditional socket.

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

scholarly journals An Ultra-Sensitive Modular Hybrid EMG–FMG Sensor with Floating Electrodes

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4775
Author(s):  
Ang Ke ◽  
Jian Huang ◽  
Luyao Chen ◽  
Zhaolong Gao ◽  
Jiping He
Keyword(s):  
Data Acquisition ◽  
Robust Performance ◽  
Prosthetic Control ◽  
Hand Gestures ◽  
Emg Signal ◽  
Average Accuracy ◽  
Good Consistency ◽  
Compact Design ◽  
Data Acquisition Unit ◽  
Novel Design

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

Clinical results of an investigation of paediatric upper limb myoelectric prosthesis fitting at the Quebec rehabilitation institute

2001 ◽  
Vol 25 (2) ◽  
pp. 119-131 ◽  
Author(s):  
F. Routhier ◽  
C. Vincent ◽  
M-J. Morissette ◽  
L. Desaulniers
Keyword(s):  
Upper Limb ◽  
Dropout Rate ◽  
Clinical Results ◽  
User Evaluation ◽  
Rehabilitation Centre ◽  
Myoelectric Prosthesis ◽  
Myoelectric Prostheses ◽  
Prosthesis Fitting ◽  
Children And Parents

This study was designed to investigate the satisfaction level of young users of myoelectric prostheses who received an upper limb myoelectric prosthesis, to assess their dropout rate and to identify which factors influence the use or non-use of the upper limb myoelectric prosthesis in the eastern part of Quebec (Canada). The users were fitted between 1990 and 1999 at the Quebec Rehabilitation Institute, a major rehabilitation centre located in the province of Quebec. This rehabilitation centre provides cutting-edge expertise not only for the eastern part of Quebec, but also across the entire province, because it is one of only two highly specialised centres serving all of Quebec.A literature review was completed to compile the results obtained in other rehabilitation centres and to identify factors influencing the use or non-use of paediatric upper limb myoelectric prostheses. The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) was used in order to assess the degree to which the children were satisfied with their prostheses.Eighteen (18) children were fitted and trained to use an upper limb myoelectric prosthesis. A total of 10 children and parents agreed to participate. Some 80% of participants said that they were satisfied with their prostheses. A dropout rate of 53% for the overall group (participants and non-participants) seems high compared with that of other studies. Recommendations linked to factors identified in the literature are made. The authors conclude that a multidisciplinary team and structured training and follow-up can improve the clinical results pertaining to all the factors proposed in the literature.

scholarly journals Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

2020 ◽  
Vol 8 (4) ◽  
pp. 304-310
Author(s):  
Windra Swastika ◽  
Ekky Rino Fajar Sakti ◽  
Mochamad Subianto
Keyword(s):  
High Resolution ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Super Resolution ◽  
Training Data ◽  
License Plate ◽  
Plate Number ◽  
Average Accuracy ◽  
Number Recognition ◽  
High Resolution Images

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

scholarly journals LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

2015 ◽  
Vol XL-5/W6 ◽  
pp. 1-8 ◽  
Author(s):  
M. Favorskaya ◽  
A. Nosov ◽  
A. Popov
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Temporal Variations ◽  
Video Sequences ◽  
Hand Gestures ◽  
Dynamic Gestures ◽  
Posture Recognition ◽  
Spatio Temporal ◽  
Skin Detector

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.

scholarly journals HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 7 (2) ◽  
pp. 164
Author(s):  
Aditiya Anwar ◽  
Achmad Basuki ◽  
Riyanto Sigit
Keyword(s):  
Support Vector Machine ◽  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Deaf People ◽  
Hand Gestures ◽  
Moment Invariant ◽  
Myo Armband

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning.  In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

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