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Proceedings ArticleDOI

Comparative Study of Motion Recognition with Temporal Modelling of Electromyography for Thumb and Index Finger Movements aiming for Wearable Robotic Finger Exercises

01 Jul 2018-pp 509-514
TL;DR: The project aims to perform pattern recognition of thumb and index finger gestures from the Electromyography recordings acquired by a recently introduced External Wearable device to implement a suitable model for creating an intuitive human-machine interface like robotic Hand exoskeleton for rehabilitation purposes.
Abstract: The project aims to perform pattern recognition of thumb and index finger gestures from the Electromyography (EMG) recordings acquired by a recently introduced External Wearable device. On the basis of the selected time domain features as reviewed based on classification performance, machine learning techniques, such as K-nearest neighbour (KNN), Support Vector Machine (SVM), Discriminant Analysis etc. are compared to choose a suitable model for recognition of same and different finger movements. The recognition model obtained for a set of six hand-finger gestures shows an accuracy of 80–86% in KNN model for two Different movements of Thumb and index finger and about S2–SS% in SVM model for two same movements of index finger and thumb using single myo armband. The trained model obtained from single myo armband was also tested with data from double myo armbands. As a result, the accuracy obtained was in a range of 66–82% for various gestures. The post-analysis results are promising and competent evidence for available literature and for developing user-friendly medical devices. The purpose of analyzing the following gestures using Myo armband is to implement a suitable model for creating an intuitive human-machine interface like robotic Hand exoskeleton for rehabilitation purposes.
Citations
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Journal ArticleDOI
TL;DR: This work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands by determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control.
Abstract: Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

47 citations


Cites result from "Comparative Study of Motion Recogni..."

  • ...Many of the relative accuracy differences found in this work are corroborated with results from some of the existing literature [7, 11, 48], but the standardized and open pattern recognition framework used here provided results that are easily comparable with any other works that opt to use the same system....

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Journal ArticleDOI
TL;DR: In this article , the authors used Support Vector Machine, K-Nearest Neighbor and multilayer artificial neural network (ANN) to identify 6 finger gestures: thumb, index finger, middle finger, little finger, ring finger and rest.

10 citations

Proceedings ArticleDOI
26 Jun 2020
TL;DR: A model for prosthetic arm control was proposed by processing EMG signals so that finger gestures can move independently and it is estimated that the functionality of the prosthetic arms will be increased by processing finger data.
Abstract: Muscles are one of the basic building blocks of our body, which allows us to perform various movements. As a result of the contraction and relaxation of the muscles, myoelectric signals are formed and movement is provided. EMG signal is obtained by measuring these signals with electrodes. By processing EMG signals, human movements can be imitated and used in many different areas.The human hand can perform many combinations of hand gestures thanks to the different mobility of the fingers. For this reason, it can be easier for the prosthetic hands to perform different hand gestures by moving the fingers independently. In this context, the aim of the research is to propose a model by processing EMG signals so that finger gestures can move independently. EMG signals were acquired using myo armbands. Data set was created with 5 finger gestures and resting hand gestures. The data set was filtered, the part where the gesture was performed in the preprocessing was determined and the windowing process was applied. The classification process was performed by eliminating the features of the EMG signals. In the 100 ms 50% overlapping window, 95.8% classification success was achieved by using the SKNN method with the EWL feature. When the experimental results were examined, it was observed that a successful model was created.In this study, a model for prosthetic arm control was proposed by processing finger data. This model is feasible for prosthetic arms and it is estimated that the functionality of the prosthetic arm will be increased by processing finger data.

7 citations


Cites background from "Comparative Study of Motion Recogni..."

  • ...The performance of all gesturesvaries between 6682% [3]....

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  • ...[3] 6 person, 6...

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Journal ArticleDOI
TL;DR: The aim of this work was to develop a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs.
Abstract: The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users' needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93-95% for flexion and extension, respectively.

5 citations

Journal ArticleDOI
TL;DR: A novel 7-channel sEMG armband is proposed, able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices, which can be an appealing solution for long-term medical and consumer applications.
Abstract: Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.

3 citations

References
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Journal ArticleDOI
TL;DR: The various methodologies and algorithms for EMG signal analysis are illustrated to provide efficient and effective ways of understanding the signal and its nature to help researchers develop more powerful, flexible, and efficient applications.
Abstract: Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

1,195 citations

Journal ArticleDOI
TL;DR: In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed and it is indicated that most time domain features are superfluity and redundancy.
Abstract: Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.

1,151 citations

Journal ArticleDOI
TL;DR: This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance.
Abstract: This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.

730 citations


"Comparative Study of Motion Recogni..." refers background in this paper

  • ...Another feature we chose is waveform length as it makes gesture recognition more robust and acceptable as shown by [14]....

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Journal ArticleDOI
01 Nov 2011
TL;DR: A framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors that facilitates intelligent and natural control in gesture-based interaction.
Abstract: This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.

544 citations

Journal ArticleDOI
TL;DR: This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006, but it was brought to the attention of the journal and authors that reference 74 was incorrect.
Abstract: This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

515 citations


"Comparative Study of Motion Recogni..." refers background in this paper

  • ...Detection of EMG signal and its analysis has become an area of great interest due to its immense biomedical applications in the field of Humanmachine interaction systems and Motor-Rehabilitation [1]....

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