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This study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results. EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition. 14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set. The greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures. • Reliable real-time classification of EMG data achieved using SVM algorithm and one feature. • Performance using individual and generalized EMG data compared. • Training on individual data determined to give significant better accuracy • Participants observed to train to repeat activation of relevant muscles. [ABSTRACT FROM AUTHOR]

• Revolutionary Rehab Tool: This advanced hand exoskeleton is designed to help those with partial or total hand function loss, blending cutting-edge tech with user-friendly design for better rehabilitation. • Wide Range of Motion: With 14 active and 6 passive degrees of freedom, the exoskeleton supports various hand movements, including finger and wrist motions, making it highly adaptable. • Comfort and Style: Fits comfortably on hands of all sizes while maintaining a sleek, appealing look, ensuring ease of use and daily wearability. • Everyday Usability: Portable and practical, the exoskeleton can be easily integrated into daily routines, promoting ongoing rehabilitation. • Future-Ready: This system paves the way for future advancements in wearable rehab tech, with potential applications for other limbs and integration with new therapeutic methods like virtual reality. Numerous studies have been conducted using wearable sensors on gesture recognition and classification methods for the control of robotic arms, prostheses, and exoskeleton systems. The primary goal of this study is to classify 32 different hand gestures in real-time using electromyography (EMG) signals. EMG signals obtained by measuring the electrical activity of the muscles were simulated wirelessly on a hand exoskeleton. A Myo Armband placed on the upper arm was used to generate these signals for evaluating and validating the system. The signals were normalized according to maximum voluntary contraction (MVC), and relevant features such as MAV, STD, VAR, RMS, IEMG, ZC, and WL were selected using the univariate feature selection. A support vector machine (SVM) was then used for classification. The 32 different hand gestures were classified to an accuracy level of 99.9%. In short, this proposed approach can be effectively used in real-time hand gesture recognition and generating real-time actions for the hand exoskeleton system. [ABSTRACT FROM AUTHOR]

A cost-effective, easily-accessible neuro-motor rehabilitation solution is proposed that can determine the range of motion and the kinematic ability of participants. A serious game comprising four-scenarios are developed in which the players control an avatar that mirrors the rotations of the upper-limb joints through multi-channel-input devices (Kinect, Myo, FootPedal). Administered functional reach tests (FRT) challenge the player to interact with a 3D-environment while standing or sitting and using the FootPedal which simulates the action of walking whilst body movement is measured concurrently. The FRT’s complexity level is adapted using a Monte Carlo Tree Search algorithm which determines a virtual object’s position based on the proved ability of the user. Twenty-three volunteers were recruited to play the game in 45-min sessions. The data show that the system has a more positive impact on players performance and is more motivating than formal therapy. The visual representation of the trajectory of the objects is shown to increase the perception of the participants voluntary/involuntary upper extremity movement, and the results show a comparable inter-session reliability (acceptable-good) over two repeated sessions. A high Pearson correlation demonstrates the validity of using Kinect and Myo devices in assessing upper-limb rehabilitation, and the timing and the clinically relevant movement data have a higher accuracy when the devices are paired. [ABSTRACT FROM AUTHOR]

The classification of the bio-signal has been used for various purposes in the literature as they are versatile in diagnosis of anomalies, improvement of overall health and sport performance and creating intuitive human computer interfaces. However, automatic identification of the signal patterns on a streaming real-time signal requires a series of complex procedures. A plethora of heuristic methods, such as neural networks and fuzzy systems, have been proposed as a solution. These methods stipulate certain conditions, such as preconditioning the signals, manual feature selection and large number of training samples. In this study, we introduce a novel variant and application of the Collaborative Representation based Classification (CRC) in spectral domain for recognition of hand gestures using raw surface electromyography (EMG) signals. The CRC based methods do not require large number of training samples for an efficient pattern classification. Additionally, we present a training procedure in which a high end subspace clustering method is employed for clustering the representative samples into their corresponding class labels. Thereby, the need for feature extraction and spotting patterns manually on the training samples is obviated. We presented the intuitive use of spectral features via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set compared to the available methods. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation. [ABSTRACT FROM AUTHOR]

The effect of user experience on user satisfaction metrics in using MYO armband (hand gesture navigation) to control interactive maps and various other applications is still in a research state. In general, usability can be measured in terms of effectiveness, efficiency and satisfaction based on the metrics-task completion, error counts, task times and satisfaction scores. In this paper, we considered only satisfaction metrics. A simple and widely used System Usability Scale (SUS) questionnaire model is implemented to suggest some guidelines about the use of MYO armband. Another questionnaire, with a focus on ergonomic issues related to the use of the device such as Social Acceptability, Ease of use and ease of learning, Comfort and Stress, attempted do discover characteristics of hand gesture navigation using MYO. The results of this study can be used in a way to support the development of interactive maps using hand gesture navigation. [ABSTRACT FROM AUTHOR]

• Finger gestures are difficult to process due to overlapping layered structure of the arm muscles. • By examining the effects of gyroscope data, a new feature has been tried to be given to the literature. • EMG and gyroscope signals were acquired using Myo Armband with surface electrodes. • The data set used was shared in the supplementary documents section of the journal. • The highest performance features were determined using sequential forward feature selection. Gestures of the human hand can be identified through processing of surface electromyography (sEMG) signals. The human hand can perform many gestures via manipulation of the fingers. With correct classification of finger gestures, the mobility of a prosthetic hand can be increased and provide greater functionally. In this study, reliable classification was obtained for sEMG finger data acquired from a Myo armband placed on the lower forearm. In order to improve classification, gyroscopic signals, not previously used in other studies, were investigated in the sEMG finger data. Data was acquired from ten normal subjects using the Myo armband to identify 6 finger gestures: thumb, index finger, middle finger, little finger, ring finger and rest. Participants repeated each gesture thirty times. sEMG signals were preprocessed to extract features. 17 features were used in the feature matrix. By using the sequential forward feature selection method, the highest performance feature set was determined. Support Vector Machine, K-Nearest Neighbor and multilayer artificial neural network were used as classification algorithm. The classification was made using the Classification Learner Application and Neural Network Pattern Recognition Tool in Matlab®. The best performance with the features extracted only from sEMG data was 94.40% using the Artificial Neural Networks (ANN) method. The best performance with the features extracted from both sEMG and gyroscopic data was 96.30% (p-value < 0.05)with the ANN method. It is seen that gyroscopic signals can increase classification performance. [ABSTRACT FROM AUTHOR]