Hand Gesture Recognition Systems with the Wearable Myo Armband

2018 ◽  
Author(s):  
Engin Kaya ◽  
Tufan Kumbasar
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Myo Armband ◽  
Recognition Systems

scholarly journals A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2321 ◽  
Author(s):  
Myoungseok Yu ◽  
Narae Kim ◽  
Yunho Jung ◽  
Seongjoo Lee
Keyword(s):  
Real Time ◽  
Conventional Method ◽  
Gesture Recognition ◽  
Detection Method ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Probability ◽  
Recognition Systems ◽  
Cw Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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>

scholarly journals Hand Gesture Recognition Systems: A Survey

2013 ◽  
Vol 71 (15) ◽  
pp. 25-37 ◽  
Author(s):  
Arpita RaySarkar ◽  
G. Sanyal ◽  
S. Majumder
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Systems

scholarly journals A User-Specific Hand Gesture Recognition Model Based on Feed-Forward Neural Networks, EMGs, and Correction of Sensor Orientation

2020 ◽  
Vol 10 (23) ◽  
pp. 8604
Author(s):  
Marco E. Benalcázar ◽  
Ángel Leonardo Valdivieso Caraguay ◽  
Lorena Isabel Barona López
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Maximum Energy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Performance Reduction ◽  
Recognition Systems ◽  
The Right

Hand gesture recognition systems have several applications including medicine and engineering. A gesture recognition system should identify the class, time, and duration of a gesture executed by a user. Gesture recognition systems based on electromyographies (EMGs) produce good results when the EMG sensor is placed on the same orientation for training and testing. However, when the orientation of the sensor changes between training and testing, which is very common in practice, the classification and recognition accuracies degrade significantly. In this work, we propose a system for recognizing, in real time, five gestures of the right hand. These gestures are the same ones recognized by the proprietary system of the Myo armband. The proposed system is based on the use of a shallow artificial feed-forward neural network. This network takes as input the covariances between the channels of an EMG and the result of a bag of five functions applied to each channel of an EMG. To correct the rotation of the EMG sensor, we also present an algorithm based on finding the channel of maximum energy given a set of synchronization EMGs, which for this work correspond to the gesture waveout. The classification and recognition accuracies obtained here show that the recognition system, together with the algorithm for correcting the orientation, allows a user to wear the EMG sensor in different orientations for training and testing, without a significant performance reduction. Finally, to reproduce the results obtained in this paper, we have made the code and the dataset used here publicly available.

A Review on Vision-Based Hand Gesture Recognition and Applications

2015 ◽  
pp. 256-281 ◽  
Author(s):  
Ananya Choudhury ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Research Area ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Dynamic Background ◽  
Lighting Condition ◽  
Recognition Systems

In the present scenario, vision based hand gesture recognition has become a highly emerging research area for the purpose of human computer interaction. Such recognition systems are deployed to serve as a replacement for the commonly used human-machine interactive devices such as keyboard, mouse, joystick etc. in real world situations. The major challenges faced by a vision based hand gesture recognition system include recognition in complex background, in dynamic background, in presence of multiple gestures in the background, under variable lighting condition, under different viewpoints etc. In the context of sign language recognition, which is a highly demanding application of hand gesture recognition system, coarticulation detection is a challenging task. The main objective of this chapter is to provide a general overview of vision based hand gesture recognition system as well as to bring into light some of the research works that have been done in this field.

scholarly journals Hand gesture recognition using machine learning and the Myo armband

2017 ◽  
Author(s):  
Marco E. Benalcazar ◽  
Andres G. Jaramillo ◽  
Jonathan ◽  
A. Zea ◽  
Andres Paez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Myo Armband

scholarly journals An Energy-Based Method for Orientation Correction of EMG Bracelet Sensors in Hand Gesture Recognition Systems

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6327
Author(s):  
Lorena Isabel Barona Barona López ◽  
Ángel Leonardo Valdivieso Valdivieso Caraguay ◽  
Victor H. Vimos ◽  
Jonathan A. Zea ◽  
Juan P. Vásconez ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Correction Method ◽  
Maximum Energy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Energy Channel ◽  
Recognition Systems ◽  
Accuracy Measures ◽  
Common Architecture

Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

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