International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 05 | May 2023
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p-ISSN: 2395-0072
RECOGNITION SYSTEM USING MYO ARMBAND FOR HAND GESTURES SURVEY PAVAN K S 1, Prof. TEJASWINI. P. S 2 1 M. Tech student, CSE, Bangalore Institute of Technology, Karnataka, India.
2 Professor, Dept. of CSE, Bangalore Institute of Technology, Karnataka, India.
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Abstract -In previous years, the recognition system for hand
loss in 2022, 430 million of them have disabling hearing loss. It is expected that by 2050, people diagnosed with hearing loss may cross 180% of the current cases.[5] The number was 278 million in 2005.[4] Hence, the requirement of a recognition system for hand gestures that the hearing impaired may use to easily express their thoughts, feeling, and knowledge instead of verbal communication is important. In these studies, it has been shown that it is not efficient to construct a basic EMG-based recognition system for hand gestures as the problem is complex because of the various gestures being used, and EMG systems having multiple electrodes.[8] Thus, to build an efficient solution, algorithms/methods of machine learning or deep learning are used to interpret the underlying pattern of the signals to recognize hand gestures automatically. To accomplish such a goal, k-Nearest Neighbor (kNN) [8], Decision-Tree algorithm, Support-Vector-Machine algorithm (SVM) [6][8], and Artificial-Neural-Networks algorithm (ANN) [8] are employed as they are effective in this process. This method can even be of use to the movement of the prosthetic hand with the attachment of the Myo armband to the arm that helps control gestures of the dexterous prosthetic hand by using arm muscles data.[7][13] The data used is tested by a 10-fold cross-validation method to compare the learning algorithms with each other(“which are as follows Random Forest1, RBF-Network2, Neural-Network3, Naïve-Bayes4, KNN5, NB-Tree6, J487, and Decision Table8 that can be some of the algorithms”). This is done to obtain the data for the Myo armbands to be trained to recognize.[9] Recent studies have explored supervised learning-based methods, such as CNN (convolutional neural network) and RNN (recurrent neural network) to implement the HCI (Human-Computer Interaction) device.[10] We can make use of deep-learning techniques for feature extraction to be done automatically and classification is also done the same way.[12]This method of recognition is utilized in a variety of topics that includes the recognition of sign language, prosthesis control of the human limb or arm, controlling the robots, and interfaces used in games/VR.[13]Currently, in 2020, using Photoplethysmography to recognize the data of hand gestures.[14] Then U-net was used for hand image processing to recognize the gestures more accurately with respect to hand movements.[15] The UNET architecture can be used to access the semantically segmented mask of the input, which is then given to a VGG16 model for classification.[16]
gestures has been researched greatly as it has the ability to communicate and connect with any IoT device fruitfully with the help of the Human-computer interface. This paper presents an overview of the recent recognition systems using Myo armbands for hand gestures. Common methods used in the recognition system for hand gestures of the referred papers are listed in this paper. A summary of the recognition system for hand gestures with methods, databases, and algorithms used with the aim of the paper and some drawbacks and future work are mentioned. KEYWORDS: U-Net, CNN, ANN, RNN, Deep Learning, Myo Armband.
1.INTRODUCTION The recognition system for hand gestures aim is to implement a natural interaction between humans and computers in a way in which the registered gestures are used in controlling the robotic system or transferring useful information. This research is useful for people who are suffering from hearing loss and people who can gain a helpful way to use input gadgets to obtain peak-level of accuracy while maxing out the convenience at which the users make use of it. As they rely on hand gestures for communication they have to be able to convert any gesturebased language into a text form with the help of recognition gadgets that are used for translations. [1] The Myo Armband for controlling the gesture is one of the few devices used in identifying Hand gestures that usually represents the user’s intention to act upon or achieve specific behaviors using devices equipped to the arm to easily capture the data and interpret them. [2] This device is a new approach to the process of humancomputer interaction (HCI) tool that is implemented to capture the forearm’s muscular activity of the person its equipped to and then interprets the gestures of the fingers, hands, and arms by analyzing EMG (electro-myo-gram) signals of the muscles.[3] In this, they use EMG sensors for the effective capture of EMG signals.[2][3] According to the data collected by World - Health Organization, the values of people suffering due to hearingloss is approximately more than 1.5 billion people (nearly 20% of the human population in the world) live with hearing
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