COVID – 19 OUTBREAK ANALYSIS ON THE BASIS OF MACHINE LEARNING

2021 ◽  
Vol 1 (10) ◽  
pp. 351-363
Author(s):  
Abhishek Sharma ◽  
Shubham Sharma
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Research Field ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Random Forest Regression ◽  
Normal People ◽  
Human Gestures ◽  
Dynamic Gestures

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Covid-19, SIRD model, Linear Regression, XGBoost, Random Forest Regression, SVR, LightGBM, Machine learning, Intervention.

HAND GESTURE RECOGNITION IN REAL-TIME

2021 ◽  
Vol 1 (10) ◽  
pp. 346-350
Author(s):  
Priyanshi Gupta ◽  
Amita Goel ◽  
Nidhi Sengar ◽  
Vashudha Bahl
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Research Field ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Normal People ◽  
Human Gestures ◽  
Dynamic Gestures ◽  
Hand Pose

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

scholarly journals Hand gesture recognition using machine learning algorithms

2020 ◽  
Vol 1 (3) ◽  
pp. 116-120
Author(s):  
Abhishek B. ◽  
Kanya Krishi ◽  
Meghana M. ◽  
Mohammed Daaniyaal ◽  
Anupama H. S.
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Human Gestures ◽  
The Face ◽  
Mathematical Algorithms ◽  
User Friendly ◽  
Computer Interaction

Gesture recognition is an emerging topic in today’s technologies. The main focus of this is to recognize the human gestures using mathematical algorithms for human computer interaction. Only a few modes of Human-Computer Interaction exist, they are: through keyboard, mouse, touch screens etc. Each of these devices has their own limitations when it comes to adapting more versatile hardware in computers. Gesture recognition is one of the essential techniques to build user-friendly interfaces. Usually gestures can be originated from any bodily motion or state, but commonly originate from the face or hand. Gesture recognition enables users to interact with the devices without physically touching them. This paper describes how hand gestures are trained to perform certain actions like switching pages, scrolling up or down in a page.

scholarly journals A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1504
Author(s):  
Mohammed Asfour ◽  
Carlo Menon ◽  
Xianta Jiang
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Recognition Performance ◽  
Classification Performance ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Processing Pipeline ◽  
Gesture Classification ◽  
Art Research

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

scholarly journals Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 1771-1777
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Decision Fusion ◽  
Hand Gesture Recognition ◽  
Machine Learning Techniques ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition

The hand gesture detection problem is one of the most prominent problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the hand gesture recognition. These techniques find applications in sign language recognition, virtual reality, human machine interaction, autonomous vehicles, driver assistive systems etc. In this paper, the goal is to design a system to correctly identify hand gestures from a dataset of hundreds of hand gesture images. In order to incorporate this, decision fusion based system using the transfer learning architectures is proposed to achieve the said task. Two pretrained models namely ‘MobileNet’ and ‘Inception V3’ are used for this purpose. To find the region of interest (ROI) in the image, YOLO (You Only Look Once) architecture is used which also decides the type of model. Edge map images and the spatial images are trained using two separate versions of the MobileNet based transfer learning architecture and then the final probabilities are combined to decide upon the hand sign of the image. The simulation results using classification accuracy indicate the superiority of the approach of this paper against the already researched approaches using different quantitative techniques such as classification accuracy.

A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition

2020 ◽  
Author(s):  
Ali Moin ◽  
Andy Zhou ◽  
Abbas Rahimi ◽  
Alisha Menon ◽  
Simone Benatti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Biosensing System

A Study of Hand Gesture Segmentation Techniques for Sign Languages

2020 ◽  
Vol 17 (4) ◽  
pp. 1764-1769
Author(s):  
S. Gobhinath ◽  
T. Vignesh ◽  
R. Pavankumar ◽  
R. Kishore ◽  
K. S. Koushik
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Morphological Processing ◽  
Hand Gesture ◽  
Hand Detection ◽  
Hand Segmentation ◽  
Human Gestures ◽  
Gesture Segmentation ◽  
Physically Challenged

This paper presents about an overview on several methods of segmentation techniques for hand gesture recognition. Hand gesture recognition has evolved tremendously in the recent years because of its ability to interact with machine. Mankind tries to incorporate human gestures into modern technologies like touching movement on screen, virtual reality gaming and sign language prediction. This research aims towards employed on hand gesture recognition for sign language interpretation as a human computer interaction application. Sign Language which uses transmits the sign patterns to convey meaning by hand shapes, orientation and movements to fluently express their thoughts with other person and is normally used by the physically challenged people who cannot speak or hear. Automatic Sign Language which requires robust and accurate techniques for identifying hand signs or a sequence of produced gesture to help interpret their correct meaning. Hand segmentation algorithm where segmentation using different hand detection schemes with required morphological processing. There are many methods which can be used to acquire the respective results depending on its advantage.

scholarly journals Smart Home Automation using Hand Gesture Recognition System

2019 ◽  
Vol 9 (2) ◽  
pp. 18-21
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Personal Computer ◽  
Smart Home ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Home Automation ◽  
Proof Of Concept ◽  
Visual Interpretation

Visual interpretation of hand gestures is a natural method of achieving Human-Computer Interaction (HCI). In this paper, we present an approach to setting up of a smart home where the appliances can be controlled by an implementation of a Hand Gesture Recognition System. More specifically, this recognition system uses Transfer learning, which is a technique of Machine Learning, to successfully distinguish between pre-trained gestures and identify them properly to control the appliances. The gestures are sequentially identified as commands which are used to actuate the appliances. The proof of concept is demonstrated by controlling a set of LEDs that represent the appliances, which are connected to an Arduino Uno Microcontroller, which in turn is connected to the personal computer where the actual gesture recognition is implemented

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