Deaf people around the globe use sign languages for their communication needs. Innovations of new technologies, such as smartphones, offer a host of new functionalities to their users. If such mobile devices become capable of recognizing sign languages, this will open up the opportunity for offering significantly more user-friendly mobile apps to sign language users. However, in order to achieve satisfactory results, there are many challenges that must be considered and overcome, such as light conditions, background noise, processing, and energy limitations. This paper aims to cover the most recent techniques in mobile-based sign language recognition systems. We categorize existing solutions into sensors-based and vision-based, as these two categories offer distinct advantages and disadvantages. The primary focus of this literature review is on two main aspects of sign language recognition: feature detection and sign classification algorithms.

A Survey on Sign Language Recognition Using Smartphones

A new theory of discriminant analysis “the Theory” after R. Fisher is explained. There are five serious problems with discriminant analysis. I completely solve these problems through five mathematical programming-based linear discriminant functions (MP-based LDFs). First, I develop an optimal linear discriminant function using integer programming (IP-OLDF) based on a minimum number of misclassifications (minimum NM (MNM)) criterion. We consider discriminating the data with n-cases by p-variables. The case x i = (x 1i + … + x pi ) is p-vector (i = 1, …, n). Because I formulate IP-OLDF in the p-dimensional discriminant coefficient space b, n-linear hyperplanes (x i × b + 1 = 0) divide the coefficient space into finite convex polyhedrons (CPs). LDFs that correspond to a CP interior point misclassify the same k-cases, and this clearly reveals the relationship between NM and the discriminant coefficient. Because there are finite CPs in the discriminant coefficient space, we should select the CP interior point with MNM. We call this CP, “optimal CP (OCP).” MNM decreases monotonously (MNM p ≥ NMN(p+1)). Therefore, if MNM p = 0, all MNMs of the models, including these p-variables, are zero. If data are general positions, IP-OLDF searches for the vertex of true OCP. However, if data are not general positions, such as Student data, IP-OLDF might not search for the vertex of true OCP. Therefore, I develop Revised IP-OLDF that searches for the interior point of true OCP directly. If LDF corresponds to the CP vertex or edge, there are over p-cases on the discriminant hyperplane and LDF cannot discriminate these cases correctly (Problem 1). This fact means that NM might not be true. Only Revised IP-OLDF is free from Problem 1. When IP-OLDF discriminates Swiss banknote data that have six variables, MNM of the two-variable model (X4, X6) is zero. Therefore, 16 models, including (X4, X6), are zero, and 47 models are not linearly separable. Although a hard-margin SVM (H-SVM) indicates linearly separable data (LSD) clearly, there are few types of research on LSD discrimination. Most statisticians erroneously believe that the purpose of discrimination is to discriminate overlapping data, not LSD. All LDFs, with the exception of H-SVM and Revised IP-OLDF, might not discriminate LSD correctly (Problem 2). Moreover, such LDFs cannot determine whether the data overlap or LSD because MNM = 0 means LSD and MNM > 0 means overlap. I demonstrate that Fisher’s LDF and a quadratic discriminant function (QDF) cannot judge the pass/fail determination using examination scores and that the 18 error rates of both discriminant functions are very high. I explain the defect of the generalized inverse matrix technique and that QDF misclassifies all cases of class 1 to class 2 for a particular case (Problem 3) using Japanese-automobile data. Fisher never formulated an equation for standard errors (SEs) of the error rate and discriminant coefficient (Problem 4). The k-fold cross-validation for small sample method (Method 1) solves Problem 4. This offers the error rate means, M1 and M2, from the training and validation samples in addition to the 95 % confidence interval (CI) of the error rate and coefficient. I propose a simple and powerful model selection procedure to select the best model with minimum M2 instead of the leave-one-out (LOO) procedure. The best models of Revised IP-OLDF are better than seven other LDFs. For more than ten years, many researchers have struggled to analyze microarray dataset (the dataset) that is LSD (Problem 5). We call the linearly separable dataset as the largest Matroska. Only Revised IP-OLDF can select features naturally and find the smaller gene set or subspace (smaller Matroska) in the dataset. When we discriminate the smaller Matroska again, we can find the smaller Matroska. If we cannot find the smaller Matroska anymore, I call the last smaller Matroska as small Matroska (SM) that is linearly separable gene subspace. Because the dataset has the structure of Matroska, I develop a Matroska feature-selection method (Method 2) that finds the surprising structure of the dataset that is the disjoint union of several SMs, which are linearly separable subspaces or models. Now, we can analyze each SM very quickly because all SMs are small samples. The Theory is most suitable to analyze the datasets.

New Theory of Discriminant Analysis

As technology is rising, more advancements are made in making the life of people easier, by providing methods for easy monitoring and managing In this paper, a Home Automation model is designed to provide ease of control of home appliances, using an android application The elderly and physically challenged people can perform their day-to-day activities efficiently In previous methods, accelerometers are used to monitor the activity, which are accurate but are not flexible and portable The proposed system detects the gestures given as input by the user and controls the home appliance The client interface is responsible for capturing the input gesture from the user, using an android application and uploading it on the raspberry pi server Raspberry pi acts as an important preprocessor Backend Processing involves image preprocessing, training the CNN model, and prediction of image class category of input gesture image Based on this predicted class of image, the respectively assigned action takes place at the home interface

Gesture Controlled Home Automation using CNN

This paper focuses on hand pose estimation by proposing a system that solves real-time static hand gesture detection and recognition issues for interacting with smartphones. The first step of our work consists in detecting and tracking the hand in a complex background. The second step consists in recognizing hand gestures using SVM “Support Vector Machine”. Finally, we developed a grammar to generate gesture commands for mobile applications control. This work presents a system based on a real-time hand gesture recognition algorithm for Android devices. The idea here is to allow the user interacting with the mobile device without the need to touch the screen. In this system, the Smartphone is able to perform all necessary steps to recognize gestures without the need to connect to a distant device.

Hand pose estimation system based on Viola-Jones algorithm for Android devices

Sign language is a type of language that uses manual communication to convey meaningful messages to other people. This includes simultaneous employing of hand gestures, movement, orientation of the fingers, arms or body, and facial expressions to convey a speaker's ideas. American Sign Language is one of the popular sign language used by most of deaf and dumb people to communicate with each other. American Sign Language is also referred to as ASL. A real-time sign language translator is required for facilitating communication between the deaf community and the general public. We propose a system called Dynamic tool for American Sign Language (ASL) finger spelling interpreter which can consistently classify the letters a-z. The dataset consists of a set of American Sign Language videos. Our approach first converts the videos into frames and then pre-processes the frames to convert them into greyscale images. Then the Convolutional Neural Network (CNN) classifier is used for building the classification model which classifies the frames into 26 different classes representing 26 English alphabets. Finally, the evaluation of the classification model is carried out with test data providing the output in the form of text or voice. The cross-validation accuracy results of 98.66% is achieved from our approach.

Dynamic Tool for American Sign Language Finger Spelling Interpreter

These days mobile devices like phones or tablets are very common among people of all age. They are connected with network and provide seamless communications through internet or cellular services. These devices can be a big help for the people who are not able to communicate properly and even in emergency conditions. A disabled person who is not able to speak or a person who speak a different language, these devices can be a boon for them as understanding, translating and speaking systems for these people. This chapter discusses a portable android based hand sign recognition system which can be used by disabled people. This chapter shows a part of on-going project. Computer Vision based techniques were used for image analysis and PCA was used after image tokenizer for recognition. This method was tested with webcam results to make system more robust.

Android based Portable Hand Sign Recognition System.

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Android based Portable Hand Sign Recognition System.