Tiny hand gesture recognition without localization via a deep convolutional network

Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds

Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data

Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers, k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures

Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis

We present a wireless-inertial-measurement-unit- (WIMU-) based hand motion analysis technique for handwriting recognition in three-dimensional (3D) space. The proposed handwriting recognition system is not bounded by any limitations or constraints; users have the freedom and flexibility to write characters in free space. It uses hand motion analysis to segment hand motion data from a WIMU device that incorporates magnetic, angular rate, and gravity sensors (MARG) and a sensor fusion algorithm to automatically distinguish segments that represent handwriting from nonhandwriting data in continuous hand motion data. Dynamic time warping (DTW) recognition algorithm is used to recognize handwriting in real-time. We demonstrate that a user can freely write in air using an intuitive WIMU as an input and hand motion analysis device to recognize the handwriting in 3D space. The experimental results for recognizing handwriting in free space show that the proposed method is effective and efficient for other natural interaction techniques, such as in computer games and real-time hand gesture recognition applications

Gesture-Recognizing Hand-Held Interface with Vibrotactile Feedback for 3D Interaction

This article presents a hand-held interface system for 3D interaction with digital media contents. The system is featured with 1) tracking of the full 6 degrees-of-freedom position and orientation of a hand-held controller, 2) robust gesture recognition using continuous hidden Markov models based on the acceleration and position measurements, and 3) dual-mode vibrotactile feedback using both vibration motor and voice-coil actuator. We also demonstrate the advantages of the system through a usability experiment.(1)X1122sciescopu

Reconocimiento de gestos basado en acelerómetros

En los últimos años, ha crecido de forma significativa el interés por la utilización de dispositivos capaces de reconocer gestos humanos. En este trabajo, se pretenden reconocer gestos manuales colocando sensores en la mano de una persona. El reconocimiento de gestos manuales puede ser implementado para diversos usos y bajo diversas plataformas: juegos (Wii), control de brazos robóticos, etc. Como primer paso, se realizará un estudio de las actuales técnicas de reconocimiento de gestos que utilizan acelerómetros como sensor de medida. En un segundo paso, se estudiará como los acelerómetros pueden utilizarse para intentar reconocer los gestos que puedan realizar una persona (mover el brazo hacia un lado, girar la mano, dibujar un cuadrado, etc.) y los problemas que de su utilización puedan derivarse. Se ha utilizado una IMU (Inertial Measurement Unit) como sensor de medida. Está compuesta por tres acelerómetros y tres giróscopos (MTi-300 de Xsens). Con las medidas que proporcionan estos sensores se realiza el cálculo de la posición y orientación de la mano, representando esta última en función de los ángulos de Euler. Un aspecto importante a destacar será el efecto de la gravedad en las medidas de las aceleraciones. A través de diversos cálculos y mediante la ayuda de los giróscopos se podrá corregir dicho efecto. Por último, se desarrollará un sistema que identifique la posición y orientación de la mano como gestos reconocidos utilizando lógica difusa. Tanto para la adquisición de las muestras, como para los cálculos de posicionamiento, se ha desarrollado un código con el programa Matlab. También, con este mismo software, se ha implementado un sistema de lógica difusa con la que se realizará el reconocimiento de los gestos, utilizando la herramienta FIS Editor. Las pruebas realizadas han consistido en la ejecución de nueve gestos por diferentes personas teniendo una tasa de reconocimiento comprendida entre el 90 % y 100 % dependiendo del gesto a identificar. ABSTRACT In recent years, it has grown significantly interest in the use of devices capable of recognizing human gestures. In this work, we aim to recognize hand gestures placing sensors on the hand of a person. The recognition of hand gestures can be implemented for different applications on different platforms: games (Wii), control of robotic arms ... As a first step, a study of current gesture recognition techniques that use accelerometers and sensor measurement is performed. In a second step, we study how accelerometers can be used to try to recognize the gestures that can make a person (moving the arm to the side, rotate the hand, draw a square, etc...) And the problems of its use can be derived. We used an IMU (Inertial Measurement Unit) as a measuring sensor. It comprises three accelerometers and three gyroscopes (Xsens MTI-300). The measures provided by these sensors to calculate the position and orientation of the hand are made, with the latter depending on the Euler angles. An important aspect to note is the effect of gravity on the measurements of the accelerations. Through various calculations and with the help of the gyroscopes can correct this effect. Finally, a system that identifies the position and orientation of the hand as recognized gestures developed using fuzzy logic. Both the acquisition of samples to calculate position, a code was developed with Matlab program. Also, with the same software, has implemented a fuzzy logic system to be held with the recognition of gestures using the FIS Editor. Tests have involved the execution of nine gestures by different people having a recognition rate between 90% and 100% depending on the gesture to identify