Monochrome glove: A robust real-time hand gesture recognition method by using a fabric glove with design of structured markers

Ishiyama, Hidetoshi; Kurabayashi, Shuichi

doi:10.1109/vr.2016.7504716

Cited by 29 publications

(11 citation statements)

References 4 publications

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“…One fundamental problem arises when it comes to interaction devices. Several ways exist for interacting with robots, e.g., data gloves [2], markers [3], gesture controllers [4]. These methods can not be the natural way to interact with neither humans nor robots.…”

Section: A Motivationmentioning

confidence: 99%

3D Gesture Recognition and Adaptation for Human–Robot Interaction

et al. 2022

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Gesture-based human-robot interaction has been an important area of research in recent years. The primary aspect for the researchers has always been to create a gesture detection system that is insensitive to lighting and backdrop surroundings. This research proposes a 3D gesture recognition and adaption system based on Kinect for human-robot interaction. The framework consists of four modules, i.e., pointing gesture recognition, 3D dynamic gesture recognition, gesture adaptation, and robot navigation. The proposed dynamic gesture recognition module employs three distinct classifiers: HMM, Multi-class SVM, and CNN. The adaptation module can adapt to new and unrecognized gestures applying semi-supervised self-adaptation or user consent-based adaptation. A Graphical User Interface (GUI) is built for training and testing the proposed system on the fly. A simple simulator along with two different robot-navigation algorithms are developed to test robot navigation based on the recognized gestures. The framework is trained and tested through a five-fold cross-validation method with a total of 3,600 gesture instances of ten predefined gestures performed by 24 persons (Three age categories: Young, Middle Aged, Adult; each with 1,200 gestures). The proposed system achieves a maximum accuracy score of 95.67% with HMM for Middle Aged category, 92.59% with SVM for Middle Aged category, and 89.58% with CNN for Young category in dynamic gesture recognition. Considering all the three age categories, the system achieves an average accuracy of 94.61%, 91.95%, and 88.97% in recognizing dynamic gestures with HMM, SVM, and CNN respectively. Moreover, the system recognizes pointing gestures in real-time.

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Section: A Motivationmentioning

confidence: 99%

3D Gesture Recognition and Adaptation for Human–Robot Interaction

et al. 2022

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“…Different types of hand gloves [28] were used depending upon the applications. The commonly used gloves are colour gloves, monochromatic gloves [29], fabric gloves [29], virtual gloves [30] and so on.…”

Section: B Detection Based On Special Devicesmentioning

confidence: 99%

Real Time Vision Based Fingertip Detection Strategies

Jacob*,

Sugitha

2019

IJITEE

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The Fingertip Detection acts a specific role in most of the vision based applications. The latest technologies like virtual reality and augmented reality actually follows this fingertip detection concept as its foundation. It is also helpful for Human Computer Interaction (HCI). So fingertip detection and tracking can be applied from games to robot control, from augmented reality to smart homes. The most important interesting field of fingertip detection is the gesture recognition related applications. In the context of interaction with the machines, gestures are the most simplest and efficient means of communication. This paper analyses the various works done in the areas of fingertip detection. A review on various real time fingertip methods is explained with different techniques and tools. Some challenges and research directions are also highlighted. Many researchers uses fingertip detection in HCI systems those have many applications in user identification, smart home etc. A comparison of results by different researchers is also included.

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“…However, its inflexibility and high cost defeat the original purpose of natural human-computer interaction. The other is vision-based methods [4] [5], which use the camera to collect images and then analyse the image information. Compared to sensor-based gesture recognition, vision-based gesture recognition systems use lower-cost cameras, and the interaction between computers and human is more natural.…”

Section: Introductionmentioning

confidence: 99%

MIFD-Net : a hand gesture recognition model based on feature fusion of MLP and CNN

Wang

Song²,

Ni³

et al. 2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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Gesture recognition can play a crucial role in addressing the issue of Human-computer interaction. In this paper, we proposed a vision-based Multi-input fusion deep network (MIFD-Net), which consists of Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN). MIFD-Net first processes hand keypoint data and gesture images using Euclidean distance normalization (ED-Normalization) and image segmentation technologies, respectively. Then, two kinds of data are simultaneously used as input to MIFD-Net. The experimental results show that the MIFD-Net achieves an average accuracy of 99.65% on the self-built dataset in this paper and 99.10% on the NUS hand posture datasets II (NUS-II). The MIFD-Net significantly decreases its FLOPs and the number of parameters and reduces the complexity of the model while maintaining a high recognition rate compared with other gesture recognition models. The MIFD-Net can obtain high accuracy and strong robustness in different environments, lighting, and angles.

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Monochrome glove: A robust real-time hand gesture recognition method by using a fabric glove with design of structured markers

Cited by 29 publications

References 4 publications

3D Gesture Recognition and Adaptation for Human–Robot Interaction

3D Gesture Recognition and Adaptation for Human–Robot Interaction

Real Time Vision Based Fingertip Detection Strategies

MIFD-Net : a hand gesture recognition model based on feature fusion of MLP and CNN

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