Tracing the Motion of Finger Joints for Gesture Recognition via Sewing RGO-Coated Fibers Onto a Textile Glove

Huang, Xiaodong; Wang, Qi; Zang, Siyao; Wan, Jiaxin; Yang, Guang; Huang, Yongqing; Ren, Xiaomin

doi:10.1109/jsen.2019.2924797

Cited by 56 publications

(20 citation statements)

References 25 publications

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“…In recent years, scholars at home and abroad have developed a large number of data gloves based on optical fibers, bending sensors, gyroscopes and so on [1]. Jiang et al [13] based on data gloves and depth cameras, using an iterative fitting triangle mesh model to achieve For real-time measurement of hand movement, Huang et al [11] used optical fiber to develop a data glove that can detect the movement of 10 joints of the hand, and used it for the recognition of digital gestures and simple language gestures. Song et al [28] proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Sign language recognition based on skeleton with K3D-LSTMresidual networks

Han

Huangfu

Min

et al. 2021

Preprint

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Most existing deep learning-based dynamic sign language recognition methods directly use either the video sequence based on RGB information, or whole sequences instead of only the video sequence that represents the change of gesture. These characteristics lead to inaccurate extraction of hand gesture features and failure to achieve good recognition accuracy for complex gestures. In order to solve these problems, this paper proposes a new method of dynamic hand gesture recognition for key skeleton information, which combines residual convolutional neural network and long short-term memory recurrent network, which is called KLSTM-3D residual network (K3D ResNet). In K3DResNet, the spatiotemporal complexity of network computation is reduced by extracting the representative skeleton frame of gesture change. Then, the spatiotemporal features are extracted from the skeleton keyframe sequence, and the intermediate score corresponding to each action in the video sequence is established after the feature analysis. Finally, the classification of video sequences can accurately identify sign language. Experiments were performed on datasets DHG14/28 and SHREC’17 Track. The accuracy of verification on dataset DEVISIGN D reached 88.6%. In addition, the accuracy of the combination of RGB and skeleton information reached 93.2%.

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Section: Related Workmentioning

confidence: 99%

Sign language recognition based on skeleton with K3D-LSTMresidual networks

Han

Huangfu

Min

et al. 2021

Preprint

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“…The data glove-based interaction method [19]- [22] is extensively studied at the beginning. This method first measures various types of information of the hand through the glove, and then uses the measured information for the modeling and recognition of the gesture.…”

Section: Related Workmentioning

confidence: 99%

“…This method first measures various types of information of the hand through the glove, and then uses the measured information for the modeling and recognition of the gesture. For example, Huang et al [22] design and fabricate a textile glove by sewing reduced graphene oxide-coated fiber to monitor the motion of ten finger joints from one hand for gesture recognition. KITTY [19] is a glove-type input device that covers parts of the hand with electronic contacts.…”

Section: Related Workmentioning

confidence: 99%

A Novel Human-3DTV Interaction System Based on Free Hand Gestures and a Touch-Based Virtual Interface

Zhang

2019

IEEE Access

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The input method based on free-hand gestures has gradually become a hot research direction in the field of human-computer interaction. Hand gestures like sign languages, however, demand quite a lot of knowledge and practice for interaction, and air writing methods require their users to hold the arm and hand in mid-air for a period of time. These methods limit the user experience and get severer when a large number of gestures are required. To address the problem, this paper presents a novel human-3DTV interaction system based on a set of simple free-hand gestures for direct-touch interaction with a virtual interface to facilitate human-3DTV interaction. Specifically, our system projects a virtual interface in front of the user who wears the 3D shutter glass, and the user just stretches the arm and touches the virtual interface like performing on a smart phone with a touch screen, using gestures such as Click, Slide, Hold, Drag and Zoom In/Out. Our system is able to recognize the user's gesture fast and accurately, as the system only needs to search for a small region neighboring the virtual interface for a small set of gesture types. Because we adopt the key gestures using on smart phones, our free-hand gestures can be easily used by anyone with only a brief training. The users feel more comfortable than traditional gesture input methods and can effectively interact with 3DTV using our system. We report a comprehensive user study on accuracy and speed to validate the advantages of the proposed human-3DTV interaction system.

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“…Huang et al proposed a data glove by sewing the reduced grapheme oxide-coated fibers onto a textile glove. They carried out a static gesture recognition experiment and a dynamic gesture recognition experiment, and the recognition accuracy rates were 98.5% and 98.3%, respectively [ 13 ]. Michiko et al presented a sensing glove with hetero-core fiber-optic nerve sensors [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

Wang

Cao

et al. 2021

Micromachines

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Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.

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Tracing the Motion of Finger Joints for Gesture Recognition via Sewing RGO-Coated Fibers Onto a Textile Glove

Cited by 56 publications

References 25 publications

Sign language recognition based on skeleton with K3D-LSTMresidual networks

Sign language recognition based on skeleton with K3D-LSTMresidual networks

A Novel Human-3DTV Interaction System Based on Free Hand Gestures and a Touch-Based Virtual Interface

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

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