Glove-Based Gesture Recognition System

Cabrera, María Eugenia; Bogado, Juan M.; Fermín, Leonardo; Acuña, Raul; Ralev, Dimitar

doi:10.1142/9789814415958_0095

Cited by 27 publications

(14 citation statements)

References 3 publications

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“…[4]. In another proposed method, data captured by gloves is sent to the neural network and is processed for classification [5]. InerTouchHand System is proposed for Human Machine Interaction (HMI) and uses distributed inertial sensors, vibro-tactile simulators [14].…”

Section: Related Workmentioning

confidence: 99%

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Web Based Recognition and Translation of American Sign Language with CNN and RNN

et al. 2021

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Individuals with hearing hindrance utilize gesture based communication to exchange their thoughts. Generally hand movements are used by them to communicate among themselves. But there are certain limitations when they communicate with other people who cannot understand these hand movements. There is a need to have a mechanism that can act as a translator between these people to communicate. It would be easier for these people to interact if there exists direct infrastructure that is able to convert signs to text and voice messages. As of late, numerous such frameworks for gesture based communication acknowledgment have been developed. But most of them are made either for static gesture recognition or dynamic gesture recognition. As sentences are generated using combinations of static and dynamic gestures, it would be simpler for hearing debilitated individuals if such computerized frameworks can detect both the static and dynamic motions together. We have proposed a design and architecture of American Sign Language (ASL) recognition with convolutional neural networks (CNN). This paper utilizes a pretrained VGG-16 architecture for static gesture recognition and for dynamic gesture recognition, spatiotemporal features were learnt with the complex architecture, called deep learning. It contains a bidirectional convolutional Long Short Term Memory network (ConvLSTM) and 3D convolutional neural network (3DCNN) and this architecture is responsible to extract 2D spatio temporal features.

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Section: Related Workmentioning

confidence: 99%

“…Software based solutions require image processing before classifying gesture images. Amazon Alexa also is able to respond to sign language gestures [5]. But in this system, you have to capture yourself repeatedly performing each sign every time you launch the site in the browser and this is a very tedious task.…”

Section: Related Workmentioning

confidence: 99%

Web Based Recognition and Translation of American Sign Language with CNN and RNN

et al. 2021

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“…A1. Maria Eugenia Cabrera, Juan Manuel Bogado, Leonardo Fermin, Raul Acuna and Dimitar Ralev [5] proposed a system wherein they perfected a Glove based Hand Gesture Recognition System. In this paper, they have presented a unique Glove, which has accelerometer sensors attached on it.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Home Automation using Hand Gesture Recognition System

Nadar,

Sinha,

Ratre

2019

IJEAT

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 Abstract: 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 UnoMicrocontroller, which in turn is connected to the personal computer where the actual gesture recognition is implemented.

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“…In [5], the 5DT Data Glove 5 Ultra along with an accelerometer was used to obtain each finger's flexion degree and information about wrist orientation. These readings afterwards were processed offline with an artificial neural network to test classification of 24 ASL static hand gestures for fingerspelling.…”

Section: Related Workmentioning

confidence: 99%

“…These sensors are generally straightforward to integrate in fabric, but offer less precision as they measure changes in the bending of the fingers, and suffer from hysteresis effects and accumulating measurement errors [5][6][7]. Few systems have thus far utilized fully integrated sensor chips that contain tri-axial accelerometers, gyroscopes, and magnetometers on all fingers, while none have been designed or evaluated for distinguishing different hand articulations on the glove itself in real-time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

et al. 2018

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This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user's hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.

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Glove-Based Gesture Recognition System

Cited by 27 publications

References 3 publications

Web Based Recognition and Translation of American Sign Language with CNN and RNN

Web Based Recognition and Translation of American Sign Language with CNN and RNN

Smart Home Automation using Hand Gesture Recognition System

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

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