An Assistive Body Sensor Network Glove for Speech- and Hearing-Impaired Disabilities

Vutinuntakasame, Satjakarn; Jaijongrak, V-ris; Thiemjarus, Surapa

doi:10.1109/bsn.2011.13

Cited by 36 publications

(24 citation statements)

References 9 publications

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“…Automatic hand gesture recognition has been applied in various application domains, for example, surgical skill assessment [1], virtual reality [2], sign translation [3][4][5][6][7][8][9][10][11][12][13] and human-computer interface [14]. The gesture recognition techniques can be classified into three main categories based on the input modality, i.e.…”

Section: Introductionmentioning

confidence: 99%

Design of an Assistive Communication Glove Using Combined Sensory Channels

Tanyawiwat

Thiemjarus

2012

2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks

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This paper presents a new design of a wireless sensor glove developed for American Sign Language fingerspelling gesture recognition. Five contact sensors are installed on the glove, in addition to five flex sensors on the fingers and a 3D accelerometer on the back of the hand. Each pair of flex and contact sensors are combined into the same input channel on the BSN node in order to save the number of channels and the installation area. After which, the signal is analyzed and separated back into flex and contact features by software. With electrical contacts and wirings made of conductive fabric and threads, the glove design has become thinner and more flexible. For validation, ASL fingerspelling gesture recognition experiments have been performed on signals collected from six speech-impaired subjects and a normal subject. With the new sensor glove design, the experimental results have shown a significant increase in classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Design of an Assistive Communication Glove Using Combined Sensory Channels

Tanyawiwat

Thiemjarus

2012

2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks

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“…A classification rate of 94.07% accuracy was obtained on 1200 test patterns provided the network was trained on 5300 patterns. In [6], Vutinuntakasame et al recognized fingerspelling with 5 flex sensors and a 3-DOF accelerometer connected to a Body Sensor Network (BSN). They proposed a hierarchical framework using multivariate Gaussian distribution coupled with bigram and set of rules to detect a particular padgram with an accuracy of about 72.7-73.6%.…”

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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“…providing a measure of finger bending, as well as motion, orientation of the hand, recognition and a speech synthesizer as shown in Figure 1 [26]. The flex sensors placed along five fingers are used for detecting finger bending and the 3D accelerometer placed on the back of the hand is used for detecting hand orientation and motion.…”

Section: Attribute Based Encryption (Abe)mentioning

confidence: 99%

“…In [26], a framework for constructing the fingerspelling gesture recognition model based on the data acquired from a wireless BSN sensor glove is proposed. The glove consists of five flex sensors and a 3D accelerometer Data integrity is not provided when number of errors is more than the detecting capability.…”

Section: Sensor Network Used In Assistive Technologiesmentioning

confidence: 99%