Review in Sign Language Recognition Systems

Al-Ahdal, M. Ebrahim; Nooritawati, Tahir

doi:10.1109/isci.2012.6222666

Cited by 21 publications

(4 citation statements)

References 10 publications

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“…Comparing with related works, our paper presents several advantages: less sensitivity to environmental noise due to use of sEMG signals, data acquisition being performed for a larger number of volunteers, and signals being acquired for a larger number of gestures. In this work, we obtained similar results using a larger number of gestures (14), without using a complex classifier algorithm, when compared to the few works listed in the state of the art, which used a smaller number of gestures. (The one that used the most had 10, but these were not SIS gestures; the two works with 5 SIS gestures were the next highest.…”

Section: Discussionsupporting

confidence: 68%

“…Usually, SIS systems use pattern recognition based on images or videos [11][12][13], and image systems are highly dependent on environmental factors, such as lighting, high contrast, shadows, and background color [14]. As the operation room presents high lighting and most equipment is metallic, it can be difficult to operate the classification system.…”

Section: Introductionmentioning

confidence: 99%

“…Example of sEMG signals obtained from the chosen 14 SIS. The gestures from (1) to(14) are the same as presented in Figure1:(1) Compress, (2) Medical Thread on a Spool, (3) Medical Thread Loose, (4) Plier Backhaus, (5) Hemostatic Forceps, (6) Kelly Hemostatic Forceps, (7) Farabeuf Retractor, (8) Bistouri, (9) Needle Holder,(10) Valve Doyen,(11) Allis Clamp, (12) Anatomical Tweezers, (13) Rat's Tooth Forceps, and (14) Scissors.…”

mentioning

confidence: 99%

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Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals

Freitas

Mendes

Dias

et al. 2023

Sensors

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Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals

Freitas

Mendes

Dias

et al. 2023

Sensors

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“…Recently, there has been signifcant interest in the recognition and translation of sign language using RGB cameras alone [5,6,8,13,14]. Yin et al [30] showed state-of-the-art translation of German Sign Language.…”

Section: Past Workmentioning

confidence: 99%

CopyCat: Using Sign Language Recognition to Help Deaf Children Acquire Language Skills

Bansal

Ravi

et al. 2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Deaf children born to hearing parents lack continuous access to language, leading to weaker working memory compared to hearing children and deaf children born to Deaf parents. CopyCat is a game where children communicate with the computer via American Sign Language (ASL), and it has been shown to improve language skills and working memory. Previously, CopyCat depended on unscalable hardware such as custom gloves for sign verifcation, but modern 4K cameras and pose estimators present new opportunities. Before re-creating the CopyCat game for deaf children using of-the-shelf hardware, we evaluate whether current ASL recognition is sufcient.Using Hidden Markov Models (HMMs), user independent word accuracies were 90.6%, 90.5%, and 90.4% for AlphaPose, Kinect, and MediaPipe, respectively. Transformers, a state-of-the-art model in natural language processing, performed 17.0% worse on average.Given these results, we believe our current HMM-based recognizer can be successfully adapted to verify children's signing while playing CopyCat. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; Accessibility technologies; • Applied computing → Computer-managed instruction; • Computing methodologies → Machine learning; Feature selection.

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GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation

Nagendraswamy

Kumara

Chinmayi

2015

Mining Intelligence and Knowledge Exploration

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Review in Sign Language Recognition Systems

Cited by 21 publications

References 10 publications

Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals

Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals

CopyCat: Using Sign Language Recognition to Help Deaf Children Acquire Language Skills

GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation

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