An Interactive System for Sensory and Gustatory Impaired People Based On Hand Gesture Recognition

Elakkiya, R.; Selvamani, K.; Kanimozhi, S.; Velumadhava, Rao.; Senthilkumar, J.

doi:10.1016/j.proeng.2012.06.367

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Nonetheless, for the signer, the usage of these sensors is somewhat uncomfortable and extremely limiting ( Elakkiya et al, 2012 ). In addition, due to the high cost of sensors, practical applications of sensor-based SLR systems are impractical.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. Results This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. Conclusion Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.

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Section: Introductionmentioning

confidence: 99%

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…59,78,67,81 Thus, these interfaces make technologies more usable, as they ease interaction with devices such as smartphones and tablets, 69 allow integration in different environments, 34 facilitate the interaction with different systems 86 and make technologies more accessible, especially for disabled people. 107,20 The performance of spoken language dialog systems has improved over time, extending the initial application domains to complex information retrieval and question answering applications, 71 surveys applications, 100 e-commerce systems, 105 recommendations systems, 85 e-learning and tutoring systems, 54 in-car systems, 38 spoken dialog within vehicles, 73 remote control of devices and robots in smart envi-ronments, 72 Ambient Assisted Living systems, 8 or embodied dialog systems and companions. 39 The described systems are usually designed adhoc for their specific domain using rule-based dialog models and standards in which developers must specify the steps to be followed by the system.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Griol

Iglesias

Ledezma

et al. 2016

Int. J. Neur. Syst.

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This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

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Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition

Elakkiya

Selvamani

2017

J Med Syst

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Subunit segmenting and modelling in medical sign language is one of the important studies in linguistic-oriented and vision-based Sign Language Recognition (SLR). Many efforts were made in the precedent to focus the functional subunits from the view of linguistic syllables but the problem is implementing such subunit extraction using syllables is not feasible in real-world computer vision techniques. And also, the present recognition systems are designed in such a way that it can detect the signer dependent actions under restricted and laboratory conditions. This research paper aims at solving these two important issues (1) Subunit extraction and (2) Signer independent action on visual sign language recognition. Subunit extraction involved in the sequential and parallel breakdown of sign gestures without any prior knowledge on syllables and number of subunits. A novel Bayesian Parallel Hidden Markov Model (BPaHMM) is introduced for subunit extraction to combine the features of manual and non-manual parameters to yield better results in classification and recognition of signs. Signer independent action aims in using a single web camera for different signer behaviour patterns and for cross-signer validation. Experimental results have proved that the proposed signer independent subunit level modelling for sign language classification and recognition has shown improvement and variations when compared with other existing works.

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An Interactive System for Sensory and Gustatory Impaired People Based On Hand Gesture Recognition

Cited by 4 publications

References 8 publications

Recognition of Urdu sign language: a systematic review of the machine learning classification

Recognition of Urdu sign language: a systematic review of the machine learning classification

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition

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