Sign and Human Action Detection Using Deep Learning
Authors: Dhulipala, S., Adedoyin, F.F. and Bruno, A.
Journal: Journal of Imaging
Volume: 8
Issue: 7
eISSN: 2313-433X
DOI: 10.3390/jimaging8070192
Abstract:Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.
https://eprints.bournemouth.ac.uk/36858/
Source: Scopus
Sign and Human Action Detection Using Deep Learning.
Authors: Dhulipala, S., Adedoyin, F.F. and Bruno, A.
Journal: J Imaging
Volume: 8
Issue: 7
eISSN: 2313-433X
DOI: 10.3390/jimaging8070192
Abstract:Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model's performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.
https://eprints.bournemouth.ac.uk/36858/
Source: PubMed
Sign and Human Action Detection Using Deep Learning
Authors: Dhulipala, S., Adedoyin, F.F. and Bruno, A.
Journal: JOURNAL OF IMAGING
Volume: 8
Issue: 7
eISSN: 2313-433X
DOI: 10.3390/jimaging8070192
https://eprints.bournemouth.ac.uk/36858/
Source: Web of Science (Lite)
Sign and Human Action Detection using Deep Learning
Authors: Adedoyin, F., Bruno, A. and Dhulipala, S.
Journal: Journal of Imaging
Abstract:Human beings usually rely on communication to express their feeling, and ideas and solve disputes among them. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, or even vocals. It is usually essential for all the communicating parties to be fully conversant with a common language that they are using. However, this hasn’t been the case between speech impaired people who use sign language and the regular people in the society who use spoken languages. Different studies have pointed out a significant gap between these people and the regular people, limiting the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language. This is in an attempt to narrow this communication gap between the speech-impaired people and the regular people in the community. Two models were developed in the research, which includes CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training, and testing accuracies of 98.8% and 97.4%, respectively. The model also achieved average weighted precession, and recall was also 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with maximum training and testing, the achieved performance is 49.4% and 48.7%respectively. The research concluded that the CNN model was the best for recognizing and determining British sign language.
https://eprints.bournemouth.ac.uk/36858/
Source: Manual
Sign and Human Action Detection Using Deep Learning.
Authors: Dhulipala, S., Adedoyin, F.F. and Bruno, A.
Journal: Journal of imaging
Volume: 8
Issue: 7
Pages: 192
eISSN: 2313-433X
ISSN: 2313-433X
DOI: 10.3390/jimaging8070192
Abstract:Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model's performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.
https://eprints.bournemouth.ac.uk/36858/
Source: Europe PubMed Central
Sign and human action detection using deep learning
Authors: Dhulipala, S., Adedoyin, F. and Bruno, A.
Journal: Journal of Imaging
Volume: 8
Issue: 7
ISSN: 2313-433X
Abstract:Human beings usually rely on communication to express their feeling, and ideas and solve disputes among them. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, or even vocals. It is usually essential for all the communicating parties to be fully conversant with a common language that they are using. However, this hasn’t been the case between speech impaired people who use sign language and the regular people in the society who use spoken languages. Different studies have pointed out a significant gap between these people and the regular people, limiting the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language. This is in an attempt to narrow this communication gap between the speech-impaired people and the regular people in the community. Two models were developed in the research, which includes CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training, and testing accuracies of 98.8% and 97.4%, respectively. The model also achieved average weighted precession, and recall was also 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with maximum training and testing, the achieved performance is 49.4% and 48.7% respectively. The research concluded that the CNN model was the best for recognizing and determining British sign language.
https://eprints.bournemouth.ac.uk/36858/
Source: BURO EPrints