Physical contact-based typing interfaces are not suitable for people with upper limb disabilities such as Quadriplegia. This paper, thus, proposes a touch-less typing interface that makes use of an on-screen QWERTY keyboard and a front-facing smartphone cameramounted on a stand. The keys of the keyboard are grouped into nine color-coded clusters. Users pointed to the letters that they wanted to type just by moving their head. The head movements of the users are recorded by the camera. The recorded gestures are then translated into a cluster sequence. The translation module is implemented using CNN-RNN, Conv3D, and a modified GRU based model that uses pre-trained embedding rich in head pose features . The performances of these models were evaluated under four different scenarioson a dataset of 2234 video sequences collected from 22 users . The modified GRU-based model outperforms the standard CNN-RNN and Conv3D models for three of the four scenarios. The results are encouraging and suggest promising directions for future research .
In this paper, we propose a novel touchless typing interface that makes use of an on-screen QWERTY keyboard and a smartphone camera. The keyboard was divided into nine color-coded clusters. The user moved their head toward clusters, which contained the letters that they wanted to type . A front-facing smartphone camera recorded the head movements. A bidirectional GRU based model which used pre-trained embedding rich in head pose features was employed to translate the recordings into cluster sequences. The model achieved an accuracy of 96.78\% and 86.81\% under intra- and inter-user scenarios, respectively, over a dataset of 2234 video sequences collected from 22 users .
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Physical contact-based typing interfaces are not suitable for people with upper limb disabilities such as Quadriplegia. This paper, thus, proposes a touch-less
In this paper, we propose a novel touchless
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front-facing smartphone cameramounted on a stand. The keys of the keyboard are grouped
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Users pointed to
The user moved their head toward clusters, which contained
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just by moving their head. The head movements of the users are recorded by the camera. The recorded gestures are then translated into a cluster sequence. The translation module is implemented using CNN-RNN, Conv3D, and a modified
. A front-facing smartphone camera recorded the head movements. A bidirectional
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which used
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. The performances of these models were evaluated under four different scenarioson
was employed to translate the recordings into cluster sequences. The model achieved an accuracy of 96.78\% and 86.81\% under intra- and inter-user scenarios, respectively, over
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. The modified GRU-based model outperforms the standard CNN-RNN and Conv3D models for three of the four scenarios. The results are encouraging and suggest promising directions for future research