AirPen: A Touchless Fingertip Based Gestural Interface for Smartphones and Head-Mounted Devices

@article{Jain2019AirPenAT,
  title={AirPen: A Touchless Fingertip Based Gestural Interface for Smartphones and Head-Mounted Devices},
  author={Varun Jain and Ramya Hebbalaguppe},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.06122}
}
Hand gestures are an intuitive, socially acceptable, and a non-intrusive interaction modality in Mixed Reality (MR) and smartphone based applications. Unlike speech interfaces, they tend to perform well even in shared and public spaces. Hand gestures can also be used to interact with smartphones in situations where the user's ability to physically touch the device is impaired. However, accurate gesture recognition can be achieved through state-of-the-art deep learning models or with the use of… CONTINUE READING

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Key Quantitative Results

  • The overall framework works in real-time on mobile devices and achieves a classification accuracy of 80% with an average latency of only 0.12 s.
  • LSTM and Bi-LSTM achieve classification accuracy of 92.5% and 94.3% respectively, outperforming the traditional approaches that are being used for similar classification tasks.

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