Abstract
Nowadays a commercial product for sign language translation is still not available. This paper presents our latest results towards this goal, presenting a functional prototype called Talking Hands. Talking Hands uses a data-glove to detect the hand movements of the user, and a smartphone application to gather all the data and translates them into voice, using a speech synthesizer. Talking Hands implements the most suitable solutions for a massive production without penalizing its reliability. This paper presents the improvements of the last prototype in terms of hardware, software and design, together with a preliminary analysis for the translation of dynamic gestures through this device.
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References
Perkins, R., Battle, T., Edgerton, J., Mcneill, J.: A survey of barriers to employment for individuals who are deaf. J. Am. Deaf. Rehabil. Assoc. 49(1), 66–85 (2015)
Kim, H., Lee, S., Lee, D., Choi, S., Ju, J., Myung, H.: Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier. Sensors (Switzerland) 15(6), 2410–12427 (2015)
Hirafuji Neiva, D., Zanchettin, C.: Gesture recognition: a review focusing on sign language in a mobile context. Expert Syst. Appl. 103, 159–183 (2018)
Cooper, H., Pugeault, N., Bowden, R.: Reading the signs: a video based sign dictionary. In: IEEE International Conference Computer Vision workshops, ICCV 2011, Barcelona (2011)
Starner, T., Weaver, J., Pentland, A.: Real time american sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12) (1998)
Kelly, D., McDonald, J., Markham, C.: A person independent system for recognition of hand postures used in sign language. Pattern Recognit. Lett. 31, 1359–1368 (2010)
Yoon, H.S., Soh, J., Bae, Y.J., Seung Yang, H.: Hand gesture recognition using combined features of location, angle and velocity. Pattern Recognit. 37(4), 1491–1501 (2001)
Ahmed, M.A., Zaidan, B.B., Zaidan, A.A., Salih, M.M., Bin Lakulu, M.M.: A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors (Switzerland) 18(7) (2018)
Bajpai, D., Porov, U., Srivastav, G., Sachan, N.: Two way wireless data communication and American sign language translator glove for images text and speech display on mobile phone. In: Proceedings 2015 5th International Conference on Communication Systems and Network Technologies. CSNT 2015, pp. 578–585 (2015)
Bukhari, J., Rehman, M., Malik, S.I., Kamboh, A.M., Salman, A.: American sign language translation through sensory glove: SignSpeak. Int. J. u- e-Serv. Sci. Technol. 8, 131–142 (2015)
Shukor, A.Z., Miskon, M.F., Jamaluddin, M.H., Bin Ali Ibrahim, F., Asyraf, M.F., Bin Bahar, M.B.: A new data glove approach for malaysian sign language detection. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), vol. 76, pp. 60–67 (2015)
Seymour, M., Tsoeu, M.: A mobile application for South African Sign Language (SASL) recognition, pp. 1–5 (2015)
Kau, L.J., Su, W.L., Yu, P.J., Wei S.J.: A real-time portable sign language translation system. In: 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–4 (2015)
Devi, S., Deb, S.: Low cost tangible glove for translating sign gestures to speech and text in Hindi language. In: 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–5 (2017)
Pezzuoli, F., Corona, D., Corradini, M.L., Cristofaro, A.: Development of a wearable device for sign language translation. In: International Workshop on Human-Friendly Robotics (HFR2017), pp. 115–126 (2017)
Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Le Carpentier E.: A testing system for a real-time gesture classification using surface EMG. In: 20th IFAC World Congress, pp. 11498–11503 (2017)
Kouichi, M., Hitomi, T.: Gesture recognition using recurrent neural networks. In: ACM Conference on Human factors in computing systems: reaching through technology (1999)
Vogler, C.: American sign language recognition: reducing the complexity of the task with phoneme-based modeling and parallel hidden markov models. University of Pennsylvania (2003)
Li, X.: Gesture recognition based on fuzzy C-Means clustering algorithm. Department of Computer Science, The University of Tennessee, Knoxville
Nagi, J., et al.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 International Conference on Signal and Image Processing and Applications (ICSIPA), pp. 342–347 (2011)
Huynh, D.Q.: Metrics for 3D rotations: comparison and analysis. J. Math. Imaging Vis. 35, 155–164 (2009)
Ong, S.C.W., Ranganath, S.: Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Trans. Pattern Anal. Mach. Intell. 27(6) (2005)
Acknowledgments
This work is supported by Limix S.r.l. (www.limix.it). Limix is an Italian start-up and spin-off of the University of Camerino. The intellectual property of Talking Hands and its different parts (hardware, software, design) is of Limix S.r.l.
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Pezzuoli, F., Corona, D., Corradini, M.L. (2020). Improvements in a Wearable Device for Sign Language Translation. In: Ahram, T. (eds) Advances in Human Factors in Wearable Technologies and Game Design. AHFE 2019. Advances in Intelligent Systems and Computing, vol 973. Springer, Cham. https://doi.org/10.1007/978-3-030-20476-1_9
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DOI: https://doi.org/10.1007/978-3-030-20476-1_9
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