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Automating Stress Detection from Handwritten Documents

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Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Abstract

Stress is a crucial problem in life, which is growing because of the fast-paced and demanding modern-life. Meanwhile, stress must be detected at early stages to prevent the negative effects on human health. Graphologist who analyze human handwriting have been able to detect stress from human handwriting. This is by extracting some features from the handwriting to detect stress level. The manual stress detection process is expensive, tedious and exhausting. This made the automation of the stress detection system important. Little research has been done on this field. In this research we will focus on automating stress detection from handwritten documents. We are working with graphologist to create a database of handwritten documents for stress detection. Later we will experiment different features to automate stress detection from the person’s handwriting.

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References

  1. Alberdi, A., Aztiria, A., Basarab, A.: Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J. Biomed. Inform. 59, 49–75 (2016)

    Article  Google Scholar 

  2. Binali, H., Wu, C., Potdar, V.: Computational approaches for emotion detection in text. In: 4th IEEE International Conference on Digital Ecosystems and Technologies, pp. 172–177, April 2010

    Google Scholar 

  3. Minu, R.I., Ezhilarasi, R.: Automatic emotion recognition and classification. Procedia Eng. 38, 21–26 (2012)

    Article  Google Scholar 

  4. Giakoumis, D., et al.: Using activity-related behavioural features towards more effective automatic stress detection. PloS One 7, e43571 (2012)

    Article  Google Scholar 

  5. Shahin, M.A., Ahmed, B., Ballard, K.J.: Classification of lexical stress patterns using deep neural network architecture, December 2014

    Google Scholar 

  6. Blanco-Gonzalo, R., Sanchez-Reillo, R., Miguel-Hurtado, O., Bella-Pulgarin, E.: Automatic usability and stress analysis in mobile biometrics. Image Vis. Comput. 32(12), 1173–1180 (2014)

    Article  Google Scholar 

  7. Aigrain, J., Dapogny, A., Bailly, K., Dubuisson, S., Detyniecki, M., Chetouani, M.: On leveraging crowdsourced data for automatic perceived stress detection, October 2016

    Google Scholar 

  8. Thelwall, M.: Tensistrength: stress and relaxation magnitude detection for social media texts. Inf. Process. Manag. 53(1), 106–121 (2017)

    Article  Google Scholar 

  9. Likforman-Sulem, L., Esposito, A., Faundez-Zanuy, M., ClémençSon, S., Cordasco, G.: EMOTHAW: a novel database for emotional state recognition from handwriting and drawing. IEEE Trans. Hum.-Mach. Syst. 47, 273–284 (2017)

    Article  Google Scholar 

  10. Bay, Y., Erbilek, M., Celebi, E.: Emotional state prediction from online handwriting and signature biometrics. IEEE Access 1 (2019)

    Google Scholar 

  11. Khayyat, M., Lam, L., Suen, C.Y., Yin, F., Liu, C.L.: Arabic handwritten text line extraction by applying an adaptive mask to morphological dilation. In: 10th IAPR International Workshop on Document Analysis Systems (DAS 2012), Gold Coast, Queenslands, Australia, 27–29 March 2012, pp. 100–104 (2012)

    Google Scholar 

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Correspondence to Najla AL-Qawasmeh or Muna Khayyat .

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AL-Qawasmeh, N., Khayyat, M. (2020). Automating Stress Detection from Handwritten Documents. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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