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
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)
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
Minu, R.I., Ezhilarasi, R.: Automatic emotion recognition and classification. Procedia Eng. 38, 21–26 (2012)
Giakoumis, D., et al.: Using activity-related behavioural features towards more effective automatic stress detection. PloS One 7, e43571 (2012)
Shahin, M.A., Ahmed, B., Ballard, K.J.: Classification of lexical stress patterns using deep neural network architecture, December 2014
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)
Aigrain, J., Dapogny, A., Bailly, K., Dubuisson, S., Detyniecki, M., Chetouani, M.: On leveraging crowdsourced data for automatic perceived stress detection, October 2016
Thelwall, M.: Tensistrength: stress and relaxation magnitude detection for social media texts. Inf. Process. Manag. 53(1), 106–121 (2017)
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)
Bay, Y., Erbilek, M., Celebi, E.: Emotional state prediction from online handwriting and signature biometrics. IEEE Access 1 (2019)
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)
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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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