Abstract
Eye Tracking is an important research technique used to analyze the movement of the eye and to recognize a pattern. Eye Tracking is a frequently used tool to understand the prognosis of a disease. iAOI is an Artificial Neural Network model that predicts the Area or Region of Interest viewed by a participant depending on the eye movement data. An eye-tracking experiment is conducted for participants with Parkinson’s Disease and healthy controls for visual search tasks. From the eye movements recorded a higher-order dataset based on the Area of Interest is derived. This dataset is explored to understand the underlying AOI patterns for participants with Parkinson’s Disease. This prediction from iAOI help in understanding the ability to search for a region of interest by patients suffering from Parkinson’s Disease. iAOI predicts the viewed region of interest and how it deviates from the intended Area of Interest. iAOI provides offbeat visualizations that depict the higher-order Area of Interest. By applying the ANN model for this multi-class classification, an accuracy of 83% was observed.
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References
Doğan, M., Metin, Ö., Tek, E., Yumuşak, S., Öztoprak, K.: Speculator and influencer evaluation in stock market by using social media. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 4559–4566. IEEE (2020)
Guan, C., Liu, W., Cheng, J.Y.C.: Using social media to predict the stock market crash and rebound amid the pandemic: the digital ‘haves’ and ‘have-mores’. Ann. Data Sci. 9(1), 5–31 (2022)
Hiransha, M., Gopalakrishnan, E.A., Menon, V.K., Soman, K.: NSE stock market prediction using deep-learning models. Procedia Comput. Sci. 132, 1351–1362 (2018)
Jiao, P., Veiga, A., Walther, A.: Social media, news media and the stock market. J. Econ. Behav. Organ. 176, 63–90 (2020)
Khan, W., Ghazanfar, M.A., Azam, M.A., Karami, A., Alyoubi, K.H., Alfakeeh, A.S.: Stock market prediction using machine learning classifiers and social media, news. J. Ambient. Intell. Humaniz. Comput. 13, 3433–3456 (2022). https://doi.org/10.1007/s12652-020-01839-w
Krishnan, S., Amudha, J., Tejwani, S.: Gaze exploration index (GE i)-explainable detection model for glaucoma. IEEE Access 10, 74334–74350 (2022)
Kuttichira, D.P., Gopalakrishnan, E., Menon, V.K., Soman, K.: Stock price prediction using dynamic mode decomposition. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 55–60. IEEE (2017)
Li, D., et al.: Analyzing stock market trends using social media user moods and social influence. J. Am. Soc. Inf. Sci. 70(9), 1000–1013 (2019)
Menon, B., et al.: Parkinson’s disease, depression, and quality-of-life. Indian J. Psychol. Med. 37(2), 144–148 (2015). pMID: 25969597
Nair, B.B., et al.: Forecasting short-term stock prices using sentiment analysis and artificial neural networks. J. Chem. Pharm. Sci. 9(1), 533–536 (2016)
Nair, B.B., Minuvarthini, M., Sujithra, B., Mohandas, V.: Stock market prediction using a hybrid neuro-fuzzy system. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 243–247. IEEE (2010)
Piñeiro-Chousa, J., Vizcaíno-González, M., Pérez-Pico, A.M.: Influence of social media over the stock market. Psychol. Mark. 34(1), 101–108 (2017)
Radhakrishnan, S., Menon, U.K., Sundaram, K., et al.: Usefulness of a modified questionnaire as a screening tool for swallowing disorders in Parkinson disease: a pilot study. Neurol. India 67(1), 118 (2019)
Sasidharakurup, H., Melethadathil, N., Nair, B., Diwakar, S.: A systems model of Parkinson’s disease using biochemical systems theory. OMICS J. Integr. Biol. 21(8), 454–464 (2017)
Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V.K., Soman, K.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1643–1647. IEEE (2017)
Unnithan, N.A., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: A data-driven model approach for daywise stock prediction. In: Sridhar, V., Padma, M.C., Rao, K.A.R. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 545, pp. 149–158. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5802-9_14
Venugopal, D., Amudha, J., Jyotsna, C.: Developing an application using eye tracker. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), pp. 1518–1522 (2016)
Acknowledgement
We acknowledge the National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, India for providing the eye movement tracker for experimentation and for allowing us to study the eye movements of PD patients at NIMHANS.
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Akshay, S., Amudha, J., Narmada, N., Bhattacharya, A., Kamble, N., Pal, P.K. (2023). iAOI: An Eye Movement Based Deep Learning Model to Identify Areas of Interest. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_61
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