Abstract
In this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The proposed system achieves an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, thus the novelty in this study is both on phases segmentation and experimental setup.
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Acknowledgments
This work is within the BESIDE project (no. YJTGRA7) funded by the Regione Puglia POR Puglia FESR - FSE 2014-2020. Fondo Europeo Sviluppo Regionale. Azione 1.6 - Avviso pubblico “InnoNetwork”.
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Dentamaro, V., Impedovo, D., Pirlo, G. (2020). Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. 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_52
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