@article{10902/31730,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10902/31730},
abstract = {Dementia represents one of the great problems to be solved in medicine for a society that is becoming increasingly long-lived. One of the main causes of dementia is Alzheimer's disease, which accounts for 80% of cases. There is currently no cure for this disease, although there are treatments to try to alleviate its effects, which is why detecting Alzheimer's disease in its early stages is crucial to slow down its evolution and thus help sufferers. One of the symptoms of the disease that manifests in its early stages is apraxia, difficulties in carrying out voluntary movements. In the clinical setting, apraxia is typically assessed by asking the patient to imitate hand gestures that are performed by the examiner. To automate this test, this paper proposes a system that, based on a video of the patient making the gesture, evaluates its execution. This evaluation is done in two steps, first extracting the skeleton of the hands and then using a similarity function to obtain an objective score of the execution of the gesture. The results obtained in an experiment with several patients performing different gestures are shown, showing the effectiveness of the proposed method. The system is intended to serve as a diagnostic tool, enabling medical experts to detect possible mobility impairments in patients that may have signs of Alzheimer's disease.},
organization = {For Alicia Nieto-Reyes this research was funded by Grant No. 21.VP67.64662 of the "Proyectos Puente 2022" from the Spanish "Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria". Santos Bringas was supported by University of Cantabria, trial doctorate Grant (DI27), awarded in the 2020 Industrial
doctorate program. For Rafael Duque this research was funded by Grant No. 21.VP50.64662 of the "Proyectos Puente 2021" from the "Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria". Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.},
publisher = {Springer},
publisher = {Machine Vision and Applications, 2023, 34(4), 60},
title = {Automatic apraxia detection using deep convolutional neural networks and similaritymethods},
author = {Vicedo, Cristina and Nieto Reyes, Alicia and Bringas Tejero, Santos and Duque Medina, Rafael and Lage Martínez, Carmen and Montaña Arnaiz, José Luis},
}