TY - GEN
T1 - Toileting Expression Detection System for Children with Disabilities Based on Feature Extraction Using Support Vector Machine
AU - Siregar, Salsabiela Khairunnisa
AU - Arifin, Achmad
AU - Hikmah, Nada Fitrieyatul
AU - Suprayitno, Eko Agus
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Children with disabilities often struggle to express their need to use the toilet, leading to health issues and inappropriate toileting behaviors. This study aims to identify toileting needs based on the facial expressions of children with disabilities using a camera system. Images captured during school activities were analyzed using 51 facial landmark points, focusing on angles, distances, and inclinations. The TOP5 and TOP10 datasets, based on features with the highest Pearson correlation values, were used in a Support Vector Machine (SVM) classification. The TOP5 model achieved a 96% accuracy using parameters C=25 and gamma=0.001, and 5-fold cross-validation. Despite strong performance, challenges such as data collection difficulties and misclassification errors need to be addressed. Increasing dataset diversity by involving subjects with various disabilities is essential for system improvement.
AB - Children with disabilities often struggle to express their need to use the toilet, leading to health issues and inappropriate toileting behaviors. This study aims to identify toileting needs based on the facial expressions of children with disabilities using a camera system. Images captured during school activities were analyzed using 51 facial landmark points, focusing on angles, distances, and inclinations. The TOP5 and TOP10 datasets, based on features with the highest Pearson correlation values, were used in a Support Vector Machine (SVM) classification. The TOP5 model achieved a 96% accuracy using parameters C=25 and gamma=0.001, and 5-fold cross-validation. Despite strong performance, challenges such as data collection difficulties and misclassification errors need to be addressed. Increasing dataset diversity by involving subjects with various disabilities is essential for system improvement.
KW - Computer Vision
KW - Disability Behaviour
KW - Disability Expression Classification
KW - Facial Landmarks
KW - Support Vector Machine
KW - Toileting for Disabilities
UR - https://www.scopus.com/pages/publications/86000017021
U2 - 10.1109/CENIM64038.2024.10882629
DO - 10.1109/CENIM64038.2024.10882629
M3 - Conference contribution
AN - SCOPUS:86000017021
T3 - Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024
BT - Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024
Y2 - 19 November 2024 through 20 November 2024
ER -