TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Fahmy Hafe, Omar
A1  - Nosseir, Ann
A1  - Seepold, Ralf
A1  - Martínez Madrid, Natividad
T1  - Digital detection of attention and distraction behaviors
T2  - Procedia computer science
N2  - Paying attention helps us learn, advance in our careers, and build successful relationships, but when it’s compromised, achievement of any kind becomes far more challenging. Causes of not paying attention can range from common factors like sleep deprivation, stress, or a mood disorder to health difficulties such as ADHD, OCD, or a thyroid problem that affects concentrating. This work extracts paying attention and not paying attention behavior patterns in the context of learning. In early work, our study identified attention and distraction behaviors using gathered video recordings of online classes. The work found ten paying attention behaviors and six distracted behavior patterns. In this paper, we use computer vision techniques to extract features related to these behaviors. These features are distance between hand and face, pitch yaw roll, eye-to-camera distance, hand-to-camera distance, iris direction, gaze tracking, mouth aspect ratio, eye aspect ratio, distance between face and frame side, and facial landmark configuration. This research also applied three types of machine learning—logistic regression, decision trees, and random forest—and the accuracy rates were 79%, 86%, and 89%, respectively. This result is better than relying only on two extracted features in our previous work.
KW  - attention behviour
KW  - computer vison
KW  - online sessions
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-53077
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2024.09.332
DO  - https://doi.org/10.1016/j.procs.2024.09.332
VL  - 246
SP  - 4673
EP  - 4682
S1  - 10
PB  - Elsevier
CY  - Amsterdam
ER  -