Cumulative discomfort of preterm infants can lead to abnormal development. An automated video-based discomfort detection system is proposed by analyzing motion patterns. We employ optical flow to estimate body motion across video-frames. Log Mel-spectrogram, Mel Frequency Cepstral Coefficients, and Spectral Subband Centroid Frequency features are calculated from 1D motion signals. These features enable 1D motion signals to be represented by 2D time-frequency images. Finally, deep CNNs are used on the 2D images for comfort/discomfort classification. The model was evaluated using leave-one-infant-out cross-validation on 183 video segments recorded during 17 heel prick events. Experimental results showed an AUC value of 0.985.
|Titel||SPIE Medical Imaging|
|Status||Gepubliceerd - 2020|
|Evenement||2020 SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, Verenigde Staten van Amerika|
Duur: 16 feb 2020 → 19 feb 2020
|Congres||2020 SPIE Medical Imaging|
|Land||Verenigde Staten van Amerika|
|Periode||16/02/20 → 19/02/20|