Infant Vomit Detection Using ShuffleNetV2

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題名:	Infant Vomit Detection Using ShuffleNetV2
作者:	Chang, Chuan-yu;Lin, Xin-hui;Hsu, Hui-huang
日期:	2023-11-14
上傳時間:	2024-01-08 12:06:12 (UTC+8)
摘要:	Infants often experience phenomena such as vomiting during the neonatal period. Typically, vomiting is when the food or milk in the baby's stomach comes out of the mouth after feeding, usually due to poor digestion or overeating. Therefore, this paper proposes a monitoring system to assist parents in being aware of whether their infant is vomiting even when they briefly divert their attention. The system consists of two stages: first, the utilization of YOLO5FACE for detecting the infant's face and capturing the baby's mouth region. Image processing techniques enhance color saturation in the captured mouth images. And then, ShuffleNetV2 extracts image features, and finally, frame differencing is utilized to determine if vomiting has occurred. Parents can be immediately notified of abnormal conditions by promptly detecting such incidents. The proposed method successfully and rapidly detects vomiting in videos with an accuracy of 98.30%.
關聯:	DASC-PICOM-CBDCOM-CYBERSCITECH 2023,　p.108-149
DOI:	10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361389
顯示於類別:	[資訊工程學系暨研究所] 會議論文

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