[3E5-GS-10] AI application: Medicine / Healthcare

[3E5-GS-10-04] Detection of Self-Extubation Movements in the Intensive Care Unit Using a Posture Estimation Model

〇So Mizuno¹, Fumio Ishizaki ², Aya Umeda³, Tatsuya Okamoto⁴ (1. Institute of Life Design Counseling, 2. Modal Stage Inc., 3. National College of Nursing / National Center for Global Health and Medicine, 4. National Center for Global Health and Medicine)

Keywords:Change Detection, Computer vision, Human pose estimation, Intensive care, Self-Extubation

Critically ill patients with several life-support tubes and equipment are admitted in intensive care units. Therefore, physical restraints are commonly used to prevent contingencies such as self-extubation. In this study, we attempted to detect postures leading to self-extubation from the security footage of the ward. For patient posture estimation, we used MediaPipe, which can detect the three-dimensional coordinates of each body part. Time series data representing changes in movement of each upper body part were obtained from 13 self-extubation videos for which consent was granted. The time series data were placed on the video for visual confirmation of the posture. In three cases where the deviation between the actual posture and the coordinate position estimated by MediaPipe was considered small, we confirmed whether changes in posture leading to self-extubation could be detected using the singular spectrum transformation method. Consequently, large change values were observed in all three cases at approximately the time they started the action of grabbing the tube. All three cases where large change values were obtained at the time of self-extubation had room lighting turned on, and they were all bright videos. Under certain conditions, the potential for detecting removal actions from image-based posture estimation was suggested.