Zhu Y, Zeng Y, Huang D, Huang J, Lu H, Wang W. Occlusion-robust Sleep Posture Detection using Body Rolling Motion in a Video.
ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;
2023:1-5. [PMID:
38082939 DOI:
10.1109/embc40787.2023.10340050]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
It has been reported that the monitoring of sleep postures is useful for the treatment and prevention of sleep diseases such as obstructive sleep apnea and heart failure. Camera-based sleep posture detection is attractive for the nature of comfort and convenience of use. However, the main challenge is to detect postures from images of the body that are occluded by bed sheets or covers. To address this issue, we propose a novel occlusion-robust sleep posture detection method exploiting the body rolling motion in a video. It uses the head orientation to indicate the posture direction (supine, left or right lateral), triggered by the full-body rolling motion (as a sign of posture change). The experimental results show that our proposed method, as compared with the state-of-the-art approaches such as skeleton-based (MediaPipe) and full-image ResNet based methods, obtained clear improvements on sleep posture detection with heavy body occlusions, with an averaged precision, recall and F1-score of 0.974, 0.993 and 0.983, respectively. The next step is to integrate the sleep posture detection algorithm into a camera-based sleep monitoring system for clinical validations.
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