Motion Detection Application to Measure Straight Leg Raise ROM Using MediaPipe Pose

Hustinawaty,; Rumambi, Tavipia; Hermita, Matrissya

doi:10.1109/icoris56080.2022.10031299

Cited by 2 publications

(1 citation statement)

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“…The process of using any implement, material, or device to immobilize the body in a manner that restricts an individual's freedom of movement in their environment or approaches that individual's physical freedom back pain by tracking joint positions in the body [14,15]. Designed specifically for RGB video footage, the Pose model annotates 33 key joint positions for precise measurement.…”

Section: Patient Movement Quantificationmentioning

confidence: 99%

An automated ICU agitation monitoring system for video streaming using deep learning classification

Dai,

Wu,

Sheu

et al. 2024

BMC Med Inform Decis Mak

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Objective To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. Methods We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as “Attention” and “Non-attention”. After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances. Results The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods. Conclusion Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.

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Section: Patient Movement Quantificationmentioning

confidence: 99%