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Related Experiment Video

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Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

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Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a

Austin Reiter¹, Andy Ma¹, Nishi Rawat²

¹The Johns Hopkins University, Baltimore, MD, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|November 25, 2017

Summary

This study introduces an automated video-based system for measuring patient mobility in Intensive Care Units (ICUs). The novel system accurately tracks patients, offering a reliable alternative to manual observation for assessing mobility levels.

Keywords:

Activity recognition Patient safety Tracking

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Published on: July 27, 2018

Area of Science:

Clinical Engineering
Biomedical Informatics
Rehabilitation Technology

Background:

Accurate patient mobility measurement in Intensive Care Units (ICUs) is crucial for understanding the negative impacts of prolonged bedrest.
Current mobility assessment relies on manual observation, which is labor-intensive and highly limiting in clinical settings.

Purpose of the Study:

To develop and validate a novel automated system for objective and continuous measurement of patient mobility within an ICU environment.
To address the limitations of manual mobility assessment by introducing a video-based solution.

Main Methods:

Development of a novel multi-person tracking methodology capable of handling complex ICU environments, including occlusions and variations in human pose.
Application of human-activity attributes for analyzing patient movement patterns from video data.
Demonstration of the system's performance using data collected from an active adult ICU patient room.

Main Results:

The automated system demonstrated high accuracy in predicting the highest level of patient mobility.
Achieved a high inter-rater reliability score of 0.86 (weighted Kappa statistic) when compared to clinical experts' assessments.
The system effectively handles challenges like occlusion and pose variations in real-world ICU settings.

Conclusions:

The presented video-based automated mobility measurement system offers a reliable and objective method for assessing patient mobility in ICUs.
This technology has the potential to enhance routine patient care by providing continuous and accurate mobility data, aiding in the management of bedrest effects.