Chapter title |
Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor
|
---|---|
Chapter number | 56 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46720-7_56 |
Pubmed ID | |
Book ISBNs |
978-3-31-946719-1, 978-3-31-946720-7
|
Authors |
Austin Reiter, Andy Ma, Nishi Rawat, Christine Shrock, Suchi Saria, Austin Reiter, Andy Ma, Nishi Rawat, Christine Shrock, Suchi Saria |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
Abstract |
Throughout a patient's stay in the Intensive Care Unit (ICU), accurate measurement of patient mobility, as part of routine care, is helpful in understanding the harmful effects of bedrest [1]. However, mobility is typically measured through observation by a trained and dedicated observer, which is extremely limiting. In this work, we present a video-based automated mobility measurement system called NIMS: Non-Invasive Mobility Sensor . Our main contributions are: (1) a novel multi-person tracking methodology designed for complex environments with occlusion and pose variations, and (2) an application of human-activity attributes in a clinical setting. We demonstrate NIMS on data collected from an active patient room in an adult ICU and show a high inter-rater reliability using a weighted Kappa statistic of 0.86 for automatic prediction of the highest level of patient mobility as compared to clinical experts. |
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United States | 4 | 31% |
Australia | 2 | 15% |
United Kingdom | 2 | 15% |
Spain | 1 | 8% |
Unknown | 4 | 31% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 10 | 77% |
Scientists | 2 | 15% |
Practitioners (doctors, other healthcare professionals) | 1 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 20% |
Researcher | 4 | 20% |
Student > Bachelor | 2 | 10% |
Professor | 1 | 5% |
Other | 1 | 5% |
Other | 2 | 10% |
Unknown | 6 | 30% |
Readers by discipline | Count | As % |
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Computer Science | 3 | 15% |
Engineering | 3 | 15% |
Medicine and Dentistry | 3 | 15% |
Nursing and Health Professions | 2 | 10% |
Arts and Humanities | 2 | 10% |
Other | 0 | 0% |
Unknown | 7 | 35% |