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

Reiter, Austin; Andy, J.; Rawat, Nishi; Shrock, Christine; Saria, Suchi

doi:10.1007/978-3-319-46720-7_56

Cited by 8 publications

(12 citation statements)

References 25 publications

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“…We developed and validated a noninvasive sensor system to continuously measure patient mobility in the ICU (26). Physician and NIMS agreement regarding mobility level was very high.…”

Section: Discussionmentioning

confidence: 99%

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Andy

Rawat

Reiter

et al. 2017

Critical Care Medicine

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Objectives To develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU. Design Prospective, observational study. Setting Surgical ICU at an academic hospital. Patients Three hundred sixty-two hours of sensor color and depth image data were recorded and curated into 109 segments, each containing 1,000 images, from eight patients. Interventions None. Measurements and Main Results Three Microsoft Kinect sensors (Microsoft, Beijing, China) were deployed in one ICU room to collect continuous patient mobility data. We developed software that automatically analyzes the sensor data to measure mobility and assign the highest level within a time period. To characterize the highest mobility level, a validated 11-point mobility scale was collapsed into four categories: nothing in bed, in-bed activity, out-of-bed activity, and walking. Of the 109 sensor segments, the noninvasive mobility sensor was developed using 26 of these from three ICU patients and validated on 83 remaining segments from five different patients. Three physicians annotated each segment for the highest mobility level. The weighted Kappa (κ) statistic for agreement between automated noninvasive mobility sensor output versus manual physician annotation was 0.86 (95% CI, 0.72–1.00). Disagreement primarily occurred in the “nothing in bed” versus “in-bed activity” categories because “the sensor assessed movement continuously,” which was significantly more sensitive to motion than physician annotations using a discrete manual scale. Conclusions Noninvasive mobility sensor is a novel and feasible method for automating evaluation of ICU patient mobility.

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“…We developed and validated a noninvasive sensor system to continuously measure patient mobility in the ICU (26). Physician and NIMS agreement regarding mobility level was very high.…”

Section: Discussionmentioning

confidence: 99%

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Andy

Rawat

Reiter

et al. 2017

Critical Care Medicine

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“…The CV had an accuracy of 68.8% for quantifying the number of healthcare personnel involved in each activity [187]. Another study by Reiter et al (2016) developed an automated mobility sensor to support the monitoring of patient activity in the surgical ICU. They compared the algorithm's performance with clinician performance on the identification of physical activity and reported high inter-rater reliability with a weighted Kappa score of 0.86 [186].…”

Section: Mobilitymentioning

confidence: 99%

“…Another study by Reiter et al (2016) developed an automated mobility sensor to support the monitoring of patient activity in the surgical ICU. They compared the algorithm's performance with clinician performance on the identification of physical activity and reported high inter-rater reliability with a weighted Kappa score of 0.86 [186]. These types of models could automate documentation of physical activity in hospital patients, decreasing clinician documentation burden and increasing the accuracy of electronic health records.…”

Section: Mobilitymentioning

confidence: 99%

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

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“…Motion detectors have been repeatedly tested at the ICU for various indications as measuring patients' activity or mobility levels [8][9][10], assessing patients' sleep quality [11], or reducing the number of artefact alarms in vital sign monitoring [12]. To date, no study has examined whether motion detectors can be used to identify emergencies.…”

Section: Introductionmentioning

confidence: 99%

Motion Detectors as Additional Monitoring Devices in the Intensive Care Unit—A Proof-of-Concept Study

Güder,

von Rein,

Flohr

et al. 2023

Applied Sciences

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Background: Monitoring the vital signs of delirious patients in an intensive care unit (ICU) is challenging, as they might (un-)intentionally remove devices attached to their bodies. In mock-up scenarios, we systematically assessed whether a motion detector (MD) attached to the bed may help in identifying emergencies. Methods: We recruited 15 employees of the ICU and equipped an ICU bed with an MD (IRON Software GmbH, Grünwald, Germany). Participants were asked to replay 22 mock-up scenes of one-minute duration each: 12 scenes with movements and 10 without movements, of which 5 were emergency scenes (“lying dead-still, with no or very shallow breathing”). Blinded recordings were presented to an evaluation panel consisting of an experienced ICU nurse and a physician, who was asked to assess and rate the presence of motions. Results: Fifteen participants (nine women; 173 ± 7.0 cm; 78 ± 19 kg) joined the study. In total, 286 out of 330 scenes (86.7%) were rated correctly. Ratings were false negative (FN: “no movements detected, but recorded”) in 7 out of 180 motion scenes (3.9%). Ratings were false positive (FP: “movements detected, but not recorded”) in 37 out of 150 scenes (24.7%), more often in men than women (26 out of 60 vs. 11 out of 90, respectively; p < 0.001). Of note, in 16 of these 37 FP-rated scenes, a vibrating mobile phone was identified as a potential confounder. The emergency scenes were correctly rated in 64 of the 75 runs (85.3%); 10 of the 11 FP-rated scenes occurred in male subjects. Conclusions: The MD allowed for identifying motions of test subjects with high sensitivity (96%) and acceptable specificity (75%). Accuracy might increase further if activities are recorded continuously under real-world conditions.

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

Cited by 8 publications

References 25 publications

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Motion Detectors as Additional Monitoring Devices in the Intensive Care Unit—A Proof-of-Concept Study

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