Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Abstract

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.

Figure 1

ICU noninvasive mobility sensor system. This diagram depicts the sensor system in an ICU room and example color (converted to grayscale for demonstration) and depth images captured by the sensors. The grayscale image on the left provides texture information for human/object detection. Faces are obscured, and the image is blurred for identity protection. The depth image on the right shows the distance from the camera to the human/object with darker gray pixels indicating areas closer to the camera, lighter gray pixels indicating areas farther away, and black pixels indicating that the depth camera cannot capture the distance values around those regions. The depth image provides complementary information for better human/object detection.

Figure 2

Algorithmic stages for automated mobility measurement. The images on the left include overlaid bounding boxes to indicate the positions of people in the patient room as detected by the sensor. The flow chart on the right shows the stages of the noninvasive mobility sensor algorithm.