Detection and classification methodology for movements in the bed that supports continuous pressure injury risk assessment and repositioning compliance

Abstract

Pressure injuries are costly to the healthcare system and mostly preventable, yet incidence rates remain high. Recommendations for improved care and prevention of pressure injuries from the Joint Commission revolve around continuous monitoring of prevention protocols and prompts for the care team. The E-scale is a bed weight monitoring system with load cells placed under the legs of a bed. This study investigated the feasibility of the E-scale system for detecting and classifying movements in bed which are relevant for pressure injury risk assessment using a threshold-based detection algorithm and a K-nearest neighbor classification approach. The E-scale was able to detect and classify four types of movements (rolls, turns in place, extremity movements and assisted turns) with > 94% accuracy. This analysis showed that the E-scale could be used to monitor movements in bed, which could be used to prompt the care team when interventions are needed and support research investigating the effectiveness of care plans.

Pressure Ulcer Monitoring Platform—A Prospective, Human Subject Clinical Study to Validate Patient Repositioning Monitoring Device to Prevent Pressure Ulcers

“…For example, pressure pad-based devices typically use an array of pressure sensors, which is costly compared to only four load cells that are used in PUMP2. There exist alternative under-bed load cells to PUMP2; however, these solutions are only validated by predefined movements among healthy subjects in a lab environment [11][12][13][14] with accuracy ranges 74.9-97%. Furthermore, existing chest-worn devices come into direct contact with patient skin opposed to the PUMP1 device that does not come into patient skin contact.…”

“…Current solutions include mattressbased, loadcell-based technologies and wearable sensors to detect and confirm repositioning (e.g., earlysense, LEAF). [8][9][10][11][12][13][14] Although the reported detection accuracy is high, most of these systems have been validated only with predefined movements performed by healthy subjects in a lab environment. Our study validates two monitoring systems (PUMP1 and PUMP2) in hospital rooms with immobile patients over a period of 10 -2 h. One of the systems is a wearable sensor, and the other is a loadcell-based system.…”

Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces

“…There are a number of intelligent systems that have been developed to identify the need for patient repositioning in bed as a basis for providing prompts to caregivers. 7,[25][26][27][28][29][30] Existing commercially available systems fall into one of two categories: pressure mats and inertial sensors.…”

“…With this design, the device does not come into direct contact with the patient, which minimizes demands on healthcare providers, and would likely cost less to purchase, install, and maintain. This project builds on the work of Beattie et al 27 and Duvall et al, 26,27 who have investigated similar systems.…”

“…The authors demonstrated this change in angle can be used to detect the patient's orientation with 83% accuracy. 27 Duvall et al 26 used a similar load cell-based system to detect and classify four-patient movements in bed (rolls, turns in place, extremity movements, and assisted turns). They found a machine learning K-nearest neighbor classification system was able to classify the four movements with 94.2% accuracy.…”