ArticleJournal of rehabilitation and assistive technologies engineering
Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Understanding Human Motion from Depth Sensors: Activity Recognition and Age Group Recognition Using Skeleton Data.Sensors (Basel, Switzerland) · 2026Article
- Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management.Sensors (Basel, Switzerland) · 2024Article
- Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers.Bioengineering (Basel, Switzerland) · 2023Article
- Measuring Repositioning in Home Care for Pressure Injury Prevention and Management.Sensors (Basel, Switzerland) · 2022Article
- Monitoring muscle activity in pediatric SCI: Insights from sensorized rocking chairs and machine-learning.Journal of rehabilitation and assistive technologies engineeringArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionProlonged bed rest without repositioning can lead to pressure injuries. However, it can be challenging for caregivers and patients to adhere to repositioning schedules. A device that alerts caregivers when a patient has remained in the same orientation for too long may reduce the incidence and/or severity of pressure injuries. This paper proposes a method to detect a person's orientation in bed using data from load cells placed under the legs of a hospital grade bed.
methodsTwenty able-bodied individuals were positioned into one of three orientations (supine, left side-lying, or right side-lying) either with no support, a pillow, or a wedge, and the head of the bed either raised or lowered. Breathing pattern characteristics extracted from force data were used to train two machine learning classification systems (Logistic Regression and Feed Forward Neural Network) and then evaluate for their ability to identify each participant's orientation using a leave-one-participant-out cross-validation.
resultsThe Feed Forward Neural Network yielded the highest orientation prediction accuracy at 94.2%.
conclusionsThe high accuracy of this non-invasive system's ability to a participant's position in bed shows potential for this algorithm to be useful in developing a pressure injury prevention tool.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.