Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces
| Autor: | Tara Kajaks, Gary Evans, Gordon Wong, Geoff R. Fernie, Tilak Dutta, Elham Dolatabadi, Pamela J. Holliday, Hisham Alshaer, Sharon Gabison |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
030504 nursing business.industry medicine.medical_treatment Patient orientation fungi food and beverages Bed rest bed position Pressure injuries 03 medical and health sciences machine learning 0302 clinical medicine Physical medicine and rehabilitation technology medicine Original Research Article 030212 general & internal medicine 0305 other medical science business |
| Zdroj: | Journal of Rehabilitation and Assistive Technologies Engineering |
| ISSN: | 2055-6683 |
| DOI: | 10.1177/2055668320912168 |
| Popis: | Introduction Prolonged 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. Methods Twenty 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. Results The Feed Forward Neural Network yielded the highest orientation prediction accuracy at 94.2%. Conclusions The 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. |
| Databáze: | OpenAIRE |
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