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.

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The use of slip-resistant winter footwear is crucial for the prevention of slips and falls on ice and snow. The main objective of this paper is to evaluate a mechanical testing method to determine footwear slip resistance on wet and dry ice surfaces and to compare it with the human-centred test method introduced by researchers at KITE (Knowledge, Innovation, Talent, Everywhere)-Toronto Rehabilitation Institute-University Health Network. Phase 1 of this study assessed the repeatability and reproducibility of the mechanical method by evaluating ten different occupational winter boots using two SATRA Slip resistance testers (STM 603, SATRA Technology Centre, Kettering, UK). One tester is located in Toronto and one in Montreal. These boots were chosen based on the needs of the IRSST (Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail, Montréal, Quebec, Canada), who were primarily interested in providing safe winter footwear for police, firefighters and municipal workers. In Phase 2, the results of the human-centred test approach were compared with the mechanical results. In Phase 3, two of these boots with conflicting results from the previous phases were tested using a second human-centred method. In Phase 1, the mechanical testing results obtained in the two labs showed a high linear correlation (>0.94) and good agreement on both ice surfaces; however, they revealed a bias (~0.06) between the two labs on the dry ice condition. The mechanical and human-centred tests (phase 2) were found to be better correlated in the wet ice condition (R = 0.95) compared to the dry ice condition (R = 0.34). Finally, the rating of the footwear slip resistance based on the number of slips counted in phase 3 was consistent with the rating by the human-centred test method (phase 2), but not the mechanical method (phase 1). The findings of this study provide a better understanding of the limitations of the SATRA ice tray for measuring footwear slip resistance and demonstrate that the mechanical method must be further refined to make it more comparable to the human-centred methods to achieve better agreement with real-world performance.