Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.
Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally, some works address the problem from either pressure sensor or video image. This paper presents a multimodal approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate that the multimodal approach achieves better performance than the approaches using single modal sensing.
Pressure ulcers are a common problem among patients with limited mobility, such as those bedbound and wheelchair-bound. Constant and prolonged applied pressure is one of the extrinsic factors contributing to the development of pressure ulcers. Analyzing lying postures together with interface pressure measurements from a pressure sensitive bed helps revealing pressure hot spots that can potentially lead to pressure ulcer development. We propose an intelligent system that features lying posture classification with pressure hot spots identification based on interface pressure measurements to possibly identify potential pressure ulcer risk and to provide effective preventive measures. Experimental outcomes correctly classify different lying postures with an accuracy of up to 93%. The proposed system is expected to assist caregivers to detect risk evidence and to provide timely and appropriate interventions for effective pressure ulcer prevention.
Due to the decline in physical and cognitive abilities, many frail elderly may have to lie in the bed most of their time. It is not feasible to monitor them continuously through manual observations alone. This issue can be resolved by embedding a set of multimodal sensors into the bed and providing automated activity recognition intelligence. But it is important to design and develop such multimodal sensing intelligence system desirable to the demands made by the clinicians. This paper presents the comparison and evaluation of different sensing bed configurations to observe different granularities of patient's contexts and activities in and around the bed. Based on the achievements and lessons learned from the experimental analysis, we propose improved sensing bed hardware and software systems to meet the real needs of in and around the bed patient monitoring.
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