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.
Sensor-based smart spaces have been developed to help caring of the elderly. Selection of sensors, layout of smart space, design of fusion algorithms, and knowledge of domain experts are critical to the development of a useful ICT system for caring the elderly in the institutional facilities. We apply multi-modal sensor fusion technique to improve the confidence level of our system, and leverage the knowledge of domain experts, which is stored in ontology database, to detect the sequence of events that can lead to dangerous situations.
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