Machine learning application for patients activity recognition with pressure sensing in bed
Pages 348 - 354
Abstract
Patient activity recognition in bed is very valuable to clinician to understand patient disease and drive clinical decisions. This paper proposes a recognition method based on the CNN (Convolutional Neural Network) to identify the action of bedridden patients. The inputs are 4 time series signals acquired from pressure sensors on the bed. Through CNN we obtain the corresponding membership of four pre-defined actions. A probability density analysis is made for setting a judgment standard, and ultimately recognizing the action. The method has been tested with real human activity signal and the results are promising.
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Published: 08 May 2018
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