A Data-Driven Human Activity Classification Method for an Intelligent Hospital Bed.

Bedridden patients always need more attention in order to prevent unexpected falling, bedsores and other dangerous situations in daily care. This work proposes a data-driven classification method to recognize different bed-related human activities including exiting the bed, turning over, stretching out for something on the bedside table, sitting up and lying down by analyzing the real-time signals that are acquired from four load cells installed around the hospital bed. Considering the dynamic characteristics of the signals, dynamic principal component analysis (DPCA), here serving as a pre-processing step, is firstly utilized to extract both static and dynamic relations from the variables. Then the final statistical model for each class is established by Gaussian mixture model (GMM) with Figueiredo-Jain algorithm that can optimally select the number of components. An alarm will be triggered when a noteworthy action is detected. The proposed method has achieved superior performance using the experimental data from 10 adult volunteers. The results move a step forward towards the design of an intelligent hospital bed for practical applications.