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.

DeepPredict: A deep predictive intelligence platform for patient monitoring.

A novel platform, DeepPredict, for predicting hospital bed exit events from video camera systems is proposed. DeepPredict processes video data with a deep convolutional neural network consisting of five main layers: a 1 × 1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event. Results for a model trained on 180 hours of data demonstrate accuracy, sensitivity, and specificity of 86.

Rapid trend detection for an ambulatory monitoring system.

An algorithm for rapid trend detection of physiological parameter is introduced for ambulatory monitoring applications. Kalman prediction error of monitored parameter is used to estimate the physiological status and detect rapid change. With this algorithm, rapid trend during ambulatory monitoring can be found to predict disease exacerbation; and it is also applied to identify outliers of measurement due to poor signal quality to avoid false alarms.

Signal quality classification for an ambulatory monitoring system.

A signal quality classification algorithm is presented to evaluate signal quality in ambulatory monitoring system. Acoustic based signal is classified as good signal, weak signal or noisy signal. Certain features in the acquired signal are extracted and analyzed to differentiate the class of signal quality.

Pulmonary disease management system with distributed wearable sensors.

A pulmonary disease management system with on-body and near-body sensors is introduced in this presentation. The system is wearable for continuous ambulatory monitoring. Distributed sensor data is transferred through a wireless body area network (BAN) to a central controller for real time analysis.