Learn More
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. Physiological and environmental parameters are monitored(More)
The Smart Personal Health Manager is a system designed to monitor physiological vital signs, and personal environments, while providing disease specific expert real time analysis and feedback to the common user via a personal device such as a PDA or cell phone. This demonstration will consist of the wearable body monitor, a personal environment monitor and(More)
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. With this classification, wrong physiological(More)
This paper presents a preliminary study of performance limitations that arise in the closed-loop control of blood glucose, using an autonomous artificial pancreas. It is shown that a major source of limitations is due to model uncertainty, specifically due to the combined effect of the insulin infusion system (IIS), the continuous glucose monitor (CGM) and(More)
A portable system to monitor environmental factors of pulmonary diseases is introduced in this paper. Disease triggers including temperature, humidity and airborne particles are continuously measured in real time. Data is transferred to a handheld device through a wireless body area network (BAN). If target triggers of disease are detected, the system will(More)
Wide use of continuous glucose monitoring (CGM) provides sufficient time-series sensor data, which covers sufficient knowledge about the underlying correlations of glucose concentrations and the progressing dynamics over time direction. From self-Monitoring of blood glucose to continuous glucose monitoring, sensor performance is key for successful clinical(More)
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(More)
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(More)
  • 1