Context Aware Sensing for Health Monitoring

Health Monitoring systems with textile sensors offer more comfort compared to gel electrodes, however they tend to suffer from poor skin contact and motion artifacts. In order to improve the monitoring reliability, we propose to apply multiple sensors and context aware sensing. A context aware monitoring system can adapt monitoring according to changes in the environment and the behavior of people. In this paper, we propose a method with a Context Aware Selection Algorithm (CASA) to select the best signals obtained from multiple textile sensors, and thereby enhancing the reliability of electrocardio-gram (ECG) monitoring. In experiments with adult participants we found that CASA indeed enhances continuous monitoring by selecting the best quality ECG derivation which varies with the context, in this case the posture of the participant. CASA is suitable for embedding in the smart jacket, which is a comfortable monitoring system for babies admitted to the Neonatal Intensive Care Unit (NICU).