@inbook{1a1f385a48a141389fca7d38d34005cf,
title = "Real-time pervasive monitoring for postoperative care",
abstract = "Post surgical care is an important part of the surgical recovery process. With the introduction of minimally invasive surgery (MIS), the recovery time of patients has been shortened significantly. This has led to a shift of postoperative care from hospital to home environment. To prevent the occurrence of adverse events, the care of these patients is mainly relied on routine visits by home-care nurses. This type of episodic examination can only capture a snapshot of the overall recovery process, and many early signs of potential complication can go undetected. The development of Body Sensor Networks (BSNs) has enabled the use of miniaturised wireless sensors for continuous monitoring of postoperative patients. This paper examines the potential of processing-on-node algorithms for further reducing the wireless bandwidth, and therefore the overall power consumption of the sensors. The accuracy and robustness of the technique are demonstrated with lab experiments and a preliminary clinical case study.",
keywords = "Bayesian classifier, Multivariate gaussian model, Postoperative care, Real-time analysis",
author = "Benny Lo and Louis Atallah and Omer Aziz and {El Elhew}, Mohammed and Ara Darzi and Yang, {Guang Zhong}",
year = "2007",
doi = "10.1007/978-3-540-70994-7_21",
language = "English",
isbn = "9783540709930",
series = "IFMBE Proceedings",
publisher = "Springer Nature",
pages = "122--127",
booktitle = "4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007)",
address = "United States",
}