Why surgeons are a key target for some Google Glass app developers


Before he even knew that Google Glass was in the works, Partho Sengupta was salivating at the potential for smart eyewear in healthcare. In a draft article for a top medical journal two years ago, the Mt. Sinai cardiologist said he gushed so effusively about the possibilities for such a device that his wearable technology comment was edited out because it was deemed too futuristic.

But maybe his sentiments would be received differently now.

That’s because, these days, uber-early Google Glass adopters are actually wearing their high-tech headgear around town.  And as they show off its hands-free and voice-activated features, it’s becoming increasingly clear that medical professionals – who spend hours each day on their feet and with their hands engaged – could be among those to benefit most from the new technology. Even skeptics, who doubt the consumer appetite for Glass, say they see a much brighter future for…

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Microsoft Research Project: “Fetal Heart Rate Monitoring using Smart Phones”

May.2008 ~ Aug.2009


The goal of this Microsoft Research Project was to develop a fetal heart rate monitoring system on Windows mobile smart phones. Dr. Chang Su Lee worked on this project as a Post Doctoral Research Fellow in School of Computer Science at Edith Cowan University, Mt. Lawley, Western Australia.

Over the last decade, the rate of premature births and fetal deaths among the Indigenous population of rural and remote Australia was more than twice that of the non-Indigenous population. Because of the distance that women in these isolated communities often must travel to reach health centers and hospitals, many do not get the proper medical care to prevent or address prenatal issues. The tyranny of distance and its associated costs remain a barrier for delivering reasonable quality [prenatal] care to remote and rural populations.
For example in reality, one of the phone calls we had was from a woman who lost the twins she was carrying because she lived too far from a medical facility to get regular checkups. By the time doctors discovered a problem with her pregnancy, it was too late to save the babies.

Our team (Dr. A.Tan, Dr. M.Masek, Dr. C.Lee, Dr. C.P.Lam, Angela Fyneman) developed this system to improve the quality of prenatal care in rural and remote communities by providing expectant mothers with an inexpensive, portable, Doppler-based ultrasound device connected to a smartphone running Microsoft® Windows Mobile®.
The first prototype has been produced in mid 2009 resulted in a stand-alone Mobile application on Microsoft® Windows smartphone for calcuating fetal heart rates on front-end mobile devices and sending calculated heart rates to a back-end web server for reporting to the medical team in our partner, Mercy Hospital Mt. Lawely. Since then the system has been in trials with the help from volunteers to evaluate and enhance the accuracy and performance of our system.

This project is listed in Microsoft Research web pages;
* Microsoft Research webpages: mHealth Summit 2009research abstractsocially relevant computing brochure:p#15MSResearch_smartphone_based_fetal_monitor

Published technical papers are listed below;
“Towards Higher Accuracy and Better Noise-Tolerance for Fetal Heart Rate Monitoring using Doppler Ultrasound” – IEEE TENCON 2009
“Advances in Fetal Heart Rate Monitoring using Smart Phones” – IEEE ISCIT 2009
“Remote Home-Based Ante and Post Natal Care” – IEEE HealthCom 2009

Since then, it became a student project in School of Computer Security and Science, Eidth Cowan University, Western Australia. In November 2011, our team won a prize AUD $15,000 as a top 5 finalist for the ‘Mobile Fetal and Maternal Health mobile application’ in Univation WApp Competition in Perth, Western Australia. Link – ECU

HeatMap Generator – Another data visualization tool

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap is another data visualization tool which has been widely used for examining correlation between data rows or columns.

All three pictures above show correlations between features (column data values) in regards to ‘how much they are co-related to each other’. For instance, if their correlation is very high (i.e., very similar) then the color of the each small square dot will be 100% black in first BLACK-WHITE map, and 100% red in second RED_GREEN and third RED_BLUE maps. The data set used here is one of the Alzheimer Disease data sets that I utilized for collaboration work between CSJL solutions and my research team in Edith Cowan University.

This is one way of showing or revealing relationships of data points on the given data set. Obviously, it is much easier to pick up some facts by looking at these pictures, rather than staring at a bunch of high precision numbers.