Agiliad wins design contest at the 26th International Conference on VLSI Design and Embedded Systems!

Team Agiliad had participated in the design contest at the 26th International Conference on VLSI Design and 12th International Conference on Embedded Systems 2013, recently held in Pune. We had presented a novel solution in the design contest for a cost-effective and reliable estimation of fetus gestational age in the resource poor settings. The solution involved the measurement of symphysis-fundus height of a pregnant woman using an image processing based application built on Raspberry Pi, a $ 25 open-source computing platform, augmented with a mechanical frame for referencing the region of interest. The concept was highly appreciated at the conference and we also emerged as winners of the design contest. Following is a brief overview of the problem that we had identified along with the proposed solution. An illustrated presentation of the concept can be downloaded here!

Problem Addressed: Reliable estimation of fetus gestational age in resource poor settings

Estimation of gestational age of the fetus is an important clinical practice, crucial for monitoring the health of the mother as well as the fetus. The conventional method for this estimation is by ultrasonography. However, due to the lack of high end infrastructure in resource poor settings, this method is not practical. Another method for this estimation is the measurement of the symphysis-fundus height (SFH) using a measuring tape (shown in the figure below). This method has been approved to be suitable for rural settings. But because of the lack of proper training and documentation methods amongst the health workers at the primary level, process variations and errors in measurement are widely prevalent, leading to highly unreliable estimations. SFH measurement also facilitates in the early screening of macrosomia (excessive fetal weight), fetal growth retardation and multiple pregnancies. Hence, there is a definite need for a cost-effective technical solution to overcome the shortcomings of the manual measurement method and to help in multi-stage documentation of the procedure over the entire period of nine months.

Fundal Height Measurement

Solution Proposed: Measurement of symphysis-fundus height using Raspberry Pi image processing platform

The key drivers of the technical solution to address this problem are the following – low-cost, ease-of-use, and accuracy. The solution was derived from one of our in-house initiatives to leverage low cost open-source hardware to build a generic computing platform for diverse applications, ranging from building automation to point of care medical diagnostic devices. The present solution comprises a mechanical frame attached to the patient bed. The frame consists of three markers which are attached on top of three telescopic pillars facilitating in positioning the markers on the vertical plane. A web camera is also affixed onto the frame at certain distance from the patient bed. One of the markers is used to reference the camera from the patient by co-relating the diameter of the circular marker obtained on the image versus the actual known diameter. The other two markers are positioned at suitable points on the fundus across which the length of the curve has to be calculated. A schematic diagram of the experimental setup is shown below. A customized image processing algorithm is implemented on a Raspberry Pi computing platform consisting of the following key steps – marker detection, edge image conversion, boundary tracing and distance calculation. The Raspberry Pi is $ 25 open-source hardware based on an ARM 1176JZFS running at 700 Mz, with a Videocore 4 GPU (Bluray quality playback) in a Broadcom BCM 2835 SoC having 256 Mb RAM, 2 USB ports and an Ethernet port. The Raspberry Pi uses Linux kernel-based operating systems. The design and the algorithm were tested on a dummy model of the fundus and accurate length measurements were obtained. The next steps in this project consist of the following – testing the solution in a real clinical setting, building a mobile application on similar concept, and estimating the amniotic fluid index in a pregnant woman using the depth sensor of Microsoft Kinect.

Fundal Height

We wish to scout for more such elementary problems and come up with effective and innovative solutions to tackle them!

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