Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

This paper proposes a system for monitoring infant in an incubator and records the relevant data into a computer. The data recorded by the system can be further referred by the neonatal intensive care unit (NICU) personnel for diagnostic or research purposes. The study focuses on designing the monitoring system that consists of an incubator equipped with humidity sensor to measure the humidity level, and a pulse sensor that can be attached on an infant placed inside the incubator to monitor infant's heart pulse. The measurement results which are the pulse rate and humidity level are sent to the PC via Arduino microcontroller. The advantage of this system will be that in the future, it may also enable doctors to closely monitor the infant condition through local area network and internet. This work is aimed as an example of an application that contributes towards remote tele-health monitoring system.

This article describes the development of a wearable sensor glove for sign language translation and an Android-based application that can display words and produce speech of the translated gestures in real-time. The objective of this project is to enable a conversation between a deaf person and another person who does not know sign language. The glove is composed of five (5) flexible sensors and an inertial sensor. This article also elaborates the development of an Android-based application using the MIT App Inventor software that produces words and speech of the translated gestures in real-time. The sign language gestures were measured by sensors and transmitted to an Arduino Nano microcontroller to be translated into words. Then, the processed data was transmitted to the Android application via Bluetooth. The application displayed the words and produced the sound of the gesture. Furthermore, preliminary experimental results demonstrated that the glove successfully displayed words and produced the sound of thirteen (13) translated sign languages via the developed application. In the future, it is hoped that further upgrades can produce a device to assist a deaf person communicates with normal people without over-reliance on sign language interpreters.