Changing The Curriculum

A goniometer measures the available range of motion of an object. In medicine, this instrument is used assessment tool for joints in the body that allows physicians to evaluate a patient's condition, which then enables therapeutic adjustments. In order to improve recording diagnosis history, and the measurement process itself, this study aimed to create a digital goniometer that allows instantaneous measurement of the elbow and knee joint angles, through pictures. The device was made possible through the use of Raspberry Pi, a microcomputer capable of supporting functional applications through integration of hardware and software components. Statistical analysis shows a 98.25% and 98.09% accuracy for the elbow and knee joints respectively, resulting from closely related values between the actual goniometer, and the device created from this study.

Image-Processing-based Digital Goniometer using OpenCV

Since many diseases and infections are dependent on the count and type of Red Blood Cells (RBCs) and White Blood Cells (WBCs) present in the blood stream, detection and classification pertaining to them is necessary and relevant. Based from existing related literature, ordinary Neural Networks are usually employed. Also, in existing researches, RBC types are the main focus. Hence, after observing research gaps, a Faster Region-based Convolutional Neural Network (Faster R-CNN) was utilized for this study, focusing not only on RBCs but also on the variants of WBCs. The aim is to have a fast and reliable system in order to achieve the goal of aiding the medical field in the classification of RBCs and WBCs.

Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

https://www.mdpi.com/1424-8220/21/2/512/pdf

Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

Biomedical image analysis by machine learning enables accurate cell counting and localization, which are essential to clinic diagnosis and research. However, high-density population of the cells poses great challenges. The variability and intricacy in the shapes and colours of cells further jeopardize the performance of existing models. Aiming to overcome the difficulties, we propose an end-to-end manifold-regularized regression network (MRRN) for cell counting and localization. The backbone is a U-Net tweaked for regression and trained by density maps to preserve the spatial distribution of the cells. Based on graph Laplacian, the patch-wise manifold regularizer imposed on the U-Net can represent the intrinsically nonlinear and complex features of the images, thus increasing the generalizability of the network and lowering the risk of overfitting. Experimental results show that MRRN outperforms several state-of-the-art algorithms on a widely-adopted synthetic data set and a real-world carcinoma data set, which verifies the efficacy and applicability of the proposed model.

Manifold-Regularized Regression Network: A Novel End-to-End Method for Cell Counting and Localization

Blood cells both white and red are important part of the immune system. These cells help fight infections by attacking bacteria, viruses, and germs that invade the body. White blood cells originate in the bone marrow but circulate throughout the bloodstream, while red blood cell helps transport oxygen to our body. Accurate counting of those may require laboratory testing procedure that is not usual to everyone. Generating codes that will help counting of blood cells that produce accurate response via images gives a relief on this problem. In this study, the images were processed and a blob detection algorithm was used to detect and differentiate RBCs from WBCs. A cell counting method was also used to provide an actual count of the RBCs and WBCs detected. The automation comes with a graphical user interface backed-up with a working database system to keep the records of the users (e.g. patients, respondents). The performance of the system was statistically described as accurate compared to the manual method of counting. Results show an accuracy of 100% for platelet, 96.32% for RBCs and 98.5% for WBCs. Hence, the proposed system can benchmark with the manual methods of detection and counting of platelets, RBCs and WBCs in blood samples.

Blood Cells Counting using Python OpenCV

The Philippines implemented the new K to 12 education system starting academic year 2016 to 2017 adding two more years in the basic education called senior high school. The addition of senior high school resulted to major changes in the curriculum of both high school and college levels. One of the many changes is removing some engineering mathematics and science courses from college level and bringing it down to the high school level. Although the government was believed to have supported the rigorous design and smooth implementation of the new system, many stakeholders, especially the higher education institutions, are concerned about the preparedness of their future engineering students. This study aimed at assessing the level of preparedness of STEM senior high school graduates in taking engineering baccalaureate programs by using a multiple-choice-question instrument composed topics that are required to be learned by STEM senior high school graduates to match the entry requirement of an engineering freshman student. The instrument was deployed to 363 STEM graduates who were enrolled in the engineering programs of National University Philippines without any prior intervention. Using one-sample t test, the combined test scores resulted into a mean of 54.17 percent which is significantly lower than the test value of 75 percent. Analyzing the kurtosis of the data distribution and Tukey box plots, the data is found to be significantly relevant to arrive at a certain conclusion. The level of preparedness was presented in a Likert scale showing that 88.98 percent of the STEM senior high school graduates who took the test are only somewhat prepared to take baccalaureate engineering program.

Level of preparedness of STEM senior high school graduates in taking up engineering program: a Philippine setting

A design of a microcontroller-based wheelchair controlled by the motion of the eye is presented. The user wears a set of electronic eyeglasses used to detect the movement of the sclera and then transmits control signals to the wheelchair via Bluetooth. Detection of sclera movement is done via infrared sensors. The wheelchair is equipped with an obstacle detection system utilizing ultrasonic sensors. To operate the wheelchair, the user presses a button to move forward, and moves his eyes to turn the chair in a specific direction. The sensors can detect four directions of eye movement: half left, full left, half right and full right. Tests for response to eye movement, delay in the transmission of control signals, reliability of transmission and reliability of the ultrasonic sensors in terms of obstacle detection shows that the design met the objectives to ensure proper and safe operation of the wheelchair.

Eye-Controlled Wheelchair Using Infrared Detection

E-learning has become an alternative to traditional classroom learning as information technologies continue to develop at a rather fast pace nowadays. Instructional materials are being made available online with the promise of offering online collaborative learning and discussion as well. Among these e-learning paraphernalia, video-based learning has gained momentum as classroom instructional material especially in mathematics lectures where students are often faced with the difficulty in splitting their attention between trying to understand the content of the lecture and writing down detailed notes. In this study, technology-enhanced video lectures were introduced in Integral calculus. The influence of using video clips as an instructional material to enhance the performance and learning of students was evaluated based from three settings: (1) a classroom setup with live lecture only, (2) a classroom setup with pre-recorded lecture only, and (3) a classroom setup with live lecture and pre-recorded lecture. Analysis includes Paired Samples T -test and ANOVA. Results revealed the significant positive influences of video lectures in the improvement of student performances after comparison of pre-test and posttest scores. Students in the Lecture and Video Group gained the highest posttest scores. Post hoc analysis showed that there is significant difference between the three learning settings.

An Empirical Study on the Impact of Pre-recorded Lectures on Students' Performance in Integral Calculus

Pork in the Philippines is mostly sourced locally, and not all these meats come from well-sanitized slaughterhouses. Some carcasses of pigs that have died other than by slaughtering, called adulterated or double-dead meat, make their way to markets and get mixed up with legal and safe meat, only to be sold to unsuspecting consumers. These pose health hazards to meat handlers and consumers. One of the longtime practices in preventing the proliferation of double-dead meat in the markets is the manual inspection that analyzes the meat based on its organoleptic properties. This solution, however, is prone to limitations of subjectivity and human evaluation error. In this study, the proponents designed a system that will identify whether the meat sample is adulterated or not. The system is aimed at providing fast and accurate detection of double-dead meat using color and chemical determinants that is at least comparable to, or better than, humans. The application of the system can be utilized to replace the traditional human evaluation using electronic sensors as to avoid bias in the evaluation of the meat by different assessors as well as to eliminate the health risks on the meat inspectors and the consumers. The system utilizes three sensors: color sensor, methane sensor and pH level sensor. A microcontroller implements a logistic regression classifier. The model performs well, with an accuracy of 91.40% after validation, with an area of 0.95 under its ROC. When realized, the model has an accuracy of 93.18% compared to 84.09% of the human assessor's evaluation of the same meat samples.

Alexa Ray Fernando

Papers

Blood Cells Counting using Python OpenCV

Level of preparedness of STEM senior high school graduates in taking up engineering program: a Philippine setting

Eye-Controlled Wheelchair Using Infrared Detection

An Empirical Study on the Impact of Pre-recorded Lectures on Students' Performance in Integral Calculus

Detection of Adulterated Pork Meat via Color and Chemical Characteristics