1,720,978 research outputs found
Protein concentration determination in latex glove using Biocompatibility Morphological Mean test
No full textLatex gloves are seen as an indispensable item in the healthcare field because it offers superior protection for both the medical staff and patient against harmful substances. However, latex gloves with high protein concentration have a high possibility to induce latex allergy which in the worst case can lead to a life-threatening condition. To minimize the occurrence of an allergy reaction, the computerized Biocompatibility Morphological Mean (BMM) test for protein detection is proposed. This test initially goes through the chemical process to determine the protein that resides in the glove sample. After that, the sample is electronically converted into a digital image. Finally, the image undergoes color image processing for calculating the color difference values. These values are then plotted on a standard curve. A high correlation coefficient (R2>0.97) of the standard curve gives better accuracies. The proposed method only takes about 40 minutes to complete the test, while existing methods need at least 6 hours
Assessment of brain attentive level using visual-based mixed reality and electroencephalogram
No full textThis study aims to assess and analyse a person’s attention and distraction responses during performing a task. There are several ways of assessing those cognitive processes and Electroencephalogram (EEG) was chosen for its ability to provide direct and valuable information about the brain activity. In the past, numerous related works employed Desktop-based cognitive tests to induce attentionrelated cognitive functions in individuals and employed machine learning approaches to classify these functions based on the EEG data. However, there are two research gaps that require further exploration. The first gap involves the environment used for cognitive tests, in which the Desktop-based approach limits the participant’s immersion and engagement toward performing the test. The second gap involves the machine learning approach, where the use of feature engineering was time consuming due to the need of selecting the most suitable feature extraction method. Moreover, there is also the risk of human error when performing the feature selection, resulting in sub-optimal features selected and negatively impacting the classification performance. Therefore, to address these research gaps, the objective of this study was to first design an experimental testing framework that applied a Visual Search test to induce attention and distraction responses, while simultaneously recording their corresponding EEG signals. The study then further involved with the development of a Deep Learning (DL) model capable of learning from EEG data, selecting significant features and classifying them into their respective responses using an end-to-end approach. In the testing framework, previous studies typically perform the test in the Desktop-based environment to collect the EEG data. This study proposed the use of Mixed Reality (MR)-based environment, where the virtual objects of cognitive tests were rendered in a real environment to induce the participant’s respective responses. A comparison of the engagement, concentration, and immersion of participants performing the cognitive tests in both Desktop-based and MR-based environments was conducted through the analysis of collected EEG signals
Protein Quantitation in Latex Glove Images With Colorimetric Analysis
No full textThe medical gloves made from Natural Rubber Latex (NRL) have seen as an essential item for a healthcare worker. These gloves offer excellent protection to the user against harmful microorganisms and also reduce the risk of cross-contamination between patient and healthcare worker. However, NRL is rich in protein mixture, in which certain types of proteins can cause latex allergies. The latex glove with high protein concentration are likely to cause latex allergies to the user. Thus, to avoid the development of an allergic reaction, the protein level must be quantitated. In the past, several existing total protein determination tests such as modified Lowry microplate, and Bradford microplate have been implemented. Those existing tests will determine the presence of proteins in the solution through the colour changes to blue. After that, the saturation of blue will be analysed using existing quantitation methods such as Spectrophotometric, Smartphone for Point-of-Care, Colorimetric Microwell, and Digital Photometric to quantitate the total protein value. Since numerous chemical and laboratory equipment are used, the process of existing total protein determination tests is complicated, costly, and time-consuming. While for the existing quantitation methods, most of them need to take consideration of the light interference as it will affect the sample colour. Therefore, to overcome those limitations, a novel total protein determination test and quantitation method named Glove Surfaced Based Protein Binding (GSPB) and Computerised Colorimetric Protein Quantitation (CCPQ) are proposed. The GSPB is a simple yet efficient chemical test, which uses only Bradford assay to bind with the sample surface. If the colour of sample surface become blue, it indicates the presence of protein in sample. After that, the sample will be weighed using the analytical balances and converted into digital image by scanner. The image will then analysed using the CCPQ method. This method consists of a technique called Colour Domain Delta E (CDDE), which calculates the colour difference Delta E (ΔE) value between the raw and chemical stained sample image. The experimental ΔE value will be evaluated using the Squared Polynomial Exponential Covariance (SPEC) function to predict and quantitated the total protein concentration of the sample. The result showed that SPEC function is able to achieve R2 up to 0.9985, indicating that 99.85% of the actual values are explained by the predicted total protein values
Research on Protein Level in Medical Latex Glove Images using Color Kernel Regression Method
No full textIn the healthcare environment, medical latex
gloves are a necessary medical item for healthcare workers as it
offers excellent hand barrier protection against dangerous
microorganism. However, if the healthcare workers repeated
exposure to the latex gloves which contain high protein level, it
will increase the possibility of the workers to have a risk for latex
allergy. Thus, the objective of this project is to develop a color
kernel regression (CKR) method for estimating protein level
through the analyses of color difference in glove images.
Initially, the gloves will go through an uncomplicated chemical
test for protein detection. A blue color will appear on the surface
of a glove sample that contains protein. After that, the chemical
binded sample will be digitally converted into a sample image
using the flatbed scanner. The image will then undergo image
processing to improve its quality and to calculate the color
difference values of the sample. Those calculated values with the
pre-defined protein levels will be used to plot a standard graph. A
high coefficient of determination with R2 > 98% has been
obtained from the experimental graph. This indicates that the
proposed CKR method contributes significantly toward the
estimation of protein leve
Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network
No full textAttentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants’ EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler’s fading experiment and use an EEG device to collect data on participants’ attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants’ attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator
Development of Visual-based Rehabilitation Using Sensors for Stroke Patient
No full textThis project concerns on the development of applications using sensors for the rehabilitation of stroke patients. Thus, the leap motion sensor is employed for the finger motor rehabilitation training while the Microsoft Kinect sensor is utilized for the upper limb motor rehabilitation. Two applications which are named ‘Pick and Place’ and ‘Stone Breaker’ are developed. For the first application, the patient is required to pick up the virtual blocks and stack it up. The ‘Stone Breaker’ game requires the patient to move the upper limb in controlling the paddle movement in the game. At the end of the project, it is able to achieve the dominant objective of the project when the tested patient shows significant improvement in both the application
Protein Concentration Determination in Latex Glove Using Biocompatibility Morphological Mean Test
No full textLatex gloves are seen as an indispensable item in the healthcare field because it offers superior protection for both the medical staff and patient against harmful substances. However, latex gloves with high protein concentration have a high possibility to induce latex allergy which in the worst case can lead to a life-threatening condition. To minimize the occurrence of an allergy reaction, the computerized Biocompatibility Morphological Mean (BMM) test for protein detection is proposed. This test initially goes through the chemical process to determine the protein that resides in the glove sample. After that, the sample is electronically converted into a digital image. Finally, the image undergoes color image processing for calculating the color difference values. These values are then plotted on a standard curve. A high correlation coefficient (R2>0.97) of the standard curve gives better accuracies. The proposed method only takes about 40 minutes to complete the test, while existing methods need at least 6 hours
Application of Mixed Reality and Electroencephalogram in Brain Attentive Level
No full textIn this project, a Mixed Reality (MR) application and Electroencephalogram (EEG) will be used to classify whether a person is being attentive or distracted. MR applications are unique in that they project virtual information into the user’s real environment. This blend of real and virtual changes the level of impact and persuasive power of the experience. Since MR is a new-wave technology, there is room to explore the potential of MR in the attention-related research field. An EEG is an electrophysiological monitoring test where it measures the electrical activity in the brain. The EEG data can provide valuable quantitative and unbiased information on brain activity in a millisecond timeframe. The main goal of this research involves the design of a framework for visual search tests with the use of MR and to propose a deep learning model for attention and distraction classification. The use of visual search tests is to induce human attention and distraction. During this period, the EEG signal of the users will be recorded in a millisecond timeframe. After that, a deep learning method named Convolution Attention Memory Neural Network (CAMNN) will be proposed to perform the attention distraction classification of EEG signals
Latex Glove Samples Cutting System with Rotary Cutter Mechanism
No full textIn this study, it involves the protein
determination of latex gloves for latex glove manufacturing
industry. Natural rubber latex (NRL) gloves provide tactile
properties, barrier protection, tear resistance, economy and
comfort touch surface area which preferred by the medical field.
However, there are more than 200 proteins in the NRL and 13 of
them which are known to be allergens to human. In the
manufacturing process of latex gloves, the Bradford reagent
protein test method is used as a standard method for assessing
protein levels for quality control. The current standard methods
typically involve many procedures that manually operate which
results in high processing time. Thus, new innovative design is
needed and involved the cutting system that allows samples
stamping and retrieved with less time consumption. The cutting
machine can perform the automatic cutting of latex glove into
several samples According to the current method, samples cutting
process conducted by humans. Therefore, the new system is
automated, which may only require an operator to place the glove
at the input of the system and remove it at the end of the process.
The new latex glove samples cutting system consists of Computer
Numerical Control (CNC) moving mechanism with a 28 mm
rotating rotary cutter as cutting die. Thus, the cutting system is
able to shorten the processing time on protein determinatio
Application of Mixed Reality and Electroencephalogram in Brain Attentive Level
No full textIn this project, a Mixed Reality (MR) application and
Electroencephalogram (EEG) will be used to classify
whether a person is being attentive or distracted. MR
applications are unique in that they project virtual
information into the user’s real environment. Since
MR is a new-wave technology, there is room to
explore the potential of MR in the attention-related
research field. An EEG is an electrophysiological
monitoring test where it measures the electrical
activity in the brain. The EEG data can provide
valuable quantitative and unbiased information on
brain activity in a millisecond timeframe
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