bluuebunny/crossref_metadata_2025_split huggingface dataset:
Knee Orthosis for Anterior Cruciate Ligament Injuries - Kinematics and Comfortability Study
Authors: Zainal Abidin N A , Shafie S N S , Azaman A , Ramlee M H 
Published: November 2021

Abstract
Knee orthosis is commonly used in supporting the knee movement and protecting the anterior cruciate ligament (ACL) injuries from worsening. In clinical practices, there are many types of ready-made orthoses that available in the market. However, different types of orthoses may provide different performances for patient’s knee joint. Therefore, this paper attempt to investigate the performance of knee orthoses for ACL-injured knee. Two groups took part in the study; (Group 1) six ACL-injured, (Group 2) four healthy participants, where two types of knee orthoses were adopted; (Brace 1) hinge brace, (Brace 2) sleeve brace with bilateral hinges. The knee joint motions were calculated using kinematics data while comfortability was conducted through surveys. From the findings, Brace 1 produced normal range of motion (ROM) for internal rotation at 5.47° while Brace 2 fall outside of the normal range at 2.165°. Meanwhile, the external rotation for Brace 1 (-13.25°) was lower than Brace 2 (-33.25°). Furthermore, the comfortability analysis suggested that Brace 1 (60%) was more effective than Brace 2 (40%). To conclude, Brace 1 portrayed optimal performance than Brace 2 during dynamic balance activities with reduction in ROM to prevent excessive knee rotation.
Effect of Arm Swing Direction on Forward and Backward Jump Performance Based on Biomechanical Analysis
Authors: Teoh L M , Sakeran Hamzah , Salleh A F , Salim M S , Wan Muhamad Wan Zuki Azman , Mohamed Shapie Mohamad Azlan 
Published: November 2021

Abstract
Previous studies have examined the role of arm swing for various types of jumping technique, but none have been found to study about the gender differences in term of the role of arm swing on forward and backward jump. This study aimed to compare the jumping performance between male and female for forward and backward jump. Seven male and seven female subjects performed four trials of forward and backward jump with (FJA, BJA) and without arm swing (FJ, BJ) respectively. Qualisys Track Manager System, EEGO Sports, Visual3D and MATLAB software was used to record and analyze the performance. According to the result, the triceps brachii muscle is the most active muscle compared to other muscles during jumping. The normalized vGRF showed significant correlation with jump height when jumping forward and backward (p<0.01). The arm swing enhanced the jumping performance by increasing the jump height. Males demonstrated greater vGRF and jump height than females. When jump with arm swing, the left knee flexion angle of males increased whereas females decreased. These findings concluded there is different between males and females during jumping.
Recent Developments on Social Robots and Imitation Learning for Robotic Therapy
Authors: Ompico C D S , Bugtai N M , Munsayac F E T 
Published: November 2021

Abstract
The development of social robots has been notably increasing and gaining popularity in recent times. These are also being integrated into healthcare systems, as a means to accompany patients, provide mental health therapy, and entertainment in place of direct human intervention. This paper discusses the recent developments on imitation learning for robot therapy in the field of social robotics to gain knowledge about the importance of this approach as an alternative solution to mental health therapy. The integration of robots to the mental healthcare system is known as robot therapy, which is used as a substitute for animal assisted therapy. Therapy that makes use of animals has been proven to be effective in dealing with mental disorders. However, there are risks such as allergic reactions, bites, and scratches that come with animal assisted therapy, but not robot therapy. The goal for developing robots for this is to make them seem almost life-like--has a way of thinking and emotions. For this to happen, humanoids are being programmed to appear human-like. A solution for this is imitation learning, which is a way for machines to learn, not only tasks, but responses in certain situations, only by observing and imitating humans in an environment.
Modelling and Simulation using Finite Element Method of Surface Acoustic Wave Biosensor for Gas Detection Application
Authors: Noor A M , Norali A N , Zakaria Z , Mazalan M , Wahab Y 
Published: November 2021

Abstract
A surface acoustic wave (SAW) sensor detects changes in physical properties such as mass and density on its surface. Compared to other types of sensors, SAW sensor have a good stability, high selectivity and sensitivity, fast response, and low-cost. On the other hand, to design and optimize a SAW biosensor requires a long process including time and cost using conventional methods. Therefore, numerical simulation and computational modelling are useful and efficiently conduct analysis for the SAW biosensor. In this paper, a numerical simulation technique is used to analyse the SAW device sensitivity for the application of gas detection. The SAW biosensor can detect very small mass loading by changing its sensor resonance frequency. The two-dimensional (2D) device model is based on a two-port SAW resonator with a gas sensing layer. We made two design of SAW biosensor device with frequency of 872 MHz and 1.74 GHz. A gas with vary concentration from 1 to 100 ppm were used to determine the change of the device resonance frequency. As a result, the high frequency (1.74 GHz) device, shows that the resonance frequency is shifted larger than to the low frequency (872 MHz) device. In addition, the high frequency device offers five times more sensitivity than the low frequency device. By changing the sensor design, the sensor characteristics such as sensitivity can be altered to meet certain sensing requirements. Numerical simulation provides advantages for sensor optimization and useful for nearly representing the real condition.
Rapid prototyping of a temperature, humidity, and pressure monitor electronic layer for Pressure Ulcer wound patch
Authors: Sta. Agueda J R , Lim J , Mondragon J M , Madrid J , Belen M G , Eustaquio G M Y , Monjardin J G , Salud N 
Published: November 2021

Abstract
Pressure ulcer is a result of relieving pressure from skin or underlying tissues, causing localized injuries. In this study, a prototype of an electronic monitoring layer that can be placed on top of the wound patch is designed. The electronic layer is comprised of four force-sensitive pressure sensors, and an integrated temperature and humidity sensor to monitor the activities surrounding the wound site. In the simulated wound bed experiments, the results indicated that the utilization of the Bosch BME280 I2C module, when placed on top of a gauze pad, can deliver accurate and real-time monitoring of the temperature and humidity values. Furthermore, the force-sensitive resistors (FSR) installed can be utilized to detect external pressure beyond the set allowable force applied of 32 mmHg or 700g. Therefore, the electronic layer assembled from commercially available sensors can be used to monitor temperature and humidity while being able to detect externally applied pressure in real-time. However, improvements in the size and flexibility of the electronic layer are necessary to reduce the discomfort that patients suffering from pressure ulcers will experience.
Three-dimensional printed orthosis in biomedical application: A short review
Authors: Hasibuzzaman Md , Wahab Asnida Abdul , Seng Gan Hong , Ramlee Muhammad Hanif 
Published: November 2021

Abstract
The three-dimensional (3D) printing in medical implants unlocks unparalleled opportunities to completely configure the product to the patient’s measurements and needs. To be noted, the use of personalized 3D printed orthosis used in regeneration for serious orthosis implants of specific patients is growing to date. The 3D printed is unique to the patient instruments that can be used to facilitate correct positioning of implants and improved functional outcomes. The 3D printing, also defined as ‘rapid prototyping’ and ‘additive manufacturing’ is widely regarded as the ‘second technological revolution. The orthosis is an “externally applied mechanism used to alter the structural and functional properties of the musculoskeletal and skeletal system”. Applications in orthosis healthcare that are pioneering the way 3D printing is performed, changing the orthosis implant markets. This paper is reporting literature on the development of orthosis using 3D printing technology that could make the users more comfortable and easier to maintain. From the literature search, this paper summarises some important information about the use of 3D printing for orthosis development where it focusses on specific regions of human body, the materials for the 3D printed orthosis and further directions of this technology and research. In conclusion, the findings from this review paper may lead to a future recommendation and study in providing better treatment for patients.
Initial Results on Primary Field Cancellation of Magnetic Induction Spectroscopy Technique for Fetal Acidosis Detection using COMSOL Multiphysics
Authors: Abdul Halim S F , Zakaria M H , Zakaria Z , Norali A N , Mohd Noor A , Ahmed A A , Pusppanathan J , Fazalul Rahiman M H , Mohd Muji S Z , Abdul Rahim R 
Published: November 2021

AbstractMonitoring of fetal condition during labor could save hundred lives in a single year. During labor, fetus is at critical condition as acidosis may occur suddenly without any early symptoms. Invasive method such as Fetal Blood Sampling (FBS) has been used to detect the decline in pH level of fetus. However, fetal loss rate after FBS may range from 1.4% up to 25%. In this paper, magnetic field induction spectroscopy was implemented to determine fetal acidosis by using primary magnetic field cancellation technique. Magnetic Induction Spectroscopy (MIS) probe was design where transmitter coil (TX) is perpendicular to receiver coil (RX). The result shows that the secondary magnetic field produced have been successfully measured without any interruption from primary magnetic field. By using transmitter input 1A, it shows that voltage is inversely proportional to the blood pH due to the conductivity properties of blood.
A Comparative Study on Instantaneous and Mean Pulse Arrival Time for Cuffless Blood Pressure Estimation
Authors: Wan Zaki W S , Correia R , Korposh S , Hayes-Gill B R , Morgan S P 
Published: November 2021

AbstractPulse arrival time (PAT) is the delay time between the peak of the R-wave Electrocardiogram (ECG) signal and the peak of Photoplethysmogram (PPG) signals. This method is widely exploited for continuous cuffless blood pressure measurement. In the literature, the PAT was determined based on the mean at a certain number or certain period of heartbeats, but none of them deployed a single pulse wave for PAT calculation. Therefore, in this paper, a relationship between mean PAT (15 pulses ± Standard Deviation (SD)) and instantaneous PAT (a pulse) with blood pressure (BP) was investigated on thirteen healthy male volunteers (aged between 17 to 42 years) through a pedal exercise. The PAT is grouped into three (3) categories which depend on the spatial position of the PPG signal measured; finger (PATf), wrist (PATw), and underfoot (PATt). The ECG and the PPG signals were synchronized using a Nexus-10 MK II data acquisition device and Matlab software (R 2014b) for subsequent analysis. An oscillometric cuff-based blood pressure instrument (Ostar, P2) was used as a BP reference during the experiment. Statistical analysis showed no significant difference in the |r| value between mean (15 pulses ± SD) and instantaneous PAT-BP; hence both methods are applicable for BP estimation using the PAT-BP calibration technique.
Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)
Authors: Yazid H , Mat Som M H , Basah S N , Abdul Rahim S , Mahmud M F , Arof H 
Published: November 2021

Abstract
Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.
DNA Microarray Image Segmentation Using Markov Random Field Algorithm
Authors: Mat Said K A , Jambek A B 
Published: November 2021

Abstract
A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.
Large Amplitude Harmonic Vibration under Pulse Doppler Measurement
Authors: Hermawan N , Ishii T , Yamakoshi Y , Saijo Y 
Published: November 2021

Abstract
A flow velocity or tissue motion can be measured by pulse Doppler method. However, when the method is applied to a sinusoid motion or harmonic vibration, the estimation result is deviated. In this paper, the generalized theory of pulse Doppler measurement to the sinusoid motion is explained and the relation between the estimation deviation and the real value is given. Furthermore, a special case of binary velocity estimation is expanded and simulated with different parameters. This simulation result suggested that different selection of parameters may fundamentally affect the output of pulse Doppler estimation.
An Image Pre-Processing Improvement Procedure for Standardizing Multiple Colour Variations in the Astrocytoma Immunohistochemical Staining Images
Authors: Dzulkifli F A , Mashor M Y , Jaafar H 
Published: November 2021

Abstract
Colour variations in the histopathological images can occur when the acquired images came from different laboratories. This issue happens may be due to different protocols between each laboratory. Besides that, the usage of dye or staining is also different since it arrived from different manufacturers. Hence, it will affect the performance of a system, especially for an automated image diagnosis system. If this issue is taken lightly, the whole process of image analysis can produce a false diagnosis outcome, and thus, it will affect the patient’s treatment options and endanger the patient’s life. This paper proposed an automated image pre-processing procedure for standardizing the colour variations in immunohistochemical staining images. The proposed procedure was done by modifying the reference image, which will be used later for the stain standardization process. Based on 50 astrocytoma histopathological images, the proposed procedure showed a promising result, where the quality of the output images has been enhanced and standardized.
An Investigation of Brain Signal Characteristics between Hafiz/Hafizah Subjects and Non-Hafiz/Hafizah Subjects
Authors: Abdul Halim S F , Awang S A , Mohamaddan S 
Published: November 2021

Abstract
Tahfiz education has gain its popularity among Malaysians thus expand the circle of hafiz and hafizah all over the country. This study has been done to investigate effect of memorizing Al-Quran by determining the difference between hafiz/hafizah subjects and non-hafiz/hafizah subjects in terms of their focus using brain signal characteristics. 10 subjects (5 hafiz/hafizah and 5 non-hafiz/hafizah) have been participated in this study. Database of EEG was recorded by using EegoSport (ANT Neuro, ES-230, The Netherlands) while listening no music, rock music, instrumental music and Al-Quran audio simultaneously with Continuous Performance Task (CPT). The classification has been done by using machine learning method. Decision Tree method have obtained the highest accuracy (96.63%) for PSD Burg by using beta wave. The finding shows that hafiz/hafizah group were more focus in all given tasks compared to non-hafiz/hafizah group. Statistical analysis by using Wilcoxon Signed-Ranks Test found that the designed methodology was significant with 95% confidence interval.
A Biomedical Voice Measurement Diagnosis of Parkinson’s Disease through the utilization of Artificial Neural Network
Authors: Ayap N F M , Eugenio B A , Hinolan J I V , Puno J C V , Baldovino R G , Billones R K C 
Published: November 2021

Abstract
In 2016 alone, there were a total of 120,000 cases of PD diagnosed and documented, however, experts believe that there are still loss cases which remain to be undiagnosed because of external factors such as medical cost and accuracy of diagnosis. The detection and diagnosis of PD on its early onset has become a problem in the medical field because of the slow progression of its symptoms. With the advent of technology, different diagnosing methods are being introduced and explored - one of which is through the concept of Neural Network. This paper highlights the human voice of patients using Multilayer Perceptron Neural Network (MLP) to accurately diagnose individuals who are diagnosed with PD. It was seen that the MLP classification prediction has achieved an average of 91.5% accuracy.
Reliability of Electrocardiogram Signals during Feature Extraction Stage for Smart Textile Shirts
Authors: Mohd Nawawi MM , Sidek Khairul Azami , Azman Amelia Wong , Nashrul Fazli Mohd Nasir 
Published: November 2021

Abstract
Wearable smart textiles have garnered significant interest due to their high flexibility, reusability, convenience and ability to work on home-based, real-life and real-time monitoring. Wearable smart textiles are shirts with inbuilt textile sensors that enable electrocardiogram (ECG) data to be collected more comfortably and smoothly outside the laboratory and clinical environment for a continuous and longer duration for ECG data collection. However, the existing ECG wearable smart textile main challenge is maintaining the quality and reliability of data across multiple wearable smart textile shirts. Therefore, this research analyses the capability of ECG morphology during Feature Extraction stages for different wearable smart textile shirts. This paper reports the experiment conducted on eleven healthy volunteers, either wearing the Hexoskin smart shirt or the HeartIn Fit shirt or both. ECG data were recorded while they are doing normal daily routine activities for at least 45 minutes. The study demonstrates a significant possibility of reliability in Feature Extraction stages at different time instances among subject and wearable smart textiles shirts. With R peaks average between 0.543 to 1.194 mV and R-R interval average between 0.625 to 0.799 seconds, the study concludes that both wearable smart textiles do not significantly differ in Feature Extraction stages. Thus, both wearable smart textiles gave a significant result, although both are affected by their wearer’s motion artefacts during the shifting of body postures and the wearer’s body physical states. Furthermore, the ECG morphology in this study has yielded a promising result in real life and as on-the-go ECG smart textile biometric readiness for future explorations.
Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals
Authors: Nawayi Siti Habibah , Vijean Vikneswaran , Salleh Ahmad Faizal , Rashid Abd Rusdi , Planiappan Rajkumar , Lim C C , Fook CY , Awang Ardeenawatie Saidatul 
Published: November 2021

Abstract
Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.
Electromyography Signal Pattern Recognition for Movement of Shoulder
Authors: Norali A N , Anas M N , Zakaria Z , Asymawi M , Abu Bakar A H , Chong Y F 
Published: November 2021

Abstract
Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers that are used are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Classification results shows highest accuracy on ZC feature using an SVM classifier with cubic kernel. The study on shoulder movement using EMG of pectoralis and deltoid muscles could be extended on arm amputees based on hypothesis that the EMG signal could be utilized for control of robotic prosthetic arm.