Forthcoming and Online First Articles

International Journal of Biomedical Engineering and Technology

International Journal of Biomedical Engineering and Technology (IJBET)

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International Journal of Biomedical Engineering and Technology (22 papers in press)

Regular Issues

  • A supervised machine learning approach to predict performance and aid decision making of biomaterials design for skin tissue engineering applications   Order a copy of this article
    by Aakriti Aggarwal, Pankaj Jain, Saurabh Gupta, Mahesh Kumar Sah 
    Abstract: The physical and chemical interactions among the cells and scaffolds are pivotal for regenerating the desired tissue. The fields of material science and tissue engineering aim to understand this complex behaviour, which can pave new ways for optimising the tissue growth. The present study attempts to predict the in-vitro fibroblast cell growth by modelling the physico-chemical characteristics of the biopolymeric scaffolds through different supervised machine learning strategies for skin tissue engineering application. The chemical nature, porosity, surface roughness, and wettability of the chitosan and gelatin-based scaffolds were used as indicative support; and the cell growth percentage to train various regression models. The random forest classifier provided the specificity, sensitivity, and precision of 88.6%, 99.87%, and 93.75% respectively after hyperparameter tuning. The applicability and efficiency of machine learning for predicting skin tissue engineering outcomes can help in saving time, resources, and human errors while biomaterials designing.
    Keywords: biopolymeric scaffolds; cell-scaffold interaction; supervised machine learning; decision making; skin tissue engineering; biomaterial design.
    DOI: 10.1504/IJBET.2023.10059543
  • Can Running Technique Modification Benefit Patellofemoral Pain Improvement in Runners? A Systematic Review and Meta-Analysis   Order a copy of this article
    by Hongbin Chang, Xuanzhen Cen 
    Abstract: Patellofemoral pain is one of the most common running-related pathologies among the running population. Running technique modification as a suggestive treatment strategy for mitigating patellofemoral pain in runners has been widely investigated in previous literature. The objective of this systematic review was to summarise the literature addressing the running technique modification associated with patellofemoral joint loading, to guide prevention strategy for patellofemoral pain. The English-language searches were conducted in the electronic databases of CINAHL, MEDLINE, and Web of Science for studies investigating biomechanical outcomes of the knee joint until May 2022. Study methodological quality was assessed using modified Downs and Black scales, with a meta-analysis conducted. Fourteen qualified studies were involved. The moderate evidence indicated that the forefoot strike (FFS) pattern led to a reduction in patellofemoral joint (PFJ) loading compared with the rearfoot strike (RFS) pattern. Also, moderate evidence indicated that the PFJ loading decreased progressively as the step rate increased, but was inversely proportional to step length. These findings highlight the moderate evidence that running technique modifications, such as adopting FFS, increasing step rate, and decreasing step length, could be suggested to reduce PFJ loading and potentially alleviate PFJ pain in runners. Further research should focus on the long-term measurement to investigate the retention of these lower limbs' biomechanical characteristics under different running technique strategies.
    Keywords: Patellofemoral pain; knee joint loading; running technique modification; running strike pattern; running step rate; running step length.
    DOI: 10.1504/IJBET.2023.10060062
  • Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)   Order a copy of this article
    by Deepak Kumar Maharana, Pranati Das 
    Abstract: This paper represents an unsupervised approach for the segmentation of the retinal vasculatures in an instinctive way. In this approach, first, the ophthalmoscope images are enhanced by contrast stretching along with logarithmic transformation and contrast limited adaptive histogram equalisation (CLAHE). Next, the edges of the enhanced images are preserved by using a rolling guidance filter, and, subsequently, the residual image is obtained by the subtraction of the rolling guidance image from the CLAHE image. After that, the optic disk is excluded to give a better segmentation result. Next, the ISODATA thresholding method is applied to obtain the binary image. Finally, morphological operation with noise removal and the boundary is excluded to obtain the final segmented image. The presented approach's performance is appraised using different mathematical measures. It achieves average false positive rate (FPR), true positive rate (TPR) and accuracy of 0.0152, 0.7037 and 0.9604 for DRIVE database and 0.0313, 0.776 and 0.9541 for STARE database.
    Keywords: ophthalmoscope images; CLAHE; rolling guidance filter; RGF; false positive rate; FPR; true positive rate; TPR.
    DOI: 10.1504/IJBET.2023.10060250
  • A Compact Mathematical Representation of Human Body Silhouettes from Frontal and Lateral Views   Order a copy of this article
    by Fozia Rajbdad, Murtaza Aslam, Shoaib Azmat, Faiza Rdad, Jian Xu 
    Abstract: Human body silhouettes are used extensively in three-dimensional body shape modelling, activity recognition, apparel design, obesity, and posture assessment. These applications require efficient storage of human body images for future use and comparison. We proposed a novel one-dimensional mathematical representation of human body silhouettes from frontal and lateral views using a discrete cosine transform. Our method saved 75% of the storage space, significantly reducing costs, and achieved a compression ratio of 4:1 with an average reconstruction accuracy of 90% for all views of male and female images. Additionally, segment-wise silhouette representation decreased the average reconstruction complexity four times. Human body silhouettes are also modelled for the first time using polynomial curve fitting, discrete wavelet transform, and discrete Fourier transform with a systematic comparison. The polynomial curve fitting achieved the highest average space saving of 84%; however, reconstruction accuracy decreased by 12% compared to the discrete cosine transform. In addition, our novel method attained 46% additional storage space saving compared to standard two-dimensional JPEG and PNG image compression methods. Our work can be used to assess human body fat distribution, detect pose abnormalities and classify body shapes, ages, and genders.
    Keywords: silhouettes; frontal; lateral; mathematical representation; reconstruction; discrete cosine transform; DCT.
    DOI: 10.1504/IJBET.2023.10060649
  • The study of the response of postural stimuli in relation to Heart rate and Skin conductance in the nonlinear domain   Order a copy of this article
    by Ankita Soni, Kirti Rawal, Tushar Tyagi 
    Abstract: This study examines the relationship between skin conductance response (SCR) and heart rate variability (HRV) as well as the impact of postural provocation on the autonomic nervous system. The HRV and SCR were examined in this study using the self-recorded dataset of 45 subjects in supine and standing body positions. Nonlinear methods have been applied to the recorded data set in order to ascertain the impact of postural shifts on the recorded signals. For the statistical analysis of the value of nonlinear parameters, Spearman rank correlation and pair t-test have been used to validate the nonlinear features of the applied nonlinear methods. From the results, it has been found that the HRV decreases in the body’s standing posture while SCR increases in this position. This implies that in the standing position, sympathetic activity increases while parasympathetic activity decreases, as shown by a decrease in HRV and an increase in SCR.
    Keywords: heart rate variability; HRV; skin conductance response; SCR; autonomic nervous system; nonlinear analysis; Spearman correlation; postural change.
    DOI: 10.1504/IJBET.2023.10061108
  • Lower Limb Biomechanical Difference Between Bounced and Alternating Jumping rope   Order a copy of this article
    by Tianle Jie, Jiao Li, Ee-chon Teo 
    Abstract: Jumping rope is easy to perform, providing physical benefits, but different techniques may have varying effects on the body. This study aimed to compare the biomechanical differences between alternating jumping rope (AJ) and bounced jumping rope (BJ). Twenty adolescent participants were recruited, and lower limb kinematic and kinetic data were collected using the Vicon 3D motion capture system and the AMTI 3D force platform. During the AJ, the knee joint exhibited an increased adduction angle (p < 0.05) and a higher dorsiflexion angle of the ankle joint (p < 0.05). Additionally, the AJ showed significantly larger flexion (p < 0.001), extension (p < 0.001), and abduction moments (p < 0.001) compared to the BJ. Ankle joint stiffness was significantly higher in the AJ (p < 0.05). The study results provide valuable insights into jumping rope biomechanics, with practical significance for promoting healthy activities among adolescents.
    Keywords: jumping rope; adolescents; biomechanics; joint stiffness; OpenSim musculoskeletal modelling.
    DOI: 10.1504/IJBET.2023.10061139
  • The Effect of Running Experience on Muscle Forces and Knee Joint Reaction Forces during Running   Order a copy of this article
    by Zhihui Kang, Xinyan Jiang 
    Abstract: It is possible that increased experience leads to improved running mechanics and fewer injuries, however, the reason behind this is still unclear Indicators of running-related injuries or running biomechanics related to higher knee joint loading are frequently investigated during running research The aim of our present study was to investigate the differences in muscles around the knee and knee joint reaction forces between novice and experienced runners 15 novice runners and 15 experienced runners were enrolled in this study and underwent 3D running analysis The lower limb muscle forces and knee joint loading of runners were estimated by musculoskeletal modelling based on OpenSim The results showed that novice runners and experienced runners have different running mechanisms, mainly novice runners showed significantly bigger knee loading and muscle forces than the experienced group in most of the stance phases. Considering the proposed relationship between knee joint loading, muscles, and running-related injuries, the novice group may be more prone to lower extremity injuries due to increased loading production during running compared to experienced runners. However, the evidence is not direct that novice runners are at greater risk for running-related injury.
    Keywords: OpenSim; knee; muscle force; running experience; musculoskeletal modeling.
    DOI: 10.1504/IJBET.2023.10061174
  • Automated KL Grading of Knee X-ray Images using Convolutional Neural Network   Order a copy of this article
    by Rajkumar Sadagopan, V.A. Sairam, R. Saranya, N. Sandhiya, V. Shivanie, V. Sapthagirivasan 
    Abstract: Knee osteoarthritis affects people across the globe; Kellegren Lawrence’s grading method is widely used for diagnosing and grading the diseased condition based on X-ray images. The work aims to develop an AI tool trained by deep learning (DL) algorithms to perform automated classification of the grades of deterioration using knee X-ray image. A modified version of Inception-ResNet-v2, which uses transfer learning, is developed as a CNN model to classify the KL grade of knee X-ray images. Open source OAI dataset containing X-ray images used 9786 images with ground truth labelling. A web-based AI tool is developed to categorise knee X-ray images into one of five KL grades. The classifier developed and trained on the OAI dataset (curated) produced 75% validation accuracy, 0.74 validation loss, 78% specificity, 69% sensitivity, and 0.942 AUC. The proposed model is helpful to clinical professionals to know the knee osteoarthritis condition and improves diagnostic quality.
    Keywords: KL grading; convolutional neural network; CNN; transfer learning; knee X-ray; knee osteoarthritis.
    DOI: 10.1504/IJBET.2023.10061491
  • Stepping Towards Independence: A Creative Low-Cost Robotic Ankle-Foot Mechanism for Post-Stroke Rehabilitation   Order a copy of this article
    by Nader Abd El-Rahman Mohamed, Mohamed Abdelkader Aboamer 
    Abstract: This paper presents a robotic ankle-foot system for post-stroke rehabilitation, addressing ankle-joint issues in stroke patients. The system interfaces with MATLAB mobile sensors and consists of two key components: a basic robotic platform and a control system with a shared microcontroller and two IR sensor modules for angle adjustment limits. The mechanical construction utilises plain carbon steel SAE 1030 due to its strength properties. Computer simulations were used to validate the technique, focusing on angular velocity as a key parameter. Analysis of passive/active joint parameters showed significant improvements, with maximal dorsiflexion/plantarflexion angles increasing from 12.0 to 28.0 degrees and 20.5 to 27.0 degrees. This system effectively reduces ankle spasticity and contracture, offering a viable clinical rehabilitation option. Mathematical models for stress, strain, displacement, and safety factor were provided with strong correlation. Promising results have prompted further testing on real stroke survivors and exploration of lightweight, weight-bearing materials for future research.
    Keywords: post-stroke rehabilitation; rehabilitation robotic systems; robotic ankle-foot mechanism; ankle-foot rehabilitation; continuous passive motion; CPM.
    DOI: 10.1504/IJBET.2023.10062147
  • Automated COVID-19 Detection from Chest X-ray and CT Images Using Optimised Hybrid Classifier   Order a copy of this article
    by Madhavi Bhongale, Pauroosh Kaushal, Renu Vyas 
    Abstract: Amidst the global threat of infectious diseases, exemplified by COVID-19, conventional RT-PCR detection methods are time-consuming and potentially misleading. This study introduces an innovative approach, utilising CT and X-ray images as markers for efficient COVID-19 detection. An automatic assessment tool, integrating V-SLBT and GLCM features, optimises image texture analysis for precise classification by a deep belief network (DBN). Enhancing accuracy, a hybrid BWUCOA is integrated into DBN. The tool's workflow involves image pre-processing, optimal texture feature computation, and DBN-based classification. Validation with clinical data from 82 patients attests to a 98% accuracy. Comparative analysis reveals a 1.32% improvement for X-ray and a 2.38% enhancement for CT images over existing methods, underscoring the efficacy of V-SLBT and BWUCOA in refining the classifier's accuracy. This swift and cost-effective tool provides a precise diagnosis for COVID-19.
    Keywords: COVID-19 detection; CT image; chest X-ray image; GLCM; SLBT feature; deep belief network; DBN; black widow updated coronavirus optimisation algorithm; BWUCOA.
    DOI: 10.1504/IJBET.2023.10062228
  • Classifying Muscle Performance of Junior Endurance and Power Athletes using Machine Learning   Order a copy of this article
    by Maisarah Sulaiman, Aizreena Azaman, Noor Aimie Salleh, Muhammad Amir As`ari, Izwyn Zulkapri 
    Abstract: This paper aimed to characterise the muscle performance of endurance and power athletes using machine learning approach. In this regard, electromyography (EMG) features were extracted from the vastus lateralis muscle and used to feed the support vector machine (SVM) classifier. The performance of various EMG features was evaluated based on their classification accuracy, sensitivity, specificity, and F-score. The accuracy was the highest for the feature set selected using the feature selection approach compared to the single feature. Specifically, the performance of sequential backward selection (SBS) was superior to the sequential forward selection (SFS) approach. Meanwhile, based on the SVM classification result, the radial basis function (RBF) kernel performed better than the other investigated kernel types, such as linear, polynomial, and sigmoid kernel. This muscle characterisation of endurance and power athletes may be useful as a muscle-monitoring tool for future talent identification and talent development, particularly in young athletes.
    Keywords: electromyography; EMG; muscle; athlete; endurance; power; distance runner; sprinter; classification; machine learning.
    DOI: 10.1504/IJBET.2023.10062238
  • Research of EEG-Based Emotion Recognition for the Deaf with Feature Fusion   Order a copy of this article
    by Zemin Mao, Xuewen Zhao, Yu Song 
    Abstract: Electroencephalogram (EEG) is better at reflecting emotional changes. The paper aims to explore the deaf brain activity mechanisms of emotional processing with EEG signals. Fifteen deaf subjects were recruited to participate in the emotional induction experiment, and EEG signals were collected when they watched three kinds of emotional movie clips. The frequency domain and brain network features were extracted and fused to capture correlation among EEG channels. The results show that fused features outperform single differential entropy (DE) features in classification indicators (accuracy: 96. 73%, recall: 96.58%, precision: 97.36%, F1 score: 96.42%). In addition, a stacking ensemble learning framework was proposed to classify the fused features, achieving higher classification accuracy than SVM by 2.72%. Investigation into brain activities reveals that deaf brain activity changes mainly in the beta and gamma bands, and the brain regions that are affected by emotions are distributed primarily in the frontal and occipital lobes.
    Keywords: emotion recognition; deaf subject; electroencephalogram; EEG; brain network; differential entropy; DE.
    DOI: 10.1504/IJBET.2023.10062279
  • Gabor Fully Convolutional Network and Ellipse Fitting Technique for Fetal Head Segmentation and Biometry Measurement   Order a copy of this article
    by Ahmed Zaafouri, Hanene Sahli, Radhouane Rachdi, Mounir Sayadi 
    Abstract: This paper introduces a new approach for foetal head segmentation and biometry measurement based on Gabor fully convolutional networks (G-FCN) along with the ellipse fitting technique. A fully convolutional network (FCN) training process based on Gabor features is presented. The new approach tends to accelerate the training stage and gives successful results. The Gabor wavelets with their steerable properties (i.e., their scales and orientations) are able to reinforce the robustness of G-FCN and reduce the training complexity. The proposed model is applied for foetal US image segmentation and foetal head circumference (HC) measurement using the elliptical fit technique. Our datasets are provided from a radiographic sequence of the foetus during different periods of pregnancy. An experimental study is conducted to prove the usefulness of the proposed algorithm for foetal biometric purposes. In addition, the automated approach makes it easier for doctors to diagnose US images.
    Keywords: ultrasound images; ellipse fitting; fully convolutional network; Gabor CNNs; Gabor wavelets.
    DOI: 10.1504/IJBET.2023.10062348
  • EMG Scalogram-Based Classification of Gait Disorders Using Attention-Based CNN: A Comparative Study of Wavelet Functions   Order a copy of this article
    by Pranshu C.B.S. Negi, Balendra ., Shubrendu Shekhar Pandey, Shiru Sharma, Neeraj Sharma 
    Abstract: This study aims to classify gait abnormalities caused by rheumatoid arthritis and prolapsed intervertebral disc using scalograms from the EMG signals. Classifying EMG signals is difficult because of their variability, high dimensionality, and sensor placement. We propose to bridge this gap by using the wavelet transform and attention-based neural networks. The study involved five participants: one with rheumatoid arthritis, two with prolapsed intervertebral disc, and two healthy subjects. The proposed methodology uses four different wavelet functions: complex Gaussian, frequency B Spline, Mexican Hat, and Shannon, to construct scalograms, and an attention-based CNN for classification. A comparison of performance of the proposed algorithm with nine machine learning classifiers: K nearest neighbour, Na
    Keywords: electromyography; EMG; convolution neural networks; attention networks; scalogram; gait analysis.
    DOI: 10.1504/IJBET.2024.10062509
  • A Statistical Shape Modeling Method for Predicting the Human Head from the Face   Order a copy of this article
    by Vi-Do Tran, Phong-Phu Vo, Ngoc-Lan-Nhi Tran, Tien-Tuan Dao, Tan-Nhu Nguyen 
    Abstract: Predicting the back head based only on the face is necessary for generating the full head and skull. We prepared a dataset of 329 surface meshes of the head. These meshes were reconstructed and post-processed from computed tomography (CT) image sets of adult subjects having normal head structures. A novel back head and face sampling technique was also developed for acquiring back head and face features. The relation between the face features and the back head features was trained using four strategies: non-rigid scaling, SSM optimization, partial least squares regression (PLSR), and principal component analysis (PCA). A ten-fold cross-validation procedure was conducted for selecting the optimal training strategies and tuning their parameters. The face features and the predicted back head features formed the head. The mean mesh-to-mesh distances between the predicted and the ground truth back head were (mean
    Keywords: face-to-head prediction; statistical shape modelling; head-to-skull prediction; biomechanical head simulation; partial least squares regression; PLSR; principal component analysis; PCA.
    DOI: 10.1504/IJBET.2023.10062592
  • Early Prediction of Heart Disease Risk using eXtreme Gradient Boosting: A Data-Driven Analysis   Order a copy of this article
    by Hamdi Al-Jamimi 
    Abstract: Heart disease is a leading cause of morbidity and mortality worldwide. Early identification of heart disease risk is critical for timely treatment and prevention of further complications. This study provides a detailed examination of a novel heart disease dataset encompassing 333 cases and 21 features. The study employed the eXtreme gradient boosting (XGBoost) algorithm to develop an intelligent predictive model to detect the likelihood of heart disease at an early stage. The choice of the XGBoost model for this study was apt, considering its strengths in managing structured medical datasets with multiple features, resistance to overfitting, and interpretability for insights into feature importance. Feature selection was utilised to identify the most important predictors for prediction. The findings demonstrate that the Gradient Boosting classifier outperforms other machine learning (ML) techniques with a 99% accuracy rate. The results highlight the capability of ML in aiding the early detection of heart diseases.
    Keywords: heart disease; healthcare; artificial intelligence; AI; machine learning; ML; early prediction.
    DOI: 10.1504/IJBET.2024.10062625
  • The Multifaceted Applications of Al2O3 Nanoparticles in Biomedicine: A Comprehensive Review   Order a copy of this article
    by Vinayakprasanna Hegde 
    Abstract: Aluminium oxide nanoparticles (Al2O3 NPs) have emerged as a promising class of nanomaterials with diverse biomedical applications. Their unique physicochemical and mechanical properties, biocompatibility, and ease of functionalization have led to extensive research exploring their potential in various biomedical fields. This review paper comprehensively summarizes the recent advances in the biomedical applications of Al2O3 NPs, encompassing drug delivery systems, tissue engineering, bioimaging, and diagnostic platforms etc. The discussion focuses on the synthesis methods and surface modifications that enhance the efficacy and biocompatibility of Al2O3 NPs. Additionally, the review shedding light on their potential toxicological implications and biodegradability. Overall, this paper provides valuable insights into the current state of research on Al2O3 NPs in the biomedical domain, fostering advancements in healthcare and medical technologies.
    Keywords: Biomedicine; nanoparticles; Al2O3; toxicity.
    DOI: 10.1504/IJBET.2024.10062831
  • S-R2F2U-Net: A single-stage model for teeth segmentation   Order a copy of this article
    by Mrinal Kanti Dhar, Mou Deb 
    Abstract: Precision tooth segmentation is crucial in the oral sector because it provides location information for orthodontic therapy, clinical diagnosis, and surgical treatments. In this paper, we investigate residual, recurrent, and attention networks to segment teeth from panoramic dental images. Based on our findings, we suggest three models: single recurrent R2U-Net (S-R2U-Net), single recurrent filter double R2U-Net (S-R2F2U-Net), and single recurrent attention enabled filter double (S-R2F2-Attn-U-Net). Particularly, S-R2F2U-Net, as emphasised in the title, outperforms state-of-the-art models in terms of accuracy and dice score. A hybrid loss function combining cross-entropy loss and dice loss is used in training. In addition, it reduces around 45% of model parameters compared to the original R2U-Net. Models are trained and evaluated on the UFBA-UESC dataset that contains 1,500 extra-oral panoramic X-ray images and divided into ten categories based on the structural variations. S-R2F2U-Net achieves 97.31% accuracy and 93.26% dice score. Codes are available on
    Keywords: tooth segmentation; semantic segmentation; deep learning; recurrent module; attention module.
    DOI: 10.1504/IJBET.2024.10063262
  • Non-contact Heart Rate Measurement from Face Video Sequences using SIFT and FastICA.   Order a copy of this article
    by Hemlata Biradar, Jayanand Gawande 
    Abstract: Traditional techniques of heart rate (HR) measurement rely upon optical or electronic sensors. This research proposes a HR measuring method in a non-contact way that enables a cardiac pulse's physiological examination without using electrodes. The proposed method is based on automated face tracking and blind source separation of the colour channels into separate components from colour video recordings of the human face using fast independent component analysis (FastICA). To reduce noise caused by face motion, we used the scale-invariant feature transform (SIFT) and affine transform (AT). The experimentation is carried out on COHFACE and DROZY datasets. The HR predicted by the proposed method and by the finger blood volume pulse sensor is compared using the Bland-Altman and correlation analysis. The proposed method when compared to other methods utilising comparable datasets outperforms in terms of mean absolute error (MSE), root mean square error (RMSE), standard deviation (SD), and correlation coefficient (CC).
    Keywords: heart rate; HR; scale invariant feature transform; SIFT; fast independent component analysis; FastICA; photoplethysmography; PPG; power spectrum.
    DOI: 10.1504/IJBET.2024.10063280
  • Modeling and simulation of electroencephalography (EEG) instrumentation to study the epileptic seizure: A noise analysis approach   Order a copy of this article
    by Sunil Choudhary, Tushar Kanti Bera 
    Abstract: Electroencephalography (EEG) is extremely useful for diagnosing and treating various brain diseases and disorders. An EEG instrumentation which consists of analogue amplifiers, filters, digitisers, and data acquisition system, plays a crucial role in designing efficient EEG acquisition-hardware for acquiring low-amplitude EEG signals. This paper presents a comprehensive simulation study conducted on the various EEG instrumentation and their noise analysis to design a high gain and low noise EEG amplifier at low-cost. Different EEG-amplifier circuits are developed in NI-Multisim and noise-analysis has been studied to identify the best EEG-measurement system. The simulation results show that the EEG amplifier developed with AD8428 and OP07 shows the highest gain (22 8k), high SNR (70.93 dB), and high CMRR (136 dB) within a low noise level for the EEG signal bandwidth. The present work provides a guideline for designing EEG circuits with high gain and low-noise levels to acquire brain signals for neuroscientific studies.
    Keywords: epileptic seizures; electroencephalography; EEG; low-cost and low-noise EEG instrumentation; circuit simulation; high CMRR; high SNR; noise analysis.
    DOI: 10.1504/IJBET.2024.10063349
  • Alterations of generic musculoskeletal models to incorporate realistic knee joint and muscle geometry for biomechanical analyses during healthy gait: A Narrative Literature Review   Order a copy of this article
    by Shivangi Giri, Ravi Prakash Tewari 
    Abstract: Knee is an important weight-bearing 6-degree-of-freedom (DOF) joint that is essential for stable locomotion. However, the majority of lower limb musculoskeletal (MSK) models only include one DOF and hence fail to represent the true biomechanics of the knee. For models with multi-DOF knee anatomically realistic modelling of muscle architecture is crucial in representing the mechanical stability of the entire limb. The purpose of this narrative review, therefore, was to report state-of-the-art knowledge on the existing generic rigid-body MSK models that have incorporated: 1) multi-DOF knee; 2) multi-line characterisation of muscle geometry, to analyse normal healthy human gait. 15 studies accommodated multi-DOF knee joint, however majority of them retained the single-line oversimplified muscle geometry. Those that focused on better characterising muscle geometry (n = 8) used only single-DOF knee joints. Most importantly, this review showed that no generic MSK model exists that incorporates realistic representations of both knee and muscle volume.
    Keywords: musculoskeletal modelling; knee; muscle redundancy; review; biomechanics.
    DOI: 10.1504/IJBET.2024.10063535
  • A Deep Learning approach for the augmented diagnosis and prediction of infectious Lung Diseases   Order a copy of this article
    by Geetha R, Umarani Srikanth, Kamalanaban E 
    Abstract: The pandemic coronavirus is an alarming threat to public health nowadays causing severe acute lung and bronchial infection that incurs a high fatality rate in humans. Researchers vigorously work in this area to find solutions for this critical issue by all means. On the other hand, tests to be carried out to determine the survival time of coronavirus infection across different communities of the population is a long-term need. Herein, this research describes a robust deep neural network to diagnose the suspicious patient’s chest X-ray (CXR) in detecting the presence of infection rapidly. This simple and rapid scalable approach has the capacity of immediate application in coronavirus diagnosis as well as predicting the spread and infection probability for every individual put under-diagnosis depending upon their health and societal parameters. Our robust deep neural network yields the best result of 97.87% accuracy and is user-friendly compared to existing methods.
    Keywords: deep learning; convolutional neural network; CNN; chest X-ray; CXR; rectified linear units; ReLUs; infectious lung disease.
    DOI: 10.1504/IJBET.2024.10063639