Journal of Mechanics in Medicine and Biology

Gait analysis is one of the highly focused research areas inviting attention of academicians and practitioners. Being a prominent analytical tool to detect the abnormalities and onset of musculoskeletal disorders (MSDs), abundant literature is published in this area. Though a large number of gait analysis methods (GMSs) for motion analysis are available in the literature, very few studies are available to showcase the comparative analysis and applicability of various techniques. This study seeks to provide useful recapitulation of various gait measurement methods and interrogates the relative prevalence of various GMS based on the applications by various researchers. The paper summarizes the findings of a systematic review on gait measurement methods carried out using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. Fifty-three research papers published during 1998–2021 were included for critical review. The categories of gait measure ment systems were further compared based on certain criteria such as portability, cost, and accuracy, and ranked using analytic hierarchy process (AHP). The review helped to encapsulate various aspects of gait analysis, especially, the types of tasks, types of gait parameters (GPs), and the gait measurement systems adopted. The study provides suitable mappings between these factors and offers comparison between the gait measurement systems using a specially devised capability index. It further provides useful guidelines for selecting the gait systems based on the criteria.

We previously developed a simplified finite element (FE) “unit” model to reproduce the mechanical interaction between the smooth muscle layer and elastic lamina (EL) in the aortic media. Nevertheless, whether this simplified FE model can represent the structure of a real medial wall and whether its modeling technique can help in developing a highly sophisticated and structure-based aortic FE model should be determined. Therefore, this study aimed to computationally represent EL buckling in the aortic medial ring at an unloaded state based on the integrated unit models and reproduce transmural variations in EL waviness across the vascular wall. We confirmed that the inner and outer layers of the medial wall were relatively subjected to compressive and tensile residual stresses, respectively, at the unloaded state, implying that the ring model will open spontaneously when it is radially cut. In addition, the residual stresses computed under such a stress-free condition were comparable to the analytically estimated values, partially supporting the validity of our modeling approach. Although further study is still required, the information obtained in this study will greatly improve the understanding of basic aortic physiology and pathophysiology and provide a basis for performing more sophisticated computational modeling of the aortic wall.

Despite the constant expansion of the global alginate market and the emergence of a plethora of new brands to meet the rising demand, the literature remains insufficient for the selection and preparation of optimized microcapsules. In this study, the cell transplantation potentials of two commercial alginate brands (Sigma and FMC Biopolymers-PRONATAL) with different M/G ratios were compared in vitro. Different brands of alginates were crosslinked with increasing concentrations of calcium chloride (1.5%, 2%, and 3%w/v). Utilizing size/dimension measurements, compression, swelling, and degradation tests, scanning electron microscopy (SEM), and bright field (BF) imaging, the intrastructure of each experimental group was investigated. Moreover, the cell viability of HDF-encapsulated Ca-alginate microcapsules was evaluated. According to SEM and compression analysis, PROTANAL KF200 with a lower M/G ratio was found to be more rigid and robust. Due to the different proportions of MM and GG blocks, Sigma Ca-alginate microcapsules exhibited greater flexibility but lower hardness than PRONATAL KF200 microcapsules. However, there was no significant difference in cell viability between the alginate microcapsules. In future in vivo experiments, encouraging cell transplantation outcomes could be monitored by the choice of PROTANAL KF200 sodium alginate polymer for the microcapsule preparation.

Objective: Most studies in epileptic seizure detection and classification focused on classifying different types of epileptic seizures. However, localization of the epileptogenic zone in epilepsy patient brain’s is paramount to assist the doctor in locating a focal region in patients screened for surgery. Therefore, this paper proposed robust models for the localization of epileptogenic areas for the success of epilepsy surgery. Method: Advanced feature extraction techniques were proposed as effective feature extraction techniques based on Electroencephalogram (EEG) rhythms extracted from Fourier Basel Series Expansion Multivariate Empirical Wavelet Transform (FBSE-MEWT). The proposed extracted EEG rhythms of $\delta ,\theta ,\alpha ,\beta$ and $\gamma$ features were used to obtain a joint instantaneous frequency and amplitude components using a sub-band alignment approach. The features are used in Sparse Autoencoder (SAE), Deep Belief Network (DBN), and Support Vector Machine (SVM) with the optimized capability to develop three new models: 1. FMEWT–SVM 2. FMEWT_SAE–SVM, and 3. FMEWT–DBN–SVM. The EEG signal was preprocessed using a proposed Multiscale Principal Component Analysis (mPCA) to denoise the noise embedded in the signal. Main results: The developed models show a significant performance improvement, with the SAE–SVM outperforming other proposed models and some recently reported works in literature with an accuracy of 99.7% using $\delta$-rhythms in channels 1 and 2. Significance: This study validates the EEG rhythm as a means of discriminating the embedded features in epileptic EEG signals to locate the focal and non-focal regions in the epileptic patient’s brain to increase the success of the surgery and reduce computational cost.

In applicative biosensor technology, mathematical modeling plays an indispensable role in explaining the transport of electrical signals by analyzing the binding behavior of the biochemical enzyme inhibitors to the target molecule. The biosensors are extensively used in clinical diagnostics, drug detention, food analysis and environment monitoring because they are highly sensitive, reliable and relatively cheap. Dynamic mathematical models used for biological investigation serve the purpose efficiently with very reasonable outcomes. In this study, a time-independent mathematical model for biosensor enzyme–substrate–inhibitor system under uncompetitive inhibition based on the nonlinear diffusion equations taking into consideration the kinetic rate constants and the initial concentrations of enzyme, substrate and inhibitor has been formulated and solved analytically using variational iteration method (VIM). The reliability and accuracy has been proved by comparing our results with the solution obtained by standard VIM. Chosen biosensors showed desirable sensitivity, selectivity and potential for application on real samples. They are frequently made to prevent interference from undesirable components that are present in the monitored system. The VIM is effectively and easily used to obtain solution of nonlinear equations accurately. Further, the solution has been discussed exhaustively for different values of reaction parameters avoiding linearization and unrealistic assumptions and the results obtained significantly agree with existing literature.

Drilling through bone is widely performed in orthopedic surgeries to fix the plate and implant using bone screws. Clinical observation revealed that the mechanical and thermal damages driven by the uncontrolled drilling operations loosen the implants soon after their fixations. During fracture treatment and reconstruction surgery, the quality of the hole generated is to be considered as an important metric, and therefore drill feed rate and spindle speed, apparent bone density, drill bit geometry, and skill of the surgeon are the parameters to influence the success of the bone drilling procedure. The generation of temperature due to the conversion of mechanical work around the osteotomy site leads to irreversible thermal damage to the bone tissue, when it exceeds 47^∘C. So, a non-invasive method which can identify the probability to not get a thermal osteonecrosis at the chosen drill spot can help the surgeon to strongly fix the implant around the defect site. To make this happen, the in-situ bone temperatures measured in the earlier study when drilling with a 3.20mm diameter drill bit was used for the investigation. As a procedure, the test statistics and critical test statistics values were calculated, and found that the log normal and gamma density functions could best describe the bone drilling temperatures, when drilling in the radial and circumferential directions, respectively. Additionally, the CT data sets of the patient-specific femur were used to reconstruct the bone both with geometry and drilling temperature data. The Kolmogrov–Smirnov test performed to check the validity of the temperature distribution models showed a good agreement with the experimental results. The outcomes of the method can help the surgeon to choose a suitable location for the strongest fixation using CT data sets of the patient as the only input.

An adaptive impedance control method based on dexterity for compliant interaction is proposed for the problem of compliance and motion performance of the human’s healthy side interacting with the manipulator in bilateral mirror rehabilitation motion-assisted training, and constructs a local virtual force field to hinder the movement of the manipulator to the low motion performance region, thus improving the motion performance of the manipulator. Firstly, the dynamic model of the manipulator and the human–robot interaction model based on impedance control are established. Then, a dexterity index based on the condition number is established, and an adaptive impedance control method based on the dexterity is proposed to construct a local virtual force field to hinder the movement of the manipulator to the low motion performance region. Finally, the effectiveness of the proposed method is demonstrated by experiments. The results have shown that the adaptive impedance control method based on dexterity can construct a local virtual force field, which can constrain the motion of the manipulator and keep the robot with good motion performance. It also laid the foundation for the training strategy of bilateral mirror rehabilitation.

In the brain, the abnormal growth of cells or solid intracranial neoplasm is known as brain tumor, which is one of the world’s most tedious diseases. Hence, there is a need for segmentation and classification of the brain tumor accurately. It is difficult to separate the tumor tissues and other tissues from the brain. The major aim of this research is to use magnetic resonance imaging (MRI) segment and classify the brain tumor and all the abnormalities in the brain. The MRI is initially fed into the pre-processing system and then it is segmented using the region-growing segmentation algorithm in the pre-operative MRI. It produces the segmented area and it is forwarded for classification. In the classification step, the Honey Badger Algorithm (HBA) is applied to train the U-Net classifier. The tumor tissues and the different types of tissues or abnormalities in brain tumors are classified by this algorithm. Overall, the post-operative and pre-operative MRI brain tumor segmentation and classification consist of the same steps. To find out the pixel changes, both the segmented output of pre-operative and post-operative MRI was compared. It helps in finding the emerging tumor after surgery and the success rate of surgery. Based on pre-operative MRI, the implemented scheme has maximum specificity, sensitivity, and accuracy of 0.977, 0.968, and 0.949.

A foot posture index (FPI-6) measurement is well presented as a straightforward clinical tool for diagnosing foot posture in patients. However, the FPI-6 diagnosis is mostly performed by a medical practitioner manually utilizing both palpation and rulers. Digital photos of the feet had been considered to digitize the FPI-6 assessment, unfortunately, the two-dimensional image is inadequate to identify two-palpation based on six criteria of FPI-6. This paper proposes a mechanical platform design for generating a three-dimensional (3D) model of the patient’s foot. The design is aimed to ease the test process of a large number of samples or participants as statistical data to estimate the foot posture based on the FPI-6 method. The 3D-generated models of the foot belonging to 37 children aged below 12 years old show that the FPI-6 Criteria 1 and 4 can be evaluated more clearly in the 3D models compared with flat 2D photos.

The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient’s survival, treatment process, and tumor progression computation. However, in the medical field, picture segmentation and classification are more important and difficult processes. Typically, the Magnetic Resonance Imaging (MRI) modality can detect malignancy. Segmenting tumor images with respect to Cerebrospinal Fluid (CSF), Grey Matter (GM), and White Matter is the most important task in MRI identification or classification (WM). The introduction of medical image analysis based on radiology pictures is a result of the significant contributions of engineering, data sciences, and medicine. The precise and automatic segmentation of tumors affords excessive support to doctors in the medicinal area, speed detection in the treatment process, computer-aided operation, radiation treatment and so on. Thus, Remora Aquila optimization (RAO)-enabled deep learning is devised for tumor classification and its severity classification. The deep learning approach is utilized for categorizing tumors as normal or abnormal as well as their severity grades. The RAO-aided deep learning system achieved improved performance with prediction error, specificity, sensitivity and testing accuracy of 0.072, 0.905, 0.925, and 0.917, respectively.