Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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, Naïve Bayes, support vector machine, decision tree, logistic regression, random forest, AdaBoost, gradient boost, and XGBoost was conducted. Out of the nine machine learning classifiers that were tested, XGBoost achieved the highest accuracy of 90.38%, however, in comparison to this the performance of the proposed algorithm was much better, with an accuracy of 99% and precision of 99%. These results indicate that this approach is highly effective in accurately categorising EMG signals.

The field of biomedical research has witnessed significant advancements in recent years with the advent of computer vision models for automating cell segmentation. However, the application of such models specifically for neuronal cells is still being determined. Previous studies in computer vision have primarily focused on general cell segmentation, and only a few have investigated the application of specific models for neuronal cells. This gap in research inspired the current study to investigate the application and evaluation of U-Net, mask region-based convolutional neural network (mask R-CNN), and Detectron2 models for automating the cell segmentation of microscopic images of neuronal cells. The study utilised three evaluation metrics, including intersection over union, size, and robustness, to determine the accuracy and efficiency of the models. Results showed that Detectron2 outperformed the other two models, demonstrating higher accuracy and robustness in neuronal cell segmentation, enabling new insights and applications in biomedicine through efficient evaluation of neurological disorders treatments.

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 9,786 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.

The development of motorised bionic arms has been a significant focus in prosthetics. This study explored the design and implementation of a motorised bionic arm, with particular emphasis on enhanced hand gesture accuracy and reliability. Initially, the functional circuit and algorithm were designed, followed by the development of a motorised framework to control a 3D-printed real-time bionic arm, utilising steady state visually evoked potential (SSVEP) stimuli. The system's effectiveness was evaluated through accuracy and reliability tests on repeatability of gestures such as hand open, hand close, thumb up, and pointing, induced by four flickering frequencies between the range of (10–20 Hz). Findings revealed a mean accuracy of 97.5%, with individual accuracy rates ranging from 88.75% to 100%. Over the 30 tests, average angles were 168.8° (pointing), 169° (thumb up), 174.5° (open), and 132.17° (close), demonstrating consistent performance. These results contribute valuable insights into the design, control, and evaluation of motorised prosthetic devices, providing users with a more intuitive and reliable means of restoring hand functionality.

The brain connectivity network (BCN) is considered to be an effective approach for analysing brain functionality. The EEG-based BCN considers electrodes as a node and functional similarity between EEGs from corresponding nodes as edge. The EEG dataset available for the evaluation might contain variable number of nodes. The variable number of nodes provides biased results while performing graph classification. Hence, the study proposed a strategy to mitigate the aforementioned challenge. The proposed method characterises variable node BCN with the help of network level metrics as a feature vector. The extracted metrics characterises the network as a whole and do not rely on the number of nodes. Two public datasets, with 16 electrodes and 19 electrodes EEG data, are used to test the suggested method. The classification is performed with the stacked-ensemble classification technique. Finally, the quantitative analysis of the proposed approach represents a significant performance with the 92.34% classification accuracy.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Latin shoes are considered an essential part of Latin dance, but wearing the shoes may contribute to dance injuries. The primary purpose of this article was to compare the biomechanics and muscle activity differences between 7.5 cm heel-height Latin shoes and 5.5 cm Latin shoes based on the bounce step during Jive performance. One-dimensional statistical parametric mapping (SPM1D) was used to analyse the data. 7.5 cm Latin shoes showed significantly greater peak dorsiflexion during the 4.56%-7.65%, as well as the VALR and peak vertical force significantly increased compared to 5.5 cm Latin shoes. These results suggest that a higher heel leads to a greater lower limb muscle activation in dancers and enhanced medial and lateral ankle muscle control. The lower extremity strength of dancers and the ability to control their limbs should be considered an essential element when reasonably raising Latin heels.