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Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.

The evolution of sensor technologies and real-time data processing has amplified the practicality of incorporating behavioral characteristics within security frameworks. Keystroke dynamics, in particular, has emerged as a prevalent behavioral biometric owing to the ubiquitous use of devices like mobile phones and computers, all reliant on password-based security systems. This study unveils an innovative authentication framework using leveraging deep learning algorithms, tapping into force and displacement data derived from the intricate abduction movements of the right index finger as a distinctive biometric trait. To ascertain its efficacy, we meticulously optimized this novel algorithm while benchmarking it against established deep learning models—Convolutional Neural Networks (CNNs), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and one-dimensional CNN (1D-CNN). The subsequent evaluation encompassed a comprehensive comparative analysis of their performance metrics. The findings of this evaluation are compelling, demonstrating an average F1 score of 75.5% in validation data alongside an impressive average accuracy rate of 99.4%. These outcomes unequivocally highlight the precision and reliability inherent in utilizing force and displacement patterns as behavioral biometrics. Equally noteworthy is the system's display of a remarkably low False Acceptance Rate (FAR) of 0.27%, positioning it as a promising contender for seamless integration within advanced security systems. In essence, this research not only showcases the potential of leveraging nuanced behavioral traits but also emphasizes the practicality and robustness of employing force and displacement patterns as precise indicators in the realm of behavioral biometrics for enhanced system authentication and security.

The present study investigates the effect of wearable sensor placements and the use of various machine learning (ML) algorithms for human gait pattern recognition based on temporal gait speeds using wearable multichannel sensor data. Therefore, classifying human gait from features extracted from biomechanical sensor signals and evaluating the effect of using these sensors on gait biomechanics can be successfully achieved with a machine learning approach. In this study, firstly, IMU (Inertial Measurement Unit) and GON (Goniometer) sensor features were extracted for machine learning input using the sliding windows method, and these features were applied to ML classifiers to classify human gait speed. Our experimental findings show that multi- and fusion sensor models provide superior classification performance compared to single sensor models, and the highest accuracy is achieved with the ‘FUS09’ fusion sensor model and SVM-based classifier. The classification accuracies of the FUS10 fusion sensor model, where all sensor parameters were combined, the FUS09 model, where the GON_ANK and IMU_Ft parameters in this model were excluded, are 0.895 and 0.901, respectively. Consequently, wearable biomechanical sensor data and machine learning approach can be easily preferred in multiple human activity recognition.

A characteristic average and biofidelity response corridors are commonly used to represent the average behaviour and variability of biomechanical signal data for analysis and comparison to surrogates such as anthropometric test devices and computational models. However, existing methods for computing the characteristic average and corresponding response corridors of experimental data are often customized to specific types or shapes of signal and therefore limited in general applicability. In addition, simple methods such as point-wise averaging can distort or misrepresent important features if signals are not well aligned and highly correlated. In this study, an improved method of computing the characteristic average and response corridors of a set of experimental signals is presented based on arc-length re-parameterization and signal registration. The proposed arc-length corridor method was applied to three literature datasets demonstrating a range of characteristics common to biomechanical data, such as monotonic increasing force-displacement responses with variability, oscillatory acceleration-time signals, and hysteretic load-unload data. The proposed method addresses two challenges in assessing experimental data: arc-length re-parameterization enables the assessment of complex-shaped signals, including hysteretic load-unload data, while signal registration aligned signal features such as peaks and valleys to prevent distortion when determining the characteristic average response. The arc-length corridor method was shown to compute the characteristic average and response corridors for a wide range of biomechanical data, while providing a consistent statistical framework to characterize variability in the data. The arc-length corridor method is provided to the community in the freely available and open-source software package, ARCGen.

Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.

Background: Wearable inertial sensors are essential tools in biomechanics and sports science for assessing gait in real-world conditions. This study explored gender-based differences in biomechanical walking patterns among healthy Greek athletes using the BTS G-Walk system, focusing on key gait parameters to inform gender-specific training and rehabilitation strategies. Methods: Ninety-five healthy athletes (55 men, 40 women), aged 18 to 30 years, participated in this study. Each athlete performed a standardized 14 m walk while 17 biomechanical gait parameters were recorded using the BTS G-Walk inertial sensor. Statistical analyses were conducted using SPSS to assess gender differences and left–right foot symmetry. Results: No significant asymmetry was found between the left and right feet for most gait parameters. Men exhibited longer stride lengths (left: p = 0.005, Cohen’s d = 0.61; right: p = 0.009, Cohen’s d = 0.53) and longer stride and gait cycle durations (left: p = 0.025, Cohen’s d = 0.52; right: p = 0.025, Cohen’s d = 0.53). Women showed a higher cadence (p = 0.022, Cohen’s d = −0.52) and greater propulsion index (left: p = 0.001, Cohen’s d = −0.71; right: p = 0.001, Cohen’s d = −0.73), as well as a higher percentage of first double support (p = 0.030, Cohen’s d = −0.44). Conclusions: These findings highlight the impact of biological and biomechanical differences on walking patterns, emphasizing the need for gender-specific training and rehabilitation. The BTS G-Walk system proved reliable for gait analysis, with potential for optimizing performance, injury prevention, and rehabilitation in athletes. Future research should explore larger, more diverse populations with multi-sensor setups.

Background/Objectives: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment. Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.

This study investigates the effectiveness of wearable technology in predicting injury rates among athletes, focusing on both micro and severe injuries. Over a 20-week period, 80 physical culture students were divided into an experimental group, using wearable devices for real-time data monitoring, and a control group employing traditional training methods. The study utilized a range of wearable sensors to collect comprehensive physiological and biomechanical data, which was analyzed using custom Python-based tools. Results indicated a significant reduction in micro injuries within the experimental group, affirming the hypothesis that wearable technology can decrease injury incidence through personalized training adjustments. However, the impact on severe injuries was not statistically significant, highlighting the technology's limitations in predicting and preventing acute injuries. This research underscores the potential of wearable devices to enhance athlete safety through data-driven insights but also points to the need for further studies to fully understand and leverage technology in preventing more serious injuries. The findings have important implications for sports science, suggesting a paradigm shift towards more technologically integrated training regimes to optimize health outcomes and performance in athletic populations. Keywords: wearable technology, athlete injury prevention, real-time data monitoring, sports science, biomechanical data analysis, micro injuries, severe injuries, personalized training adjustments.

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics.The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.