The ability to maintain our body's balance and stability in space is crucial for performing daily activities. Effective postural control (PC) strategies rely on integrating visual, vestibular, and proprioceptive sensory inputs. While neuroimaging has revealed key areas involved in PC-including brainstem, cerebellum, and cortical networks-the rapid neural mechanisms underlying dynamic postural tasks remain less understood. Therefore, we used EEG microstate analysis within the BioVRSea experiment to explore the temporal brain dynamics that support PC. This complex paradigm simulates maintaining an upright posture on a moving platform, integrated with virtual reality (VR), to replicate the sensation of balancing on a boat. Data were acquired from 266 healthy subjects using a 64-channel EEG system. Using a modified k-means method, five EEG microstate maps were identified to best model the paradigm. Differences in each microstate maps feature (occurrence, duration, and coverage) between experimental phases were analyzed using a linear mixed model, revealing significant differences between microstates within the experiment phases. The temporal parameters of microstate C showed significantly higher levels in all experimental phases compared to other microstate maps, whereas microstate B displayed an opposite pattern, consistently showing lower levels. This study marks the first attempt to use microstate analysis during a dynamic task, demonstrating the decisive role of microstate C and, conversely, microstate B in differentiating the PC phases. These results demonstrate the utility of microstate technique in studying temporal brain dynamics during PC, with potential applications in the early detection of neurodegenerative diseases.

This study explores alterations in cortical activity elicited by the BioVRSea experiment. This innovative postural control (PC) paradigm incorporates a moving platform synchronized with an immersive virtual reality (VR) environment that simulates the maintenance of upright posture on a boat at sea. We analyzed electroencephalogram (EEG) data from 290 subjects to assess changes occurring during the transition from visual (PRE) to visual-motor (75%) stimulation phases. The findings reveal statistically significant absolute and relative power spectra differences between the visual and visuomotor stimulation phases across various cerebral regions and frequency bands, suggesting distinct neural processing mechanisms operative in each phase.

Many work activities may elicit a biomechanical overload. Several studies have reported that work-related exposure, such as lifting loads, are associated with the development of musculoskeletal pathologies. Recently, in the field of physical ergonomics, several quantitative methods have been developed to assess lifting actions and its biomechanical risk. Moreover, several studies have shown that the combined use of artificial intelligence and wearable sensors provides an improvement in biomechanical risk assessment. In the present study, we assessed the feasibility of Machine Learning algorithms to discriminate biomechanical risk classes defined by means of the Revised NIOSH Lifting Equation. Surface electromyography signals were acquired using wearable sensors placed on trapezius muscles during lifting load tasks performed on 10 healthy volunteers. The sEMG signals were processed in order to extract several frequency-domain features to fed Machine Learning algorithms. Interesting results were obtained in terms of evaluation metrics for a binary NO-Risk/Risk classification; specifically, Gradient Boost algorithm reached accuracy and Area under the Receiver operating curve equal to 0.921 and 0.979 respectively. Study results, although preliminary, proved the feasibility of the proposed methodology to assess the biomechanical risk in a quantitative and automatic way.

Motion sickness influences postural control in virtual reality environments, but its impact on cortical neurodynamics remains unclear. This study investigated the association between postural sway-measured via center of pressure excursion-and regional power spectral density changes in participants with varying motion sickness sensitivity during the RioVRSea paradigm. Linear mixed-effects models revealed significant associations between center of pressure excursion and power spectral density in the left temporal lobe in $\theta$ and $\beta$ bands. Moreover, moderation analysis demonstrated that motion sickness sensitivity modified this relationship in the left temporal $\beta$ band. These findings highlight the role of motion sickness in modifying the cortical neurodynamics of postural control in dynamic virtual reality environments.

The assessment of Postural Control (PC) is crucial for identifying balance deficits, understanding the underlying mechanisms of instability, and guiding targeted interventions. One widely used approach to evaluate PC involves analyzing both the Center of Pressure (CoP) and Electroencephalography (EEG) data. This study aims to investigate differences in PC responses between genders and age groups (Over40 and Under 40) through the analysis of CoP and EEG across different experimental phases designed to evoke PC strategies. The BioVRSea bio-measurement system, which combines virtual reality (VR) and a mobile platform, was used to assess these differences. The CoP analysis revealed statistically significant differences between genders, particularly during transitions from dynamic visual stimuli to static visual conditions. Men exhibited greater sway than women, indicating a higher susceptibility to habituation phenomena. EEG power comparisons across frequency bands (Delta, Theta, Alpha, Beta, and Low Gamma) showed that the Beta and Low Gamma bands consistently differentiated between the sexes, with males exhibiting higher power levels than females. When comparing age groups, significant results were observed during the phases with only visual stimuli (PRE and POST). Subjects over 40 displayed greater sway during these phases, suggesting a deterioration in vestibular and proprioceptive function. EEG analysis revealed that the Theta band consistently showed the most significant age-related differences across all experimental phases. These findings are valuable for predicting fall risk, particularly with age-related decline, and provide insights into the influence of visual, vestibular, and somatosensory systems on PC. Additionally, the results contribute to understanding anatomical factors involved in responses to multiple stimuli, as postural patterns may indicate underlying pathologies.

The Postural Control (PC) system is essential for maintaining balance, facilitating movement, and managing autonomic responses necessary for daily activities. Dysfunction in this system, caused by either disease or overstimulation, can lead to vertigo, dizziness, and other balance-related disorders, significantly affecting individuals' quality of life. These issues are further compounded by age-related degeneration and conditions such as Parkinson's disease (PD) and concussions. Current diagnostic methods for those disorders are primarily qualitative, highlighting the need for quantitative approaches. This paper presents the BioVRSea system, an innovative tool introduced at Reykjavik University's Motion Sickness and Postural Control Lab. The system combines virtual reality (VR) with a moving platform to simulate maritime environments while recording comprehensive biosignals, including EEG, EMG, CoP, and ECG. By examining responses across different phases of the simulation, the BioVRSea system has proven effective in distinguishing between healthy individuals and those with specific conditions, such as concussions and early-stage PD. Our research involving almost 500 participants demonstrates the system's capability to capture significant data on PC dynamics. The integration of VR and machine learning techniques allows for the development of predictive biomarkers, enhancing the precision of diagnostic processes. The findings indicate distinct biosignal patterns among various cohorts, underscoring the potential of the BioVRSea system in advancing PC disorder diagnosis and treatment. Future research will focus on optimizing feature selection and classification algorithms, aiming to improve clinical assessments and interventions for neurodegenerative diseases and other PC-related conditions.

Work-related musculoskeletal disorders (WRMDs) affect millions of workers worldwide, posing substantial economic burdens on industries and healthcare systems. Prolonged exposure, repetitive tasks, awkward postures and intensive efforts are keys factors contributing to the development of WRMDs. Several quantitative or semi-quantitative methodologies are employed to evaluate the biomechanical risk and to prevent WRMDs in the occupational ergonomics field. However, these methods are still time-consuming and operator-dependent. Recently, the application of wearable sensors combined with artificial intelligence is providing remarkable results in terms of biomechanical risk assessment in the occupational ergonomics field. Therefore, in the present work, we examined the potential of Machine Learning (ML) models to differentiate between biomechanical risk categories as defined by the Revised NIOSH Lifting Equation (RNLE). The ML models were trained using time-domain and frequency-domain features extracted from surface electromyographic (sEMG) signals obtained from the neck extensor muscles of four healthy subjects during weight-lifting tasks. The study findings indicated that the Support Vector Machine algorithm performed the best, achieving an accuracy of 83.6% and an area under the receiver operating characteristic curve of 89.9%. However, the study was limited by its small sample size and the restricted age range of the volunteers. Future research involving a larger and more diverse population in terms of age and number of subjects could further validate the effectiveness of the proposed methodology.

IntroductionThere is accumulating evidence that many pathological conditions affecting human balance are consequence of postural control (PC) failure or overstimulation such as in motion sickness. Our research shows the potential of using the response to a complex postural control task to assess patients with early-stage Parkinson's Disease (PD).MethodsWe developed a unique measurement model, where the PC task is triggered by a moving platform in a virtual reality environment while simultaneously recording EEG, EMG and CoP signals. This novel paradigm of assessment is called BioVRSea. We studied the interplay between biosignals and their differences in healthy subjects and with early-stage PD.ResultsDespite the limited number of subjects (29 healthy and nine PD) the results of our work show significant differences in several biosignals features, demonstrating that the combined output of posturography, muscle activation and cortical response is capable of distinguishing healthy from pathological.DiscussionThe differences measured following the end of the platform movement are remarkable, as the induced sway is different between the two groups and triggers statistically relevant cortical activities in α and θ bands. This is a first important step to develop a multi-metric signature able to quantify PC and distinguish healthy from pathological response.

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Heart rate variability (HRV) is commonly used as a clinical measure to assess autonomic nervous system function and overall health. Various factors, including age, gender, physical fitness, and physiological conditions, can influence HRV. The regulation of heart function is crucial for maintaining a stable internal environment, and reduced HRV may indicate health impairment. This study focuses on evaluating ECG features during a complex postural control task in a virtual reality (VR) environment to determine their significance in classifying subjects who experienced motion sickness (MS) symptoms. The study utilized the BioVRSea setup, which combines VR with a platform that simulates waves to induce MS in subjects. HR, along with other biosignals, was measured using advanced ECG sensors. A motion sickness questionnaire was used to assess and quantify MS symptoms, and a binary index was introduced to differentiate individuals based on symptom changes. Statistical analysis and ML models were employed to determine the most significant HRV features in classifying subjects with MS symptoms during the BioVRSea task. Seventy healthy volunteers participated in the experiment, and a total of 124 HRV features were obtained from the ECG signals considering all the different phases of the experiment. The statistical analysis revealed six features that showed statistically significant differences between subjects with and without MS symptoms. ML models, including Decision Tree, Random Forest, and Linear Regression algorithms, were trained using different wrapper feature selection techniques. The best-performing model achieved an accuracy of 74.2%, precision of 61.1%, recall of 64.9%, and F1 score of 83.4%. This study highlights the importance of ECG features in classifying MS symptoms during a complex postural control task in a VR environment. The findings contribute to understanding of the autonomic responses and cardiac control mechanisms associated with MS. The results can have implications for future research on MS susceptibility and the development of personalized interventions to mitigate MS symptoms.

Knee osteoarthritis (OA) is a prevalent condition characterized by the gradual breakdown of cartilage in the knee joint. In this study, we aimed to develop synthetic 3D-printed knee joint models to evaluate the mechanical and functional properties of degenerative cartilage. Two subjects, one with degenerative cartilage and one healthy control, were included in the study. The workflow involved acquiring CT and MRI scans of the knee joint, followed by segmentation of the bones and cartilage. The segmented data were processed to create realistic 3D models of the knee joint, which were then 3D printed using composite polymers designed to mimic cartilage properties. Compression tests were conducted on the printed models to assess their mechanical properties, and the results were compared with finite element analysis (FEA) simulations. The feasibility of using synthetic knee models derived from real patient data to represent the mechanical properties of knee cartilage was evaluated. The results of the FEA model were validated by comparing healthy and degenerate cartilage through empirical experiments using the 3D printed models. The study provides insights into the mechanical and functional properties of degenerative cartilage and demonstrates the potential of using synthetic 3D printed knee joint models for assessment and research purposes.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Objective Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. Design We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. Results Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. Conclusion The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.