Heart Rate Variability During a Complex Postural Control Task with the BioVRSea Paradigm DOI
Marco Recenti, Lorena Guerrini,

Alessia Lindemann

и другие.

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Год журнала: 2023, Номер unknown, С. 876 - 881

Опубликована: Окт. 25, 2023

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

Язык: Английский

Assessing Brain Network Dynamics during Postural Control Task using EEG Microstates DOI Creative Commons

Carmine Gelormini,

Lorena Guerrini,

Federica Pescaglia

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 27, 2024

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

Язык: Английский

Процитировано

0
Heart Rate Variability During a Complex Postural Control Task with the BioVRSea Paradigm DOI
Marco Recenti, Lorena Guerrini,

Alessia Lindemann

и другие.

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Год журнала: 2023, Номер unknown, С. 876 - 881

Опубликована: Окт. 25, 2023

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

Язык: Английский

Процитировано

0