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.
Heart Rate Variability During a Complex Postural Control Task with the BioVRSea Paradigm / Recenti, Marco; Guerrini, Lorena; Lindemann, Alessia; Pierucci, Simona; Ricciardi, Carlo; Ponsiglione, Alfonso Maria; Petersen, Hannes; Gargiulo, Paolo. - (2023), pp. 876-881. ( 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 ita 2023) [10.1109/metroxraine58569.2023.10405826].
Heart Rate Variability During a Complex Postural Control Task with the BioVRSea Paradigm
Ricciardi, Carlo;Ponsiglione, Alfonso Maria;
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
