TY - GEN
T1 - Using Virtual Reality to Examine the Correlation between Balance Function and Anxiety in Stance
AU - Sun, Rongyi
AU - Kaur, Rachneet
AU - Ziegelman, Liran
AU - Yang, Shuo
AU - Sowers, Richard
AU - Hernandez, Manuel E.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - We study the interaction between balance function and anxiety in humans via a virtual reality based experimental setup, fundamentally designed to examine postural control and neurological feedback during balance-demanding height and perturbation shifts, while in quiet stance. In this work, sensory organization balance test (SOT) was utilized to quantify the balance function in subjects, and the salient features of the recorded electrocardiogram (EKG), electromyogram (EMG) and electroencephalography (EEG) signals described the subject's physiological anxiety-related responses. Also, patient-reported outcomes measurement information system (PROMIS) trait anxiety questionnaire was adopted to indicate the self-assessed trait anxiety scores by the subjects. Our statistical analysis indicates that subjects with higher SOT scores encounter a lower heart rate variability during baseline recording relative to the experimental trials and lower co-contraction index of the ankle dorsiflexion (during the experimental trials), suggesting a lower anxiety state. A similar trend was further observed via the self-reported PROMIS questionnaire responses, estimating anxiety experienced in daily life. Additionally, we utilize machine learning to describe the predictability of balance function states via the relative \alpha-band power from the frontal channels of the recorded EEG signals as descriptive features characterizing anxiety. The discussed trends analytically demonstrate the potential for adopting balance function to anticipate anxiety, especially in stressful environments involving postural threats.
AB - We study the interaction between balance function and anxiety in humans via a virtual reality based experimental setup, fundamentally designed to examine postural control and neurological feedback during balance-demanding height and perturbation shifts, while in quiet stance. In this work, sensory organization balance test (SOT) was utilized to quantify the balance function in subjects, and the salient features of the recorded electrocardiogram (EKG), electromyogram (EMG) and electroencephalography (EEG) signals described the subject's physiological anxiety-related responses. Also, patient-reported outcomes measurement information system (PROMIS) trait anxiety questionnaire was adopted to indicate the self-assessed trait anxiety scores by the subjects. Our statistical analysis indicates that subjects with higher SOT scores encounter a lower heart rate variability during baseline recording relative to the experimental trials and lower co-contraction index of the ankle dorsiflexion (during the experimental trials), suggesting a lower anxiety state. A similar trend was further observed via the self-reported PROMIS questionnaire responses, estimating anxiety experienced in daily life. Additionally, we utilize machine learning to describe the predictability of balance function states via the relative \alpha-band power from the frontal channels of the recorded EEG signals as descriptive features characterizing anxiety. The discussed trends analytically demonstrate the potential for adopting balance function to anticipate anxiety, especially in stressful environments involving postural threats.
KW - Balance function
KW - Co-contraction index
KW - Electroencephalogram
KW - Heart rate variability
KW - Machine learning
KW - Virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85084336675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084336675&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8983331
DO - 10.1109/BIBM47256.2019.8983331
M3 - Conference contribution
AN - SCOPUS:85084336675
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 1633
EP - 1640
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -