Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety

Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety

This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE sensors journal 2016-05, Vol.16 (10), p.3689-3696
Hauptverfasser: Yali Zheng, Wong, Tracy C. H., Leung, Billy H. K., Poon, Carmen C. Y.
Format: Artikel
Sprache:eng
Schlagworte:
Anxiety
Biomedical monitoring
Body sensor network
Electroencephalogram
Electroencephalography
Feature extraction
Frequency measurement
Photoplethysmogram
Quantified self
Sensors
Sports performance
Stress
Training
Unobtrusive sensing
Wearable sensors
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level (r = -0.49 and -0.58, p
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2016.2539383