Imperial College London
Alternate

DrDavidBoyle

Faculty of Engineering, Dyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

david.boyle Website

 
 
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Location

 

1M04ARoyal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shaukat:2020:10.2196/preprints.23383,
author = {Shaukat, Jali R and Van, Zalk N and Boyle, D},
doi = {10.2196/preprints.23383},
journal = {JMIR Preprints},
title = {Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study (Preprint)},
url = {http://dx.doi.org/10.2196/preprints.23383},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <sec>                    <title>BACKGROUND</title>                        <p>Subclinical (i.e., threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment that would be greatly beneficial for sufferers, society and healthcare services. Nevertheless, indicators such as skin temperature from wrist-worn sensors have not been used in prior work on physiological social anxiety detection.</p>                </sec>                                <sec>                    <title>OBJECTIVE</title>                        <p>This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including Heart Rate (HR), Skin Temperature (ST) and Electrodermal Activity (EDA).</p>                </sec>                                <sec>                    <title>METHODS</title>                        <p>Young adults (N = 12) with self-reported subclinical social anxiety (measured by the widely used self-reported version of the Liebowitz Social Anxiety Scale, LSAS-SR) participated in an impromptu speech task. Physiological data was collected using an E4 Empatica wearable device. Using the pre-processed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest Neighbours (KNN) were used to develop models for three different contexts. Models were trained to (1) classify between baseline and socially anxious states, (2) differentiate between baseline, anticipation anxiety and reactive anxiety states, and (3) classify between social anxiety experienced by individuals with diffe
AU  - Shaukat,Jali R
AU  - Van,Zalk N
AU  - Boyle,D
DO  - 10.2196/preprints.23383
PY  - 2020///
TI  - Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study (Preprint)
T2  - JMIR Preprints
UR  - http://dx.doi.org/10.2196/preprints.23383
ER  -
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