The COVID-19 pandemic has resulted in increased psychological pressure on mental health since 2019. The resulting anxiety and stress have permeated every aspect of life during confinement.
OBJECTIVE:
To provide psychologists with an unbiased measure that can aid in the preliminary diagnosis of anxiety disorders and be used as an initial treatment in cognitive-behavioral therapy, this article introduces automated recognition of three levels of anxiety.
METHODS:
Anxiety was elicited by exposing participants to virtual environments inspired by social situations in reference to the Liebowitz social anxiety scale. Relevant parameters, such as heart rate variability and vasoconstriction were derived from the measurement of the blood volume pulse (BVP) signal.
RESULTS:
A long short-term memory architecture achieved an accuracy of approximately 98% on the training and test set.
CONCLUSION:
The generated model allowed for careful study of the state of seven phobic participants during virtual reality exposure (VRE).
AnjumA.HossainS.HasanM.T.UddinM.E. and SikderM.T., Anxiety among urban, semi-urban and rural school adolescents in Dhaka, Bangladesh: investigating prevalence and associated factors, PLoS ONE17(1) (2022), e0262716. doi:10.1371/journal.pone.0262716.
2.
SteinD.J., The cross-national epidemiology of social anxiety disorder: data from the World Mental Health Survey Initiative, BMC Med.15 (2017). doi:10.1186/s12916-017-0889-2.
3.
(Lamson) LinS., Generalized anxiety disorder during COVID-19 in Canada: gender-specific association of COVID-19 misinformation exposure, precarious employment, and health behavior change, J. Affect. Disord.302 (2022), 280–292. doi:10.1016/j.jad.2022.01.100.
4.
NajjukaS.M.CheckwechG.OlumR.AshabaS. and KaggwaM.M., Depression, anxiety, and stress among Ugandan university students during the COVID-19 lockdown: an online survey, Afr. Health Sci.21(4) (2021), Art. no. 4. doi:10.4314/ahs.v21i4.6.
5.
ShrivastavaS.R. and ShrivastavaP.S., COVID-19 and impairment of mental health: public health perspective, Afr. Health Sci.21(4) (2021), Art. no. 4. doi:10.4314/ahs.v21i4.5.
6.
SlaterM.SadagicA.UsohM. and SchroederR., Small-group behavior in a virtual and real environment: A comparative study, Presence: Teleoperators and Virtual Environments9(1) (2000), 37–51. doi:10.1162/105474600566600.
JamesL.K.LinC.-Y.SteedA.SwappD. and SlaterM., Social anxiety in virtual environments: results of a pilot study, Cyberpsychology Behav. Impact Internet Multimed. Virtual Real. Behav. Soc.6(3) (2003), 237–243. doi:10.1089/109493103322011515.
9.
HealeyJ.A. and PicardR.W., Detecting stress during real-world driving tasks using physiological sensors, IEEE Trans. Intell. Transp. Syst.6(2) (2005), 156–166.
10.
GiakoumisD.TzovarasD. and HassapisG., Subject-dependent biosignal features for increased accuracy in psychological stress detection, Int. J. Hum.-Comput. Stud.71(4) (2013), 425–439. doi:10.1016/j.ijhcs.2012.10.016.
11.
OsmanH.A.DongW.H. and El-SaddikA., Ubiquitous biofeedback serious game for stress management, IEEE Access4 (2016), 1274–1286. doi:10.1109/access.2016.2548980.
12.
SriramprakashS.PrasannaV.D. and MurthyO.V.R., Stress detection in working people, Proc. Comput. Sci.115 (2017), 359–366. doi:10.1016/j.procs.2017.09.090.
13.
GiannakakisG., Stress and anxiety detection using facial cues from videos, Biomed. Signal Process. Control31 (2017), 89–101.
14.
XiaL.MalikA.S. and SubhaniA.R., A physiological signal-based method for early mental-stress detection, Biomed. Signal Process. Control46 (2018), 18–32. doi:10.1016/j.bspc.2018.06.004.
15.
IhmigF.R.Gogeascoechea H.A.Neurohr-ParakeningsF.SchäferS.K.Lass-HennemannJ. and MichaelT., On-line anxiety level detection from biosignals: machine learning based on a randomized controlled trial with spider-fearful individuals, PLoS One15(6) (2020). doi:10.1371/journal.pone.0231517.
16.
HandouziW.MaaouiC.PruskiA. and MoussaouiA., Objective model assessment for short-term anxiety recognition from blood volume pulse signal, Biomed. Signal Process. Control14 (2014), 217–227, [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1746809414001141.
PettaP.PelachaudC. and CowieR., Emotion-Oriented Systems, Springer, Heidelberg Dordrecht London New York, 2011978-3-642-15183-5.
19.
PeperE.HarveyR.LinI.-M.TylovaH. and MossD., Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardiorespiratory synchrony?Biofeedback35 (2007), 54–61.
20.
AygunA.GhasemzadehH. and JafariR., Robust interbeat interval and heart rate variability estimation method from various morphological features using wearable sensors, IEEE J. Biomed. Health Inform.24(8) (2020), 2238–2250. doi:10.1109/JBHI.2019.2962627.
21.
AffanniA.BernardiniR.PirasA.RinaldoR. and ZontoneP., Driver’s stress detection using skin potential response signals, Measurement122 (2018), 264–274. doi:10.1016/j.measurement.2018.03.040.
