Abstract
In today’s growing world, we often overlook the need of paying heed to the health of some of the most important constituents of our body viz., mental health. This might be due to working hours, which do not allow a person to pay due attention that this facility desires, this in turn may lead to a lot of disorders such as bipolar disorder, ADHD, PTSD and anxiety disorder remaining un-diagnosed which may cause further health complications. Hence, it is the need of the hour to make available a solution so as to enable the people to get themselves diagnosed within a short span of time at their own convenience and from the comfort of their homes. KnowStress has been developed with the aim of improving access to mental health resources for those who lack access to conventional support and to help people explore how technology can be used to improve general wellbeing. KnowStress provides diagnostic tests for screening stress and stress-related disorders by using well known tests and tools that are used by medical professionals. Our proposed model utilises the K-means clustering algorithm to categorise an individual’s responses into the groups of “low stress”, “bipolar disorder”, “PTSD” and “ADHD / GAD” with a validation accuracy of 88.86%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kessler RC et al (2004) The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population
Spitzer RL, Kroenke K, Williams JBW et al (2006) A brief measure for assessing generalized anxiety disorder. The GAD-7
McIntyre RS, Patel MD, Masand PS, Harrington A, Gillard P, McElroy SL, Sullivan K et al (2021) The Rapid Mood Screener (RMS): a novel and pragmatic screener for bipolar I disorder. Current Med Res Opinion 37(1)
Prins A et al (2016) The primary care PTSD screen for DSM-5 (PC-PTSD-5): development and evaluation within a veteran primary care sample. J General Internal Med 31(10)
Fliege H, Rose M, Arck P, Walter O, Kocalevent R-D, Weber C, Klapp B (2005) The Perceived Stress Questionnaire (PSQ) reconsidered: validation and reference values from different clinical and healthy adult samples
Li R, Liu Z (2020) Stress detection using deep neural networks. BMC Med Inform Decis Mak 20:285
Albertetti F, Simalastar A, Rizzotti-Kaddouri A (2020) Stress detection with deep learning approaches using physiological signals. In: International Conference on IoT Technologies for HealthCare, pp 95–111. Springer, Cham
Kumar P, Garg S, Garg A (2020) Assessment of anxiety, depression and stress using machine learning models. Procedia Comp Sci 171
Baheti RR, Kinariwala SA (2019) Detection and analysis of stress using machine learning techniques. Int J Eng Adv Tech (IJEAT). 9(1). ISSN: 2249-8958
Baheti RR, Kinariwala SA (2019) Survey: sentiment stress identification using tensi/strength framework. Int J Sci Res Eng Dev 2(3)
Williams JE, Pykett J (2022) Mental health monitoring apps for depression and anxiety in children and young people: a scoping review and critical ecological analysis. Soc Sci Med 297:114802
Brevik EJ, Lundervold AJ, Haavik J, Posserud M-B (2020) Validity and accuracy of the ADHD Self-Report Scale (ASRS)
Pranckeviciene A, Saudargiene A, Gecaite-Stonciene J, Liaugaudaite V, Griskova-Bulanova I, Simkute D, Naginiene R, Dainauskas LL, Ceidaite G, Burkauskas J (2022) Validation of the patient health questionnaire-9 and the generalized anxiety disorder-7 in Lithuanian student sample. PLoS One
Sayyah M, Delirrooyfard A, Rahim F (2022) Assessment of the diagnostic performance of two new tools versus routine screening instruments for bipolar disorder: a meta-analysis. Brazilian J Psychiatry 44
Williamson MLC et al (2022) Diagnostic accuracy of the primary care PTSD screen for DSM-5 (PC-PTSD-5) within a civilian primary care sample. J Clin Psych. https://doi.org/10.1002/jclp.23405
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Navlakha, M., Mankodi, N., Bari, P. (2023). KnowStress: A Mobile Application Prototype Detection of Stress and Stress Related Disorders. In: Singh, Y., Verma, C., Zoltán, I., Chhabra, J.K., Singh, P.K. (eds) Proceedings of International Conference on Recent Innovations in Computing. ICRIC 2022. Lecture Notes in Electrical Engineering, vol 1011. Springer, Singapore. https://doi.org/10.1007/978-981-99-0601-7_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-0601-7_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0600-0
Online ISBN: 978-981-99-0601-7
eBook Packages: Computer ScienceComputer Science (R0)