{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T12:39:37Z","timestamp":1775047177862,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Depression is one of the most common mental health disorders, affecting approximately 280 million people worldwide. This condition is defined as emotional dysregulation resulting in persistent feelings of sadness, loss of interest and inability to experience pleasure. Early detection can facilitate timely intervention in the form of psychological therapy and\/or medication. With the widespread public adoption of wearable devices such as smartwatches and fitness trackers, it is becoming increasingly possible to gain insights relating the mental states of individuals in an unobtrusive manner within free-living conditions. This work presents a machine learning (ML) approach that utilizes retrospectively collected data-derived consumer-grade wearables for passive detection of depression severity. The experiments conducted in this work reveal that multimodal analysis of physiological signals in terms of their discrete wavelet transform (DWT) features exhibit considerably better performance than unimodal scenarios. Additionally, we conduct experiments to view the impact of severity on emotional valence-arousal detection. We believe that our work has implications towards guiding development in the domain of multimodal wearable-based screening of mental health disorders and necessitates appropriate treatment interventions.<\/jats:p>","DOI":"10.3390\/info13090406","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T03:34:56Z","timestamp":1661744096000},"page":"406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-4242","authenticated-orcid":false,"given":"Abdullah","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7093-5149","authenticated-orcid":false,"given":"Jayroop","family":"Ramesh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates"}]},{"given":"Sandipan","family":"Ganguly","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University College London, London WC1TE 6BT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2695-5836","authenticated-orcid":false,"given":"Raafat","family":"Aburukba","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates"}]},{"given":"Assim","family":"Sagahyroon","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5129-7789","authenticated-orcid":false,"given":"Fadi","family":"Aloul","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1001\/jamapsychiatry.2020.1107","article-title":"Findings from world mental health surveys of the perceived helpfulness of treatment for patients with major depressive disorder","volume":"77","author":"Harris","year":"2020","journal-title":"JAMA Psychiatry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/S0140-6736(22)00187-8","article-title":"Depression and anxiety during COVID-19","volume":"399","author":"Daly","year":"2022","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100399","DOI":"10.1016\/j.lanepe.2022.100399","article-title":"Machine Learning Assessment of Risk Factors for Depression in Later Adulthood","volume":"18","author":"Zhang","year":"2022","journal-title":"Lancet Reg. 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