Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning
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...
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MDPI AG
2022-08-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/9/406 |
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| author | Abdullah Ahmed Jayroop Ramesh Sandipan Ganguly Raafat Aburukba Assim Sagahyroon Fadi Aloul |
| author_facet | Abdullah Ahmed Jayroop Ramesh Sandipan Ganguly Raafat Aburukba Assim Sagahyroon Fadi Aloul |
| author_sort | Abdullah Ahmed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:41:00Z |
| format | Article |
| id | doaj.art-83bbc3e456f444569ec6c4e18d922a96 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T23:41:00Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj.art-83bbc3e456f444569ec6c4e18d922a962023-11-23T16:53:02ZengMDPI AGInformation2078-24892022-08-0113940610.3390/info13090406Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine LearningAbdullah Ahmed0Jayroop Ramesh1Sandipan Ganguly2Raafat Aburukba3Assim Sagahyroon4Fadi Aloul5Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USADepartment of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Computer Science, University College London, London WC1TE 6BT, UKDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepression 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.https://www.mdpi.com/2078-2489/13/9/406affectivedepression screeningdigital phenotypeemotionmachine learningpassive sensing |
| spellingShingle | Abdullah Ahmed Jayroop Ramesh Sandipan Ganguly Raafat Aburukba Assim Sagahyroon Fadi Aloul Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning Information affective depression screening digital phenotype emotion machine learning passive sensing |
| title | Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning |
| title_full | Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning |
| title_fullStr | Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning |
| title_full_unstemmed | Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning |
| title_short | Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning |
| title_sort | investigating the feasibility of assessing depression severity and valence arousal with wearable sensors using discrete wavelet transforms and machine learning |
| topic | affective depression screening digital phenotype emotion machine learning passive sensing |
| url | https://www.mdpi.com/2078-2489/13/9/406 |
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