A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms.

Content Provider	Europe PMC
Author	Thati, Ravi Prasad Dhadwal, Abhishek Singh Kumar, Praveen P, Sainaba
Abstract	Depression has become a global concern, and COVID-19 also has caused a big surge in its incidence. Broadly, there are two primary methods of detecting depression: Task-based and Mobile Crowd Sensing (MCS) based methods. These two approaches, when integrated, can complement each other. This paper proposes a novel approach for depression detection that combines real-time MCS and task-based mechanisms. We aim to design an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. For this purpose, we created a real-world dataset of depressed and non-depressed subjects. We experimented with: various features from multi-modalities, feature selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), etc. for classification. Our findings suggest that combining features from multiple modalities perform better than any single data modality, and the best classification accuracy is achieved when features from all three data modalities are fused. Feature selection method based on Pearson’s correlation coefficients improved the accuracy in comparison with other methods. Also, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on benchmarking dataset, and results demonstrated that the multimodal approach is advantageous in performance with state-of-the-art depression recognition techniques.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9000000&blobtype=pdf
ISSN	13807501
Journal	Multimedia Tools and Applications [Multimed Tools Appl]
Volume Number	82
DOI	10.1007/s11042-022-12315-2
PubMed Central reference number	PMC9000000
Issue Number	4
PubMed reference number	35431608
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2022-04-11
Publisher Place	New York
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Subject Keyword	Depression detection Multi-modal Mobile crowd sensing Emotion elicitation Speech elicitation Machine learning
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology