STDD: Short-Term Depression Detection with Passive Sensing

Sensors
Nematjon NarzievYoungtae Noh

Abstract

It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices' sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone a...Continue Reading

References

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Citations

Jul 3, 2021·International Journal of Environmental Research and Public Health·Xin Chen, Zhigeng Pan
Jul 30, 2021·Critical Reviews in Food Science and Nutrition·Larissa Oliveira ChavesJosefina Bressan
Oct 27, 2021·JMIR MHealth and UHealth·Lucy MacLeodSandra Meier

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Methods Mentioned

BETA
feature extraction

Software Mentioned

FINE
STDD
iHOPE
WEKA
PostgreSQL
Android smartphone
Mobilyze !
Visual Studio IEDs
KaKaoTalk
Mobilyze

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