Mohammed T. Masud|Mohammed A. Mamun|Md Shafiqul Islam|D.H. Lee|Mark D. Griffiths|Sung?Hyun Yang
Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone
Kwangwoon University|Clinical Research Organization|Clinical Research Organization|Jahangirnagar University|Kwangwoon University|Kwangwoon University|Nottingham Trent University|Kwangwoon University
number individuals mental disorders increasing commonly found among individuals avoid social interaction like live alone amongst mental health disorders depression common serious present paper introduces method assess depression level individual using smartphone monitoring daily activities time domain characteristics smartphone acceleration sensor used alongside vector machine algorithm classify physical activities additionally geographical location information clustered using smartphone gps sensor simplify movement patterns total 12 features extracted individuals physical activity movement patterns analyzed alongside weekly depression scores using nineitem patient health questionnaire using wrapper feature selection method subset features selected applied linear regression model estimate depression score support vector machine algorithm used classify depression severity level among individuals absence moderate severe accuracy 872 severe depression cases outperformed classification models including knearest neighbor artificial neural network method identifying depression costeffective solution longterm use monitor individuals depression without invading personal space creating daytoday disturbances