TY - JOUR

T1 - Individuals' Stress Assessment Using Human-Smartphone Interaction Analysis

AU - Ciman, Matteo

AU - Wac, Katarzyna

PY - 2018

Y1 - 2018

N2 - The increasing presence of stress in people' lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., 'tap', 'scroll', 'swipe' and 'text writing', and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.

AB - The increasing presence of stress in people' lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., 'tap', 'scroll', 'swipe' and 'text writing', and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.

KW - Human-smartphone interaction

KW - stress

KW - smartphone

KW - affective computing

KW - mobile sensing

KW - pervasive computing

UR - http://www.scopus.com/inward/record.url?scp=85038231491&partnerID=8YFLogxK

U2 - 10.1109/TAFFC.2016.2592504

DO - 10.1109/TAFFC.2016.2592504

M3 - Journal article

VL - 9

SP - 51

EP - 65

JO - IEEE Transactions on Affective Computing

JF - IEEE Transactions on Affective Computing

SN - 1949-3045

IS - 1

ER -