Individuals' Stress Assessment Using Human-Smartphone Interaction Analysis
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Individuals' Stress Assessment Using Human-Smartphone Interaction Analysis. / Ciman, Matteo; Wac, Katarzyna.
In: IEEE Transactions on Affective Computing, Vol. 9, No. 1, 2018, p. 51-65.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 199034767