Peers know you: A feasibility study of the predictive value of peEr's observations to estimate human states
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Peers know you : A feasibility study of the predictive value of peEr's observations to estimate human states. / Berrocal, Allan; Wac, Katarzyna.
In: Procedia Computer Science, Vol. 175, 2020, p. 205-213.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Peers know you
T2 - 17th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2020
AU - Berrocal, Allan
AU - Wac, Katarzyna
N1 - Publisher Copyright: © 2020 The Authors.
PY - 2020
Y1 - 2020
N2 - This paper examines the predictive value of peer's observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor's data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant's smartphone, and peer-derived features (pDFs) from the peer's assessments. We evaluated the pDFs on a binary classification task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classification accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classification accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The findings are encouraging about the incorporation of peer's observations in machine learning with potential benefits in the fields of personal sensing and pervasive computing, especially for mental health and well-being.
AB - This paper examines the predictive value of peer's observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor's data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant's smartphone, and peer-derived features (pDFs) from the peer's assessments. We evaluated the pDFs on a binary classification task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classification accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classification accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The findings are encouraging about the incorporation of peer's observations in machine learning with potential benefits in the fields of personal sensing and pervasive computing, especially for mental health and well-being.
KW - Ecological momentary assessment
KW - Machine learning
KW - Peerceived momentary assessment
KW - PeerMA
KW - Well-being
UR - http://www.scopus.com/inward/record.url?scp=85094564733&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2020.07.031
DO - 10.1016/j.procs.2020.07.031
M3 - Conference article
AN - SCOPUS:85094564733
VL - 175
SP - 205
EP - 213
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
Y2 - 9 August 2020 through 12 August 2020
ER -
ID: 269515256