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 -