Stress Modelling Using Transfer Learning in Presence of Scarce Data

Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. However, if the data for a subject is scarce, a good model cannot be obtained. We propose an approach based on transfer learning for building a model of a subject with scarce data. It is based on the comparison of decision trees to select the closest subject for knowledge transfer. We present an study carried out on 30 employees within two organisations. The results show that the in the case of identifying a “similar” subject, the classification accuracy is improved via transfer learning.

[1]  C. Robusto.  The Cosine-Haversine Formula , 1957 .

[2]  F. Harris.  On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[3]  Dieter Huber,et al.  Pitch period determination of aperiodic speech signals , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[6]  P. Näätänen,et al.  Bergen Burnout Indicator-15 , 2003 .

[7]  Karen Kay-Lynn Liu,et al.  A personal, mobile system for understanding stress and interruptions , 2004 .

[8]  Gabriele Soffritti,et al.  The comparison between classification trees through proximity measures , 2004, Comput. Stat. Data Anal..

[9]  Derya Birant,et al.  ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..

[10]  E. Demerouti,et al.  Measurement of Burnout ( and Engagement ) 1 Running head : MEASUREMENT OF BURNOUT AND ENGAGEMENT The Oldenburg Burnout Inventory : A Good Alternative to Measure Burnout ( and Engagement ) , 2007 .

[11]  J. Halbesleben.  Handbook of stress and burnout in health care , 2008 .

[12]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[13]  Mykola Pechenizkiy,et al.  What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[14]  Daniel Gatica-Perez,et al.  StressSense: detecting stress in unconstrained acoustic environments using smartphones , 2012, UbiComp.

[15]  Paul Lukowicz,et al.  Can smartphones detect stress-related changes in the behaviour of individuals? , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[16]  N. Lane,et al.  MoodScope: building a mood sensor from smartphone usage patterns , 2013, MobiSys '13.

[17]  Natalia Sidorova,et al.  Smart technologies for long-term stress monitoring at work , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[18]  Alex Pentland,et al.  Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits , 2014, ACM Multimedia.

[19]  Mikhail Belkin,et al.  Semi-Supervised Learning , 2021, Machine Learning.

[20]  Oscar Mayora-Ibarra,et al.  Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients , 2015, IEEE Journal of Biomedical and Health Informatics.

[21]  Venet Osmani,et al.  Smartphones in Mental Health: Detecting Depressive and Manic Episodes , 2015, IEEE Pervasive Computing.

[22]  Oscar Mayora-Ibarra,et al.  Smartphone app usage as a predictor of perceived stress levels at workplace , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[23]  Oscar Mayora-Ibarra,et al.  Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step , 2015, IEEE Journal of Biomedical and Health Informatics.

[24]  Xiaojin Zhu,et al.  Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.