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Affect Estimation with Wearable Sensors

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Abstract

Affective states are associated with people’s mental health status and have profound impact on daily life, thus unobtrusively understanding and estimating affects have been brought to the public attention. The pervasiveness of wearable sensors makes it possible to build automatic systems for affect tracking. However, constructing such systems is a challenging task due to the complexity of human behaviors. In this work, we focus on the problem of estimating daily self-reported affects from sensor-generated data. We first analyze the intra- and inter-subject differences of self-reported affect labels. Second, we explore different machine learning models as well as label transformation techniques to overcome the individual differences in self-reported responses estimation. We conceptualize three experimental settings including long-term and short-term estimation scenarios. Our experimental results show that the mixed effects model and label transformation can yield better estimation of individual daily affect. This work poses the basis for future sensor-based individualized and real-time affective digital and/or clinical interventions.

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Notes

  1. OMsignal: https://omsignal.com/technology/

  2. Fitbit Charge 2: https://www.fitbit.com/charge2

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Acknowledgments

The authors are grateful to the TILES team for the efforts in study design, data collection, and sharing, that enable this work.

Funding

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA Contract No 2017-17042800005.

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Authors and Affiliations

  1. University of Southern California, Information Sciences Institute, Los Angeles, CA, 90089, USA

    Shen Yan, Homa Hosseinmardi, Hsien-Te Kao, Shrikanth Narayanan, Kristina Lerman & Emilio Ferrara

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  1. Shen Yan
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  2. Homa Hosseinmardi
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Correspondence to Shen Yan.

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Appendix

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Table 10 Extracted features
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Yan, S., Hosseinmardi, H., Kao, HT. et al. Affect Estimation with Wearable Sensors. J Healthc Inform Res 4, 261–294 (2020). https://doi.org/10.1007/s41666-019-00066-z

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  • DOI: https://doi.org/10.1007/s41666-019-00066-z

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