skip to main content
10.1145/3594738.3611360acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
short-paper
Open access

Challenges in Using Skin Conductance Responses for Assessments of Information Worker Productivity

Published: 08 October 2023 Publication History
  • Get Citation Alerts
  • Abstract

    Breaks as discretionary self-interruptions can have beneficial effects on information worker productivity and well-being. This has design implications for potential productivity tools that can assess opportune moments to suggest these breaks. Electrodermal Activity (EDA) is a good psychophysiological metric to capture changes in autonomic activity resulting from affective states that necessitate breaks. Wrist-worn sensing platforms have been heralded as effective means for EDA-based affective state assessments in real-life scenarios. However, our study finds no correlation even in a controlled setting with a constrained operational definition of productivity and well-researched EDA measurement and processing techniques. We reflect on our rationale against prior success reported in laboratory and ambulatory assessments of EDA.

    References

    [1]
    Ane Alberdi, Asier Aztiria, and Adrian Basarab. 2016. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of biomedical informatics 59 (2016), 49–75.
    [2]
    Ebrahim Babaei, Benjamin Tag, Tilman Dingler, and Eduardo Velloso. 2021. A Critique of Electrodermal Activity Practices at CHI. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 177, 14 pages. https://doi.org/10.1145/3411764.3445370
    [3]
    Anja Baethge and Thomas Rigotti. 2013. Interruptions to workflow: Their relationship with irritation and satisfaction with performance, and the mediating roles of time pressure and mental demands. Work & Stress 27, 1 (2013), 43–63.
    [4]
    Brian P Bailey and Shamsi T Iqbal. 2008. Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Transactions on Computer-Human Interaction (TOCHI) 14, 4 (2008), 1–28.
    [5]
    Brian P Bailey and Joseph A Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in human behavior 22, 4 (2006), 685–708.
    [6]
    Flora Beeftink, Wendelien Van Eerde, and Christel G Rutte. 2008. The effect of interruptions and breaks on insight and impasses: Do you need a break right now?Creativity Research Journal 20, 4 (2008), 358–364.
    [7]
    Mathias Benedek and Christian Kaernbach. 2010. Decomposition of skin conductance data by means of nonnegative deconvolution. psychophysiology 47, 4 (2010), 647–658.
    [8]
    Javad Birjandtalab, Diana Cogan, Maziyar Baran Pouyan, and Mehrdad Nourani. 2016. A Non-EEG Biosignals Dataset for Assessment and Visualization of Neurological Status. In 2016 IEEE International Workshop on Signal Processing Systems (SiPS). 110–114. https://doi.org/10.1109/SiPS.2016.27
    [9]
    Christine Bosch and Sabine Sonnentag. 2019. Should I take a break? A daily reconstruction study on predicting micro-breaks at work.International Journal of Stress Management 26, 4 (2019), 378.
    [10]
    Wolfram Boucsein. 2012. Electrodermal activity. Springer Science & Business Media.
    [11]
    Jason J Braithwaite, Derrick G Watson, Robert Jones, and Mickey Rowe. 2013. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology 49, 1 (2013), 1017–1034.
    [12]
    Donna L Coffman, Xizhen Cai, Runze Li, and Noelle R Leonard. 2020. Challenges and opportunities in collecting and modeling ambulatory electrodermal activity data. JMIR biomedical engineering 5, 1 (2020), e17106.
    [13]
    Mihaly Csikszentmihalyi, Mihaly Csikszentmihalyi, Sami Abuhamdeh, and Jeanne Nakamura. 2014. Flow. Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi (2014), 227–238.
    [14]
    Elena Di Lascio, Shkurta Gashi, and Silvia Santini. 2018. Unobtrusive assessment of students’ emotional engagement during lectures using electrodermal activity sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1–21.
    [15]
    Betsy DiSalvo, Dheeraj Bandaru, Qiaosi Wang, Hong Li, and Thomas Plötz. 2022. Reading the Room: Automated, Momentary Assessment of Student Engagement in the Classroom: Are We There Yet?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3 (2022), 1–26.
    [16]
    Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures, Wolfram Boucsein, Don C Fowles, Sverre Grimnes, Gershon Ben-Shakhar, Walton T Roth, Michael E Dawson, and Diane L Filion. 2012. Publication recommendations for electrodermal measurements. Psychophysiology 49, 8 (2012), 1017–1034.
    [17]
    Nan Gao, Wei Shao, Mohammad Saiedur Rahaman, and Flora D Salim. 2020. n-gage: Predicting in-class emotional, behavioural and cognitive engagement in the wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–26.
    [18]
    Shkurta Gashi, Elena Di Lascio, Bianca Stancu, Vedant Das Swain, Varun Mishra, Martin Gjoreski, and Silvia Santini. 2020. Detection of artifacts in ambulatory electrodermal activity data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 2 (2020), 1–31.
    [19]
    Alberto Greco, Gaetano Valenza, Antonio Lanata, Enzo Pasquale Scilingo, and Luca Citi. 2015. cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering 63, 4 (2015), 797–804.
    [20]
    Nils Y Hammerla, Reuben Kirkham, Peter Andras, and Thomas Ploetz. 2013. On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution. In Proceedings of the 2013 international symposium on wearable computers. 65–68.
    [21]
    Javier Hernandez, Ivan Riobo, Agata Rozga, Gregory D Abowd, and Rosalind W Picard. 2014. Using electrodermal activity to recognize ease of engagement in children during social interactions. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. 307–317.
    [22]
    Francisco Hernando-Gallego, David Luengo, and Antonio Artés-Rodríguez. 2017. Feature extraction of galvanic skin responses by nonnegative sparse deconvolution. IEEE journal of biomedical and health informatics 22, 5 (2017), 1385–1394.
    [23]
    Anne Horvers, Natasha Tombeng, Tibor Bosse, Ard W Lazonder, and Inge Molenaar. 2021. Detecting emotions through electrodermal activity in learning contexts: A systematic review. Sensors 21, 23 (2021), 7869.
    [24]
    BIOPAC Systems Inc. [n. d.]. EDA Introductory Guide. Retrieved May 25, 2023 from https://www.biopac.com/wp-content/uploads/EDA-Guide.pdf
    [25]
    Shamsi T. Iqbal, Xianjun Sam Zheng, and Brian P. Bailey. 2004. Task-Evoked Pupillary Response to Mental Workload in Human-Computer Interaction. In CHI ’04 Extended Abstracts on Human Factors in Computing Systems (Vienna, Austria) (CHI EA ’04). Association for Computing Machinery, New York, NY, USA, 1477–1480. https://doi.org/10.1145/985921.986094
    [26]
    Quintus R Jett and Jennifer M George. 2003. Work interrupted: A closer look at the role of interruptions in organizational life. Academy of management Review 28, 3 (2003), 494–507.
    [27]
    Jing Jin and Laura A Dabbish. 2009. Self-interruption on the computer: a typology of discretionary task interleaving. In Proceedings of the SIGCHI conference on human factors in computing systems. 1799–1808.
    [28]
    Harmanpreet Kaur, Alex C Williams, Daniel McDuff, Mary Czerwinski, Jaime Teevan, and Shamsi T Iqbal. 2020. Optimizing for happiness and productivity: Modeling opportune moments for transitions and breaks at work. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–15.
    [29]
    Malia Kelsey, Murat Akcakaya, Ian R Kleckner, Richard Vincent Palumbo, Lisa Feldman Barrett, Karen S Quigley, and Matthew S Goodwin. 2018. Applications of sparse recovery and dictionary learning to enhance analysis of ambulatory electrodermal activity data. Biomedical Signal Processing and Control 40 (2018), 58–70.
    [30]
    Kyung Hwan Kim, Seok Won Bang, and Sang Ryong Kim. 2004. Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing 42 (2004), 419–427.
    [31]
    Ian R Kleckner, Rebecca M Jones, Oliver Wilder-Smith, Jolie B Wormwood, Murat Akcakaya, Karen S Quigley, Catherine Lord, and Matthew S Goodwin. 2017. Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data. IEEE Transactions on Biomedical Engineering 65, 7 (2017), 1460–1467.
    [32]
    Matthew Lee. 2020. Detecting affective flow states of knowledge workers using physiological sensors. arXiv preprint arXiv:2006.10635 (2020).
    [33]
    Mindware Technologies Ltd.2023. "Mindware EDA". https://support.mindwaretech.com/training/guides/skin-conductance-eda-training-guide/#
    [34]
    Dominique Makowski, Tam Pham, Zen J Lau, Jan C Brammer, François Lespinasse, Hung Pham, Christopher Schölzel, and SH Annabel Chen. 2021. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior research methods (2021), 1–8.
    [35]
    Gloria Mark, Shamsi T Iqbal, Mary Czerwinski, and Paul Johns. 2014. Bored mondays and focused afternoons: the rhythm of attention and online activity in the workplace. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 3025–3034.
    [36]
    Luca Menghini, Evelyn Gianfranchi, Nicola Cellini, Elisabetta Patron, Mariaelena Tagliabue, and Michela Sarlo. 2019. Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions. Psychophysiology 56, 11 (2019), e13441.
    [37]
    Nir Milstein and Ilanit Gordon. 2020. Validating measures of electrodermal activity and heart rate variability derived from the empatica E4 utilized in research settings that involve interactive dyadic states. Frontiers in Behavioral Neuroscience 14 (2020), 148.
    [38]
    Emily Claire Mitaro. 2019. Synchrony of Electrodermal Activity in a Socioeconomically and Racially Diverse Sample of Mothers and Preschoolers. Ph. D. Dissertation. University of Georgia.
    [39]
    Mohsen Nabian, Yu Yin, Jolie Wormwood, Karen S. Quigley, Lisa F. Barrett, and Sarah Ostadabbas. 2018. An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE Journal of Translational Engineering in Health and Medicine 6 (2018), 1–11. https://doi.org/10.1109/JTEHM.2018.2878000
    [40]
    Andrew F. H. Payne, Anne M. Schell, and Michael E. Dawson. 2016. Lapses in skin conductance responding across anatomical sites: Comparison of fingers, feet, forehead, and wrist. Psychophysiology 53, 7 (2016), 1084–1092. https://doi.org/10.1111/psyp.12643 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/psyp.12643
    [41]
    Thomas Ploetz, Nils Hammerla, and Patrick Olivier. 2011. Feature Learning for Activity Recognition in Ubiquitous Computing.1729–1734. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-290
    [42]
    Thomas PlÖtz. 2021. Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them. ACM Computing Surveys (CSUR) 54, 6 (2021), 1–25.
    [43]
    Ming-Zher Poh, Nicholas C Swenson, and Rosalind W Picard. 2010. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE transactions on Biomedical engineering 57, 5 (2010), 1243–1252.
    [44]
    Hugo F Posada-Quintero, John P Florian, Alvaro D Orjuela-Cañón, Tomas Aljama-Corrales, Sonia Charleston-Villalobos, and Ki H Chon. 2016. Power spectral density analysis of electrodermal activity for sympathetic function assessment. Annals of biomedical engineering 44 (2016), 3124–3135.
    [45]
    Hugo F Posada-Quintero, John P Florian, Alvaro D Orjuela-Cañón, and Ki H Chon. 2018. Electrodermal activity is sensitive to cognitive stress under water. Frontiers in physiology 8 (2018), 1128.
    [46]
    Izabela Rejer and Jarosław Jankowski. 2016. EEG patterns analysis in the process of recovery from interruptions. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Springer, 587–596.
    [47]
    Thought Technologies. 2023. "Flexcomp Infiniti". https://thoughttechnology.com/procomp5-infiniti-system-w-biograph-infiniti-software-t7525/
    [48]
    Yihan Wu and Wilma Koutstaal. 2020. Charting the contributions of cognitive flexibility to creativity: Self-guided transitions as a process-based index of creativity-related adaptivity. PloS one 15, 6 (2020), e0234473.
    [49]
    Lili Zhu, Petros Spachos, Pai Chet Ng, Yuanhao Yu, Yang Wang, Konstantinos Plataniotis, and Dimitrios Hatzinakos. 2023. Stress Detection through Wrist-based Electrodermal Activity Monitoring and Machine Learning. IEEE Journal of Biomedical and Health Informatics (2023).

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ISWC '23: Proceedings of the 2023 ACM International Symposium on Wearable Computers
    October 2023
    145 pages
    ISBN:9798400701993
    DOI:10.1145/3594738
    This work is licensed under a Creative Commons Attribution International 4.0 License.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 October 2023

    Check for updates

    Author Tags

    1. cognitive load
    2. electrodermal activity
    3. information work
    4. interruptions
    5. productivity
    6. wearable EDA sensors

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Conference

    UbiComp/ISWC '23

    Acceptance Rates

    Overall Acceptance Rate 38 of 196 submissions, 19%

    Upcoming Conference

    UBICOMP '24

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 381
      Total Downloads
    • Downloads (Last 12 months)381
    • Downloads (Last 6 weeks)28
    Reflects downloads up to 14 Aug 2024

    Other Metrics

    Citations

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media