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This paper reports on an in-depth study of electrocardiogram (ECG) biometrics in everyday life. We collected ECG data from 20 people over a week, using a non-medical chest tracker. We evaluated user identification accuracy in several scenarios and observed equal error rates of 9.15% to 21.91%, heavily depending on 1) the number of days used for training, and 2) the number of heartbeats used per identification decision. We conclude that ECG biometrics can work in the wild but are less robust than expected based on the literature, highlighting that previous lab studies obtained highly optimistic results with regard to real life deployments. We explain this with noise due to changing body postures and states as well as interrupted measures. We conclude with implications for future research and the design of ECG biometrics systems for real world deployments, including critical reflections on privacy.
Automatically detecting attentional states is a prerequisite for designing interventions to manage attention — knowledge workers' most critical resource. As a first step towards this goal, it is necessary to understand how different attentional states are made discernible through visible cues in knowledge workers. In this paper, we demonstrate the important facial cues to detect attentional states by evaluating a data set of 15 participants that we tracked over a whole workday, which included their challenge and engagement levels. Our evaluation shows that gaze, pitch, and lips part action units are indicators of engaged work; while pitch, gaze movements, gaze angle, and upper-lid raiser action units are indicators of challenging work. These findings reveal a significant relationship between facial cues and both engagement and challenge levels experienced by our tracked participants. Our work contributes to the design of future studies to detect attentional states based on facial cues.
Thanks to advanced sensing and logging technology, automatic personality assessment (APA) with users' behavioral data in the workplace is on the rise. While previous work has focused on building APA systems with high accuracy, little research has attempted to understand users' perception towards APA systems. To fill this gap, we take a mixed-methods approach: we (1) designed a survey (n=89) to understand users'social workplace behavior both online and offline and their privacy concerns; (2) built a research probe that detects personality from online and offline data streams with up to 81.3% accuracy, and deployed it for three weeks in Korea (n=32); and (3) conducted post-interviews (n=9). We identify privacy issues in sharing data and system-induced change in natural behavior as important design factors for APA systems. Our findings suggest that designers should consider the complex relationship between users' perception and system accuracy for a more user-centered APA design.
Emotion recognition algorithms recognize, infer, and harvest emotions using data sources such as social media behavior, streaming service use, voice, facial expressions, and biometrics in ways often opaque to the people providing these data. People's attitudes towards emotion recognition and the harms and outcomes they associate with it are important yet unknown. Focusing on social media, we interviewed 13 adult U.S. social media users to fill this gap. We find that people view emotions as insights to behavior, prone to manipulation, intimate, vulnerable, and complex. Many find emotion recognition invasive and scary, associating it with autonomy and control loss. We identify two categories of emotion recognition's risks: individual and societal. We discuss findings' implications for algorithmic accountability and argue for considering emotion data as sensitive. Using a Science and Technology Studies lens, we advocate that technology users should be considered as a relevant social group in emotion recognition advancements.
Working in an environment with constant interruptions is known to affect stress, but how do interruptions affect emotional expression? Emotional expression can have significant impact on interactions among coworkers. We analyzed the video of 26 participants who performed an essay task in a laboratory while receiving either continual email interruptions or receiving a single batch of email. Facial videos of the participants were run through a convolutional neural network to determine the emotional mix via decoding of facial expressions. Using a novel co-occurrence matrix analysis, we showed that with batched email, a neutral emotional state is dominant with sadness being a distant second, and with continual interruptions, this pattern is reversed, and sadness is mixed with fear. We discuss the implications of these results for how interruptions can impact employees' well-being and organizational climate.