Advancing the Understanding and Measurement of Workplace Stress in Remote Information Workers from Passive Sensors and Behavioral Data

Workplace stress has been increasing in recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical expenses. An essential step toward managing stress involves finding comfortable ways to sense workers and recognizing stress as soon as it happens. This work explores the potential value of using pervasive sensors such as keyboards, webcams, and behavioral data such as calendar and e-mail activity to passively assess individual stress levels of work in real-life. In particular, we collected a large corpus of such data from 46 remote information workers over one month and asked them to self-report their stress levels and other relevant factors several times a day. Analysis of the data demonstrates that passive sensors can effectively detect both triggers and manifestations of workplace stress and that having access to prior data of the worker is critical for developing well-performing stress recognition models. Furthermore, we provide qualitative feedback capturing workers' preferences in workplace stress monitoring.

Federated Remote Physiological Measurement with Imperfect Data

The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic. In health-related machine learning applications the ability to learn predictive models without data leaving a private device is attractive, especially when these data might contain features (e.g., photographs or videos of the body) that make identifying a subject trivial and/or the training data volume is large (e.g., uncompressed video). Camera-based remote physiological sensing facilitates scalable and low-cost measurement, but is a prime example of a task that involves analysing high bit-rate videos containing identifiable images and sensitive health information. Federated learning enables privacy-preserving decentralized training which has several properties beneficial for camera-based sensing. We develop the first mobile federated learning camera-based sensing system and show that it can perform competitively with traditional state-of-the-art supervised approaches. However, in the presence of corrupted data (e.g., video or label noise) from a few devices the performance of weight averaging quickly degrades. To address this, we leverage knowledge about the expected noise profile within the video to intelligently adjust how the model weights are averaged on the server. Our results show that this significantly improves upon the robustness of models even when the signal-to-noise ratio is low. © 2022 IEEE.