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Objective We explore the relationships between objective communication patterns displayed during virtual team meetings and established, qualitative measures of team member effectiveness.
Background A key component of teamwork is communication. Automated measures of objective communication patterns are becoming more feasible and offer the ability to measure and monitor communication in a scalable, consistent and continuous manner. However, their validity in reflecting meaningful measures of teamwork processes are not well established, especially in real-world settings.
Method We studied real-world virtual student teams working on semester-long projects. We captured virtual team meetings using the Zoom video conferencing platform throughout the semester and periodic surveys comprising peer ratings of team member effectiveness. Leveraging audio transcripts, we examined relationships between objective measures of speaking time, silence gap duration and vocal turn-taking and peer ratings of team member effectiveness.
Results Speaking time, speaking turn count, degree centrality and (marginally) speaking turn duration, but not silence gap duration, were positively related to individual-level team member effectiveness. Time in dyadic interactions and interaction count, but not interaction length, were positively related to dyad-level team member effectiveness.
Conclusion Our study highlights the relevance of objective measures of speaking time and vocal turn-taking to team member effectiveness in virtual project-based teams, supporting the validity of these objective measures and their use in future research.
Application Our approach offers a scalable, easy-to-use method for measuring communication patterns and team member effectiveness in virtual teams and opens the opportunity to study these patterns in a more continuous and dynamic manner.
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null (Ed.)Deep learning approaches currently achieve the state-of-the-art results on camera-based vital signs measurement. One of the main challenges with using neural models for these applications is the lack of sufficiently large and diverse datasets. Limited data increases the chances of overfitting models to the available data which in turn can harm generalization. In this paper, we show that the generalizability of imaging photoplethysmography models can be improved by augmenting the training set with "magnified" videos. These augmentations are specifically designed to reveal useful features for recovering the photoplethysmogram. We show that using augmentations of this form is more effective at improving model robustness than other commonly used data augmentation approaches. We show better within-dataset and especially cross-dataset performance with our proposed data augmentation approach on three publicly available datasets.more » « less
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Camera-based physiological measurement enables vital signs to be captured unobtrusively without contact with the body. Remote, or imaging, photoplethysmography involves recovering peripheral blood flow from subtle variations in video pixel intensities. While the pulse signal might be easy to obtain from high quality uncompressed videos, the signal-to-noise ratio drops dramatically with video bitrate. Uncompressed videos incur large file storage and data transfer costs, making analysis, manipulation and sharing challenging. To help address these challenges, we use compression specific supervised models to mitigate the effect of temporal video compression on heart rate estimates. We perform a systematic evaluation of the performance of state-of-the-art algorithms across different levels, and formats, of compression. We demonstrate that networks trained on compressed videos consistently outperform other benchmark methods, both on stationary videos and videos with significant rigid head motions. By training on videos with the same, or higher compression factor than test videos, we achieve improvements in signal-to-noise ratio (SNR) of up to 3 dB and mean absolute error (MAE) of up to 6 beats per minute (BPM).
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null (Ed.)It is well established that many datasets used for computer vision tasks are not representative and may be biased. The result of this is that evaluation metrics may not reflect real-world performance and might expose some groups (often minorities) to greater risks than others. Imaging photoplethysmography is a set of techniques that enables noncontact measurement of vital signs using imaging devices. While these methods hold great promise for low-cost and scalable physiological monitoring, it is important that performance is characterized accurately over diverse populations. We perform a meta-analysis across three datasets, including 73 people and over 400 videos featuring a broad range of skin types to study how skin types and gender affect the measurements. While heart rate measurement can be performed on all skin types under certain conditions, we find that average performance drops significantly for the darkest skin type. We also observe a slight drop in the performance for females. We compare supervised and unsupervised learning algorithms and find that skin type does not impact all methods equally. The imaging photoplethysmography community should devote greater efforts to addressing these disparities and collecting representative datasets.more » « less
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