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The proliferation of physiological sensors opens new opportunities to exploreinteractions, conduct experiments and evaluate the user experience withcontinuous monitoring of bodily functions. Commercial devices, however, can becostly or limit access to raw waveform data, while low-cost sensors areefforts-intensive to setup. To address these challenges, we introducePhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKitprovides a one-stop pipeline consisting of (i) a sensing and data acquisitionlayer that can be configured in a modular manner per research needs, (ii) asoftware application layer that enables data acquisition, real-timevisualization and machine learning (ML)-enabled signal quality assessment. Thisalso supports basic visual biofeedback configurations and synchronizedacquisition for co-located or remote multi-user settings. In a validation studywith 16 participants, PhysioKit shows strong agreement with research-gradesensors on measuring heart rate and heart rate variability metrics data.Furthermore, we report usability survey results from 10 small-project teams (44individual members in total) who used PhysioKit for 4-6 weeks, providinginsights into its use cases and research benefits. Lastly, we discuss theextensibility and potential impact of the toolkit on the research community.

PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user Studies