PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

Area of Science:

• Physiological computing
• Human-computer interaction
• Biomedical engineering

Background:

• Physiological sensors offer new research avenues but commercial options are expensive and low-cost sensors require significant setup.
• Access to raw physiological waveform data is crucial for in-depth analysis and user experience evaluation.
• Existing solutions often present a trade-off between cost, data accessibility, and ease of implementation.

Purpose of the Study:

• Introduce PhysioKit, an open-source, low-cost physiological computing toolkit.
• Provide a comprehensive pipeline for physiological data acquisition, processing, and analysis.
• Validate the performance and usability of PhysioKit in a research context.

Main Methods:

• Developed a modular sensing and data acquisition layer for flexible configuration.
• Created a software application layer supporting real-time visualization and machine learning (ML)-enabled signal quality assessment.
• Integrated basic visual biofeedback and synchronized multi-user acquisition capabilities.
• Conducted a validation study comparing PhysioKit with research-grade sensors for heart rate and heart rate variability.
• Performed a usability survey with 10 project teams (44 members) using PhysioKit for 4-6 weeks.

Main Results:

• PhysioKit demonstrated strong agreement with research-grade sensors in measuring heart rate and heart rate variability.
• Validation study with 16 participants confirmed the accuracy of the toolkit.
• Usability survey provided positive insights into PhysioKit's use cases and research benefits.
• The toolkit supports synchronized acquisition for both co-located and remote multi-user settings.

Conclusions:

• PhysioKit offers a viable, low-cost, and accessible solution for physiological computing research.
• The toolkit's modular design and ML-enabled features enhance data acquisition and analysis.
• PhysioKit has the potential to significantly impact and broaden participation in physiological computing research.