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

Abstract

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.

iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

“…All participants reported having no known health conditions, provided informed consent ahead of the study, and were compensated for their time following the study. Detailed demographic information of study participants is provided in the Table A5 in Appendix D. After being welcomed and briefed, participants were asked to remove any bulky clothing (e.g., winter coats, jackets) and seated comfortably in front of a 65-by-37-inch screen, where they were fitted with PhysioKit [20] sensors. The PPG sensor was attached to participants' left or right ear with a metal clip.…”

“…Customized software was developed using C++ programming language, which utilized the FLIR-provided Spinnaker library (https://www.flir.co.uk/products/spinnaker-sdk/, accessed on 7 November 2022) to acquire thermal infrared frames, while using OpenCV library functions to acquire RGB frames. The PhysioKit toolkit [20] was adapted to acquire the ground-truth PPG signals in synchronization with RGB and thermal frames. Implementation was adapted such that RGB frames, thermal frames, and PPG signal were acquired in separate and dedicated threads, while sharing a common onset trigger and a timer to stop the acquisition in a synchronized manner.…”

“…While PPG signals acquired from the ear tend to have good signal quality [20,51], it is still prone to noise artifacts due to head movement. Figure 2D shows a comparison of head movement across different experimental conditions, computed as the inter-frame rotation of facial frames.…”

“…These developments have been captured in a recent survey [60] that reviews signal-quality assessment methods for contact-based as well as imaging-based PPG. While the majority of works focus on developing a classifier model [58,[61][62][63] with binary inference for quality of the length of a PPG signal, a few recent works have proposed models that offer hightemporal-resolution-signal-quality assessment [20,22,23,64]. In this work, we first extend SQA-Phys, the signal-quality assessment method deployed in [20], such that the inference for signal quality is made per sample, resulting in a 1D dense segmentation task, and secondly we replace the CNN-based decoder of the encoder-decoder architecture with a decoder based on matrix decomposition [21].…”

“…Signal-quality assessment is conducted both manually through the visual inspection of the PPG signals as well as using an automated approach. For the latter, we deploy SQA-PhysMD, adapted from a recent work [20], in which the CNN-based decoder module is replaced with a more efficient matrix-decomposition-based module [21] and the inference is made per sample, making it a dense 1D segmentation task. SQA-PhysMD shows significantly improved performance against the existing SOTA methods for an automated PPG-signal-quality assessment [22,23] on the iBVP dataset (preliminary results presented in Table A4 in Appendix C).…”