PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/19/8244 |
| _version_ | 1797575166260150272 |
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| author | Jitesh Joshi Katherine Wang Youngjun Cho |
| author_facet | Jitesh Joshi Katherine Wang Youngjun Cho |
| author_sort | Jitesh Joshi |
| collection | DOAJ |
| description | 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 <i>PhysioKit</i>, an open-source, low-cost physiological computing toolkit. <i>PhysioKit</i> 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, <i>PhysioKit</i> 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 <i>PhysioKit</i> 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. |
| first_indexed | 2024-03-10T21:34:48Z |
| format | Article |
| id | doaj.art-8ac4e90062224b8394d0ad78bb494bec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:48Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ac4e90062224b8394d0ad78bb494bec2023-11-19T15:04:48ZengMDPI AGSensors1424-82202023-10-012319824410.3390/s23198244PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User StudiesJitesh Joshi0Katherine Wang1Youngjun Cho2Department of Computer Science, University College London, London NW1 2AE, UKDepartment of Computer Science, University College London, London NW1 2AE, UKDepartment of Computer Science, University College London, London NW1 2AE, UKThe 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 <i>PhysioKit</i>, an open-source, low-cost physiological computing toolkit. <i>PhysioKit</i> 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, <i>PhysioKit</i> 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 <i>PhysioKit</i> 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.https://www.mdpi.com/1424-8220/23/19/8244physiological computingdata acquisition toolkitmulti-user HCI studiesbiofeedbacksignal quality assessment |
| spellingShingle | Jitesh Joshi Katherine Wang Youngjun Cho PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies Sensors physiological computing data acquisition toolkit multi-user HCI studies biofeedback signal quality assessment |
| title | PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies |
| title_full | PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies |
| title_fullStr | PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies |
| title_full_unstemmed | PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies |
| title_short | PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies |
| title_sort | physiokit an open source low cost physiological computing toolkit for single and multi user studies |
| topic | physiological computing data acquisition toolkit multi-user HCI studies biofeedback signal quality assessment |
| url | https://www.mdpi.com/1424-8220/23/19/8244 |
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