{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:58:13Z","timestamp":1760147893029,"version":"build-2065373602"},"reference-count":92,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC-110-2223-E-011-001-MY3"],"award-info":[{"award-number":["NSTC-110-2223-E-011-001-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants\u2019 self-evaluation. Affective computing uses biometrics to recognize people\u2019s emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform\u2019s effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human\u2013robot interaction.<\/jats:p>","DOI":"10.3390\/s23062920","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T03:29:05Z","timestamp":1678246145000},"page":"2920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7587-6424","authenticated-orcid":false,"given":"Chih-Feng","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-0527","authenticated-orcid":false,"given":"Chiuhsiang Joe","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Jani, A.B., Bagree, R., and Roy, A.K. 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