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Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology
In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052076/ https://www.ncbi.nlm.nih.gov/pubmed/36991630 http://dx.doi.org/10.3390/s23062920 |
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author | Cheng, Chih-Feng Lin, Chiuhsiang Joe |
author_facet | Cheng, Chih-Feng Lin, Chiuhsiang Joe |
author_sort | Cheng, Chih-Feng |
collection | PubMed |
description | In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s 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’s 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–robot interaction. |
format | Online Article Text |
id | pubmed-10052076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100520762023-03-30 Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology Cheng, Chih-Feng Lin, Chiuhsiang Joe Sensors (Basel) Article In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s 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’s 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–robot interaction. MDPI 2023-03-08 /pmc/articles/PMC10052076/ /pubmed/36991630 http://dx.doi.org/10.3390/s23062920 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cheng, Chih-Feng Lin, Chiuhsiang Joe Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title | Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title_full | Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title_fullStr | Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title_full_unstemmed | Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title_short | Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology |
title_sort | building a low-cost wireless biofeedback solution: applying design science research methodology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052076/ https://www.ncbi.nlm.nih.gov/pubmed/36991630 http://dx.doi.org/10.3390/s23062920 |
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