Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations
In the context of evolving healthcare technologies, this study investigates the application of AI and machine learning in video-based health monitoring systems, focusing on the challenges and potential of implementing such systems in real-world scenarios, specifically for knowledge workers. The rese...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1357 |
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| author | Simão Ferreira Catarina Marinheiro Catarina Mateus Pedro Pereira Rodrigues Matilde A. Rodrigues Nuno Rocha |
| author_facet | Simão Ferreira Catarina Marinheiro Catarina Mateus Pedro Pereira Rodrigues Matilde A. Rodrigues Nuno Rocha |
| author_sort | Simão Ferreira |
| collection | DOAJ |
| description | In the context of evolving healthcare technologies, this study investigates the application of AI and machine learning in video-based health monitoring systems, focusing on the challenges and potential of implementing such systems in real-world scenarios, specifically for knowledge workers. The research underscores the criticality of addressing technological, ethical, and practical hurdles in deploying these systems outside controlled laboratory environments. Methodologically, the study spanned three months and employed advanced facial recognition technology embedded in participants’ computing devices to collect physiological metrics such as heart rate, blinking frequency, and emotional states, thereby contributing to a stress detection dataset. This approach ensured data privacy and aligns with ethical standards. The results reveal significant challenges in data collection and processing, including biases in video datasets, the need for high-resolution videos, and the complexities of maintaining data quality and consistency, with 42% (after adjustments) of data lost. In conclusion, this research emphasizes the necessity for rigorous, ethical, and technologically adapted methodologies to fully realize the benefits of these systems in diverse healthcare contexts. |
| format | Article |
| id | doaj-art-b65e7d3ff2fc4a70b25b0f3c0cbb38a5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b65e7d3ff2fc4a70b25b0f3c0cbb38a52025-08-20T02:59:01ZengMDPI AGSensors1424-82202025-02-01255135710.3390/s25051357Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data ConsiderationsSimão Ferreira0Catarina Marinheiro1Catarina Mateus2Pedro Pereira Rodrigues3Matilde A. Rodrigues4Nuno Rocha5RISE-Health, Center for Translational Health and Medical Biotechnology Research (TBIO), ESS, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalCentro Hospitalar de Vila Nova de Gaia/Espinho, 4430-999 Vila Nova de Gaia, PortugalRISE-Health, Center for Translational Health and Medical Biotechnology Research (TBIO), ESS, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalMEDCIDS—Department of Community Medicine, Information and Decision Sciences, Faculty of Medicine, University of Porto, 4200-450 Porto, PortugalRISE-Health, Center for Translational Health and Medical Biotechnology Research (TBIO), ESS, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalRISE-Health, Center for Translational Health and Medical Biotechnology Research (TBIO), ESS, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalIn the context of evolving healthcare technologies, this study investigates the application of AI and machine learning in video-based health monitoring systems, focusing on the challenges and potential of implementing such systems in real-world scenarios, specifically for knowledge workers. The research underscores the criticality of addressing technological, ethical, and practical hurdles in deploying these systems outside controlled laboratory environments. Methodologically, the study spanned three months and employed advanced facial recognition technology embedded in participants’ computing devices to collect physiological metrics such as heart rate, blinking frequency, and emotional states, thereby contributing to a stress detection dataset. This approach ensured data privacy and aligns with ethical standards. The results reveal significant challenges in data collection and processing, including biases in video datasets, the need for high-resolution videos, and the complexities of maintaining data quality and consistency, with 42% (after adjustments) of data lost. In conclusion, this research emphasizes the necessity for rigorous, ethical, and technologically adapted methodologies to fully realize the benefits of these systems in diverse healthcare contexts.https://www.mdpi.com/1424-8220/25/5/1357monitoringphysiologicalartificial intelligenceindustryhealthcarevideo assisted techniques |
| spellingShingle | Simão Ferreira Catarina Marinheiro Catarina Mateus Pedro Pereira Rodrigues Matilde A. Rodrigues Nuno Rocha Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations Sensors monitoring physiological artificial intelligence industry healthcare video assisted techniques |
| title | Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations |
| title_full | Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations |
| title_fullStr | Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations |
| title_full_unstemmed | Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations |
| title_short | Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations |
| title_sort | overcoming challenges in video based health monitoring real world implementation ethics and data considerations |
| topic | monitoring physiological artificial intelligence industry healthcare video assisted techniques |
| url | https://www.mdpi.com/1424-8220/25/5/1357 |
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