Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/4/49 |
| _version_ | 1827621221293883392 |
|---|---|
| author | Patricia Gamboa Rui Varandas João Rodrigues Cátia Cepeda Cláudia Quaresma Hugo Gamboa |
| author_facet | Patricia Gamboa Rui Varandas João Rodrigues Cátia Cepeda Cláudia Quaresma Hugo Gamboa |
| author_sort | Patricia Gamboa |
| collection | DOAJ |
| description | Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention. |
| first_indexed | 2024-03-09T10:59:36Z |
| format | Article |
| id | doaj.art-d0112857cedf4386a533636ac31ab945 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T10:59:36Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-d0112857cedf4386a533636ac31ab9452023-12-01T01:22:18ZengMDPI AGComputers2073-431X2022-03-011144910.3390/computers11040049Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational DomainsPatricia Gamboa0Rui Varandas1João Rodrigues2Cátia Cepeda3Cláudia Quaresma4Hugo Gamboa5LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalLIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalLIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalLIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalLIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalLIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2825-149 Caparica, PortugalOccupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.https://www.mdpi.com/2073-431X/11/4/49occupational healthbiosignalsattentionmachine learningcognitive taskselectrocardiogram |
| spellingShingle | Patricia Gamboa Rui Varandas João Rodrigues Cátia Cepeda Cláudia Quaresma Hugo Gamboa Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains Computers occupational health biosignals attention machine learning cognitive tasks electrocardiogram |
| title | Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains |
| title_full | Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains |
| title_fullStr | Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains |
| title_full_unstemmed | Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains |
| title_short | Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains |
| title_sort | attention classification based on biosignals during standard cognitive tasks for occupational domains |
| topic | occupational health biosignals attention machine learning cognitive tasks electrocardiogram |
| url | https://www.mdpi.com/2073-431X/11/4/49 |
| work_keys_str_mv | AT patriciagamboa attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains AT ruivarandas attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains AT joaorodrigues attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains AT catiacepeda attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains AT claudiaquaresma attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains AT hugogamboa attentionclassificationbasedonbiosignalsduringstandardcognitivetasksforoccupationaldomains |