Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
72 Downloads (Pure)


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.

Original languageEnglish
Article number49
Number of pages18
Issue number4
Publication statusPublished - 24 Mar 2022


  • attention
  • biosignals
  • cognitive tasks
  • electrocardiogram
  • electroencephalogram
  • machine learning
  • occupational health


Dive into the research topics of 'Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains'. Together they form a unique fingerprint.

Cite this