An oculometrics-based biofeedback system to impede fatigue development during computer work: A proof-of-concept study
A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stag...
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| Published in | PloS one Vol. 14; no. 5; p. e0213704 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
31.05.2019
Public Library of Science (PLoS) |
| Series | Studies in Health Technology and Informatics |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0213704 |
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| Abstract | A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31-35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen's dz = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work. |
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| AbstractList | A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31-35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen's dz = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work.A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31-35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen's dz = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work. A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31-35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen's dz = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work. A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31–35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen’s d z = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work. Background: Health professionals need valid devices to assess a stroke individual’s ability to walk. The aim was to evaluate the validity of the estimation of the walked distance by wearable devices and the impact of the sensor’s position in stroke individuals. Methods: Post-stroke patients able to walk without human assistance were equipped with several wearable devices: pedometers, Actigraph, and Sensewear Armband placed according to the manufacturers' recommendations. Participants walked for 6 min at a comfortable speed wearing all sensors at the same time. We analyzed the validity of sensor-estimated distances according to their position using Bland–Altman analysis, root-mean-square error, and coefficient of correlation. Results: In total, 35 individuals were included (mean age = 65 ± 15 years). The best estimations were given by the Actigraph worn on the unaffected ankle (mean bias (MB) = 22.6 ± 32.4 m; p = 0.37) and by the pedometer worn on the unaffected hip (MB = 20.5 ± 24.6 m; p = 0.46). The other sensors and positions provided large estimation errors over 95 m (p < 0.05). Conclusion: This study led to a recommendation of a pedometer worn on the unaffected hip or an Actigraph worn on the unaffected ankle to get a valid estimation of the distance walked by stroke individuals. |
| Author | Vuillerme, Nicolas Samani, Afshin Madeleine, Pascal Zargari Marandi, Ramtin Omland, Øyvind |
| AuthorAffiliation | 4 Institut Universitaire de France, Paris, France 2 Univ. Grenoble Alpes, AGEIS, Grenoble, France 1 Department of Health Science and Technology, Sport Sciences, Aalborg University, Aalborg, Denmark 3 Aalborg University Hospital, Clinic of Occupational Medicine, Danish Ramazzini Center, Aalborg, Denmark Politechnika Krakowska im Tadeusza Kosciuszki, POLAND |
| AuthorAffiliation_xml | – name: 2 Univ. Grenoble Alpes, AGEIS, Grenoble, France – name: 3 Aalborg University Hospital, Clinic of Occupational Medicine, Danish Ramazzini Center, Aalborg, Denmark – name: 1 Department of Health Science and Technology, Sport Sciences, Aalborg University, Aalborg, Denmark – name: 4 Institut Universitaire de France, Paris, France – name: Politechnika Krakowska im Tadeusza Kosciuszki, POLAND |
| Author_xml | – sequence: 1 givenname: Ramtin orcidid: 0000-0001-9233-1656 surname: Zargari Marandi fullname: Zargari Marandi, Ramtin – sequence: 2 givenname: Pascal orcidid: 0000-0002-2164-234X surname: Madeleine fullname: Madeleine, Pascal – sequence: 3 givenname: Øyvind surname: Omland fullname: Omland, Øyvind – sequence: 4 givenname: Nicolas surname: Vuillerme fullname: Vuillerme, Nicolas – sequence: 5 givenname: Afshin surname: Samani fullname: Samani, Afshin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31150405$$D View this record in MEDLINE/PubMed https://hal.science/hal-05005653$$DView record in HAL |
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| CitedBy_id | crossref_primary_10_1111_psyp_13828 crossref_primary_10_1016_j_jelekin_2024_102936 crossref_primary_10_1016_j_ijhcs_2023_103079 crossref_primary_10_1038_s41598_024_70141_y crossref_primary_10_1093_milmed_usab032 crossref_primary_10_1093_milmed_usab509 crossref_primary_10_2217_fnl_2019_0012 crossref_primary_10_1038_s41598_022_24415_y |
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| Copyright | 2019 Zargari Marandi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License 2019 Zargari Marandi et al 2019 Zargari Marandi et al |
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| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
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| Snippet | A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback... Background: Health professionals need valid devices to assess a stroke individual’s ability to walk. The aim was to evaluate the validity of the estimation of... |
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| StartPage | e0213704 |
| SubjectTerms | Aeronautics Biofeedback Biofeedback, Psychology - methods Biology and Life Sciences Cognition & reasoning Computers Decision trees Engineering Sciences Eye Movements Fatigue Fatigue - diagnosis Fatigue - prevention & control Feedback Female Females Humans Life Sciences Male Males Medicine and Health Sciences Models, Statistical Physical Sciences Physiology Reflex, Pupillary Sleep and wakefulness Sleepiness Social Sciences Systematic review Taskload Workload |
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| Title | An oculometrics-based biofeedback system to impede fatigue development during computer work: A proof-of-concept study |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/31150405 https://www.proquest.com/docview/2233258877 https://www.proquest.com/docview/2233852808 https://hal.science/hal-05005653 https://pubmed.ncbi.nlm.nih.gov/PMC6544207 https://doaj.org/article/1afe199cb555410382c1e6cabdd38062 http://dx.doi.org/10.1371/journal.pone.0213704 |
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