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López-Ahumada R, Jiménez-Naharro R, Gómez-Bravo F. A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115339. [PMID: 37300066 DOI: 10.3390/s23115339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human-computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain-computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.
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Affiliation(s)
- Rafael López-Ahumada
- Departamento de Ingeniería Electrónica Sistemas Informáticos y Automática, Universidad de Huelva, 21007 Huelva, Spain
| | - Raúl Jiménez-Naharro
- Departamento de Ingeniería Electrónica Sistemas Informáticos y Automática, Universidad de Huelva, 21007 Huelva, Spain
| | - Fernando Gómez-Bravo
- Centro Científico Tecnológico de Huelva (CCTH), University of Huelva, 21007 Huelva, Spain
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2
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197472. [PMID: 36236570 PMCID: PMC9573761 DOI: 10.3390/s22197472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 05/13/2023]
Abstract
Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
- Correspondence: ; Tel.: +1-(581)624-9394
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Schweizer T, Wyss T, Gilgen-Ammann R. Detecting Soldiers' Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities. Mil Med 2021; 187:e1330-e1337. [PMID: 34915554 PMCID: PMC10100772 DOI: 10.1093/milmed/usab509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/04/2021] [Accepted: 11/29/2021] [Indexed: 11/14/2022] Open
Abstract
INTRODUCTION Objectively determining soldiers' fatigue levels could help prevent injuries or accidents resulting from inattention or decreased alertness. Eye-tracking technologies, such as optical eye tracking (OET) and electrooculography (EOG), are often used to monitor fatigue. Eyeblinks-especially blink frequency and blink duration-are known as easily observable and valid biomarkers of fatigue. Currently, various eye trackers (i.e., eye-tracking glasses) are available on the market using either OET or EOG technologies. These wearable eye trackers offer several advantages, including unobtrusive functionality, practicality, and low costs. However, several challenges and limitations must be considered when implementing these technologies in the field to monitor fatigue levels. This review investigates the feasibility of eye tracking in the field focusing on the practical applications in military operational environments. MATERIALS AND METHOD This paper summarizes the existing literature about eyeblink dynamics and available wearable eye-tracking technologies, exposing challenges and limitations, as well as discussing practical recommendations on how to improve the feasibility of eye tracking in the field. RESULTS So far, no eye-tracking glasses can be recommended for use in a demanding work environment. First, eyeblink dynamics are influenced by multiple factors; therefore, environments, situations, and individual behavior must be taken into account. Second, the glasses' placement, sunlight, facial or body movements, vibrations, and sweat can drastically decrease measurement accuracy. The placement of the eye cameras for the OET and the placement of the electrodes for the EOG must be chosen consciously, the sampling rate must be minimal 200 Hz, and software and hardware must be robust to resist any factors influencing eye tracking. CONCLUSION Monitoring physiological and psychological readiness of soldiers, as well as other civil professionals that face higher risks when their attention is impaired or reduced, is necessary. However, improvements to eye-tracking devices' hardware, calibration method, sampling rate, and algorithm are needed in order to accurately monitor fatigue levels in the field.
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Affiliation(s)
- Theresa Schweizer
- Monitoring, Swiss Federal Institute of Sport Magglingen (SFISM), Macolin 2532, Switzerland
| | - Thomas Wyss
- Monitoring, Swiss Federal Institute of Sport Magglingen (SFISM), Macolin 2532, Switzerland
| | - Rahel Gilgen-Ammann
- Monitoring, Swiss Federal Institute of Sport Magglingen (SFISM), Macolin 2532, Switzerland
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Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health 2021; 3:662811. [PMID: 34713137 PMCID: PMC8521964 DOI: 10.3389/fdgth.2021.662811] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/21/2022] Open
Abstract
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.
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Affiliation(s)
- Mahsa Sheikh
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - M Qassem
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
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Kodithuwakku Arachchige SNK, Burch V RF, Chander H, Turner AJ, Knight AC. The use of wearable devices in cognitive fatigue: current trends and future intentions. THEORETICAL ISSUES IN ERGONOMICS SCIENCE 2021. [DOI: 10.1080/1463922x.2021.1965670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
| | - Reuben F. Burch V
- Industrial & Systems Engineering, Mississippi State University, Starkville, MS, USA
- Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, USA
| | - Harish Chander
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Starkville, MS, USA
- Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, USA
| | - Alana J. Turner
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Starkville, MS, USA
| | - Adam C. Knight
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Starkville, MS, USA
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Bafna T, Hansen JP. Mental fatigue measurement using eye metrics: A systematic literature review. Psychophysiology 2021; 58:e13828. [PMID: 33825234 DOI: 10.1111/psyp.13828] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/07/2021] [Accepted: 03/17/2021] [Indexed: 11/30/2022]
Abstract
Mental fatigue measurement techniques utilize one or a combination of the cognitive, affective, and behavioral responses of the body. Eye-tracking and electrooculography, which are used to compute eye-based features, have gained momentum with increases in accuracy and robustness of the lightweight equipment emerging in the markets and can be used for objective and continuous assessment of mental fatigue. The main goal of this systematic review was to summarize the various eye-based features that have been used to measure mental fatigue and explore the relation of eye-based features to mental fatigue. The review process, following the preferred reporting items for systematic reviews and meta-analyses, used the electronic databases Web of Science, Scopus, ACM digital library, IEEE Xplore, and PubMed. Of the 1,385 retrieved documents, 34 studies met the inclusion criteria, resulting in 21 useful eye-based features. Categorizing these into eight groups revealed saccades as the most promising category, with saccade mean and peak velocity providing quick access to the cognitive states within 30 min of fatiguing activity. Complex brain networks involving sympathetic and parasympathetic nervous systems control the relation of mental fatigue to tonic pupil size and have the potential to indicate mental fatigue in controlled experimental conditions. Other categories, like blinks, are derived from the field of sleep research and should be used with caution. Several limitations emerged in the analysis, including varied experimental methods, use of dim lighting during the experiment (that could possibly also induce sleepiness), and use of unclear data analysis techniques, thereby complicating comparisons between studies.
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Affiliation(s)
- Tanya Bafna
- Department of Technology, Management and Economics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - John Paulin Hansen
- Department of Technology, Management and Economics, Technical University of Denmark, Kongens Lyngby, Denmark
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Bafna T, Bækgaard P, Hansen JP. Mental fatigue prediction during eye-typing. PLoS One 2021; 16:e0246739. [PMID: 33617541 PMCID: PMC7899326 DOI: 10.1371/journal.pone.0246739] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/26/2021] [Indexed: 11/24/2022] Open
Abstract
Mental fatigue is a common problem associated with neurological disorders. Until now, there has not been a method to assess mental fatigue on a continuous scale. Camera-based eye-typing is commonly used for communication by people with severe neurological disorders. We designed a working memory-based eye-typing experiment with 18 healthy participants, and obtained eye-tracking and typing performance data in addition to their subjective scores on perceived effort for every sentence typed and mental fatigue, to create a model of mental fatigue for eye-typing. The features of the model were the eye-based blink frequency, eye height and baseline-related pupil diameter. We predicted subjective ratings of mental fatigue on a six-point Likert scale, using random forest regression, with 22% lower mean absolute error than using simulations. When additionally including task difficulty (i.e. the difficulty of the sentences typed) as a feature, the variance explained by the model increased by 9%. This indicates that task difficulty plays an important role in modelling mental fatigue. The results demonstrate the feasibility of objective and non-intrusive measurement of fatigue on a continuous scale.
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Affiliation(s)
- Tanya Bafna
- Department of Management, Technology and Economics, Technical University of Denmark, Kongens Lyngby, Capital Region of Denmark, Denmark
- * E-mail:
| | - Per Bækgaard
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Capital Region of Denmark, Denmark
| | - John Paulin Hansen
- Department of Management, Technology and Economics, Technical University of Denmark, Kongens Lyngby, Capital Region of Denmark, Denmark
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Miki N, Miyazaki K, Morimoto Y. Editorial for the Special Issue of Selected Papers from the 8th Symposium on Micro⁻Nano Science and Technology on Micromachines. MICROMACHINES 2018; 9:E627. [PMID: 30486485 PMCID: PMC6316716 DOI: 10.3390/mi9120627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 11/25/2018] [Indexed: 11/17/2022]
Abstract
The Micro⁻Nano Science and Technology Division of JSME (Japan Society of Mechanical Engineers) promotes academic activities to pioneer novel research topics on microscopic mechanics. [...].
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Affiliation(s)
- Norihisa Miki
- Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.
| | - Koji Miyazaki
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu, Fukuoka 804-8550, Japan.
| | - Yuya Morimoto
- Institute of Industrial Science (IIS), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
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