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Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 9209

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Special Issue Editors

Dr. Andrei Dragomir

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Guest Editor

The N.1 Institute for Health, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077, Singapore
Interests: consumer neuroscience; neuroergonomics; human–machine interaction; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmet Omurtag

E-Mail Website
Guest Editor

Department of Engineering, Nottingham Trent University, Nottingham, UK
Interests: computational neuroscience; neuroergonomics; machine learning; multimodal neuroimaging; signal processing; functional near-infrared spectroscopy (fNIRS)

Dr. Stavros I. Dimitriadis

E-Mail Website
Guest Editor

1. Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
2. Institut de Neurociències, University of Barcelona, Barcelona, Spain
3. Integrative Neuroimaging Lab, 55133 Thessaloniki, Macedonia, Greece
4. Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
Interests: multimodal neuroimaging; genetic neuroimaging; network neuroscience; biomarkers; reproducibility in neuroscience and neuroimaging analysis; biomedical signal processing; artificial intelligence; machine learning; Alzheimer’s disease; schizophrenia; traumatic brain injury; intervention protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable and mobile sensing is a thriving and rapidly expanding field. This expansion has been driven by a wave of innovations in both hardware and software technologies which promise to change the way in which benchtop discoveries are being taken outside the lab. Increasingly miniaturized wearable devices and sensors are now being used in a range of applications, including:

Consumer neuroscience and neuromarketing which use neuroscientific methods to understand and predict consumer behavior, decision making and individual preferences, and to help improve product design and development.
Neuroergonomics, which applies neuroscience and neuropsychology knowledge to understand the relationship between brain function and the human performance of real-world tasks, enabling augmented cognition and training.
Digital Health, which uses sensors, computing platforms, connectivity and artificial intelligence to support clinical decisions and enable personalized therapeutics as well as general wellness.

With this gaining momentum and wide application areas, wearable devices that measure individuals’ physiology (e.g., brain, heart and visual activity, as well as other physiological signals) have become very popular and increasingly pervasive, and are creating a paradigm shift to help scientists collect, quantify, and observe human data outside the laboratory and in daily life conditions.

The goal of this Special Issue is to present original research and review articles on the latest advances, technologies, solutions, applications, and future challenges in areas including but not limited to:

Neuroimaging and neurophysiological techniques for consumer neuroscience, neuroergonomics and Digital Health applications: electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), heart rate (HR), galvanic skin response (GSR), eye tracking.
Multimodal experimental paradigms incorporating virtual reality (VR) environments.
Machine learning and artificial intelligence methods and algorithms that enable real-time decoding as well as offline neurophysiological and multi-modal data analysis.
Technological advances for interfacing with the peripheral and central nervous system.
Theoretical models and tools supporting translation efforts as well as the reliability and reproducibility of the findings.

Dr. Andrei Dragomir
Dr. Ahmet Omurtag
Dr. Stavros I. Dimitriadis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

wearable sensors
consumer neuroscience
neuroergonomics
Digital Health
neurophysiology
machine learning
artificial intelligence
human–machine interaction

Published Papers (6 papers)

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Research

15 pages, 4710 KiB

Open AccessArticle

Modulating Driver Alertness via Ambient Olfactory Stimulation: A Wearable Electroencephalography Study

by Mengting Jiang, Oranatt Chaichanasittikarn, Manuel Seet, Desmond Ng, Rahul Vyas, Gaurav Saini and Andrei Dragomir

Sensors 2024, 24(4), 1203; https://doi.org/10.3390/s24041203 - 12 Feb 2024

Viewed by 632

Abstract

Poor alertness levels and related changes in cognitive efficiency are common when performing monotonous tasks such as extended driving. Recent studies have investigated driver alertness decrement and possible strategies for modulating alertness with the goal of improving reaction times to safety critical events. However, most studies rely on subjective measures in assessing alertness changes, while the use of olfactory stimuli, which are known to be strong modulators of cognitive states, has not been commensurately explored in driving alertness settings. To address this gap, in the present study we investigated the effectiveness of olfactory stimuli in modulating the alertness state of drivers and explored the utility of electroencephalography (EEG) in developing objective brain-based tools for assessing the resulting changes in cortical activity. Olfactory stimulation induced a significant differential effect on braking reaction time. The corresponding effect to the cortical activity was characterized using EEG-derived metrics and the devised machine learning framework yielded a high discriminating accuracy (92.1%). Furthermore, neural activity in the alpha frequency band was found to be significantly associated with the observed drivers’ behavioral changes. Overall, our results demonstrate the potential of olfactory stimuli to modulate the alertness state and the efficiency of EEG in objectively assessing the resulting cognitive changes. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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14 pages, 993 KiB

Open AccessArticle

Atrial Fibrillation Detection with Single-Lead Electrocardiogram Based on Temporal Convolutional Network–ResNet

by Xiangyu Zhao, Rong Zhou, Li Ning, Qiuquan Guo, Yan Liang and Jun Yang

Sensors 2024, 24(2), 398; https://doi.org/10.3390/s24020398 - 09 Jan 2024

Viewed by 769

Abstract

Atrial fibrillation, one of the most common persistent cardiac arrhythmias globally, is known for its rapid and irregular atrial rhythms. This study integrates the temporal convolutional network (TCN) and residual network (ResNet) frameworks to effectively classify atrial fibrillation in single-lead ECGs, thereby enhancing the application of neural networks in this field. Our model demonstrated significant success in detecting atrial fibrillation, with experimental results showing an accuracy rate of 97% and an F1 score of 87%. These figures indicate the model’s exceptional performance in identifying both majority and minority classes, reflecting its balanced and accurate classification capability. This research offers new perspectives and tools for diagnosis and treatment in cardiology, grounded in advanced neural network technology. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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22 pages, 3610 KiB

Open AccessArticle

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

by Max Nobre Supelnic, Afonso Fortes Ferreira, Patrícia Justo Bota, Luís Brás-Rosário and Hugo Plácido da Silva

Sensors 2024, 24(1), 214; https://doi.org/10.3390/s24010214 - 29 Dec 2023

Viewed by 1095

Abstract

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors’ performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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23 pages, 7228 KiB

Open AccessArticle

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings

by Zohreh Zakeri, Arshia Arif, Ahmet Omurtag, Philip Breedon and Azfar Khalid

Sensors 2023, 23(21), 8926; https://doi.org/10.3390/s23218926 - 02 Nov 2023

Cited by 2 | Viewed by 1288

Abstract

Collaborative robots (cobots) have largely replaced conventional industrial robots in today’s workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human–robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots’ irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker’s performance in a human–robot collaborative environment. In this study, factory workers’ mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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18 pages, 3895 KiB

Open AccessArticle

A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees

by Gianluca Di Flumeri, Andrea Giorgi, Daniele Germano, Vincenzo Ronca, Alessia Vozzi, Gianluca Borghini, Luca Tamborra, Ilaria Simonetti, Rossella Capotorto, Silvia Ferrara, Nicolina Sciaraffa, Fabio Babiloni and Pietro Aricò

Sensors 2023, 23(20), 8389; https://doi.org/10.3390/s23208389 - 11 Oct 2023

Cited by 1 | Viewed by 1029

Abstract

When assessing trainees’ progresses during a driving training program, instructors can only rely on the evaluation of a trainee’s explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one’s mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver’s subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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17 pages, 6732 KiB

Open AccessArticle

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

by Maryam Doborjeh, Xiaoxu Liu, Zohreh Doborjeh, Yuanyuan Shen, Grant Searchfield, Philip Sanders, Grace Y. Wang, Alexander Sumich and Wei Qi Yan

Sensors 2023, 23(2), 902; https://doi.org/10.3390/s23020902 - 12 Jan 2023

Cited by 3 | Viewed by 3551

Abstract

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensing for Consumer Neuroscience, Neuroergonomics and Out-of-the-Lab Applications)

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