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Ahmed, Mobyen Uddin, DrORCID iD iconorcid.org/0000-0003-3802-4721
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Biography [swe]

Mobyen Uddin Ahmed is Senior Lecturer/Assistant Professor in Computer Science and Artificial Intelligence at Artificial Intelligence and Intelligent Systems and a member of ESS-H - Embedded Sensor Systems for Health Research Profile. Mobyen has 100+ scientific publication and more than 1173 citations.

He is involved in research and development since 2005 after completing his M.Sc. in Computer Engineering (thesis) from Dalarna University, Sweden. He received his PhD (thesis) in computer science in 2011 from Mälardalen University. He has completed one postdoctoral study between the years 2012 and 2014 in Computer Science and Engineering (Center for Applied Autonomous Sensor Systems) at School of Science and Technology, Örebro University, Sweden

To mention some courses those, I am involved in teaching: Applied Artificial Intelligence, Project in intelligent embedded systemsMachine Learning With Big Data (a distance course for industrial professionals), Databases, Deep learning for industrial imaging (comming), Predictive analytics (comming) etc.

Publications (10 of 107) Show all publications
Rahman, H., Ahmed, M. U. & Begum, S. (2020). Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration. IEEE Transactions on Biomedical Engineering, 67(1), 88-98, Article ID 8715455.
Open this publication in new window or tab >>Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration
2020 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 67, no 1, p. 88-98, article id 8715455Article in journal (Refereed) Published
Abstract [en]

Objective: In this paper, four physiological parameters, i.e., heart rate (HR), inter-beat-interval (IBI), heart rate variability (HRV), and oxygen saturation (SpO2), are extracted from facial video recordings. Methods: Facial videos were recorded for 10 min each in 30 test subjects while driving a simulator. Four regions of interest (ROIs) are automatically selected in each facial image frame based on 66 facial landmarks. Red-green-blue color signals are extracted from the ROIs and four physiological parameters are extracted from the color signals. For the evaluation, physiological parameters are also recorded simultaneously using a traditional sensor 'cStress,' which is attached to hands and fingers of test subjects. Results: The Bland Altman plots show 95% agreement between the camera system and 'cStress' with the highest correlation coefficient R = 0.96 for both HR and SpO2. The quality index is estimated for IBI considering 100 ms R-peak error; the accumulated percentage achieved is 97.5%. HRV features in both time and frequency domains are compared and the highest correlation coefficient achieved is 0.93. One-way analysis of variance test shows that there are no statistically significant differences between the measurements by camera and reference sensors. Conclusion: These results present high degrees of accuracy of HR, IBI, HRV, and SpO2 extraction from facial image sequences. Significance: The proposed non-contact approach could broaden the dimensionality of physiological parameters extraction using cameras. This proposed method could be applied for driver monitoring application under realistic conditions, i.e., illumination, motion, movement, and vibration.

Place, publisher, year, edition, pages
IEEE Computer Society, 2020
Keywords
Ambient illumination, driver monitoring, motion, movement, non-contact, physiological parameters, vibration, Cameras, Extraction, Heart, Video recording, Physiological models
National Category
Control Engineering
Identifiers
urn:nbn:se:mdh:diva-46689 (URN)10.1109/TBME.2019.2908349 (DOI)000505526300009 ()2-s2.0-85077175941 (Scopus ID)
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-23Bibliographically approved
Rahman, H., Ahmed, M. U., Barua, S. & Begum, S. (2020). Non-contact-based driver's cognitive load classification using physiological and vehicular parameters. Biomedical Signal Processing and Control, 55, Article ID 101634.
Open this publication in new window or tab >>Non-contact-based driver's cognitive load classification using physiological and vehicular parameters
2020 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, article id 101634Article in journal (Refereed) Published
Abstract [en]

Classification of cognitive load for vehicular drivers is a complex task due to underlying challenges of the dynamic driving environment. Many previous works have shown that physiological sensor signals or vehicular data could be a reliable source to quantify cognitive load. However, in driving situations, one of the biggest challenges is to use a sensor source that can provide accurate information without interrupting diverging tasks. In this paper, instead of traditional wire-based sensors, non-contact camera and vehicle data are used that have no physical contact with the driver and do not interrupt driving. Here, four machine learning algorithms, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA) and neural networks (NN), are investigated to classify the cognitive load using the collected data from a driving simulator study. In this paper, physiological parameters are extracted from facial video images, and vehicular parameters are collected from controller area networks (CAN). The data collection was performed in close collaboration with industrial partners in two separate studies, in which study-1 was designed with a 1-back task and study-2 was designed with both 1-back and 2-back task. The goal of the experiment is to investigate how accurately the machine learning algorithms can classify drivers' cognitive load based on the extracted features in complex dynamic driving environments. According to the results, for the physiological parameters extracted from the facial videos, the LR model with logistic function outperforms the other three classification methods. Here, in study-1, the achieved average accuracy for the LR classifier is 94% and in study-2 the average accuracy is 82%. In addition, the classification accuracy for the collected physiological parameters was compared with reference wire-sensor signals. It is observed that the classification accuracies between the sensor and the camera are very similar; however, better accuracy is achieved with the camera data due to having lower artefacts than the sensor data. 

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2020
Keywords
Non-contact, Physiological parameters, Vehicular parameters, Cognitive load, Classification, Logistic regression, Support vector machine, Decision tree
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46634 (URN)10.1016/j.bspc.2019.101634 (DOI)000502893200022 ()2-s2.0-85071533851 (Scopus ID)
Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-02-20Bibliographically approved
Köckemann, U., Alirezaie, M., Renoux, J., Tsiftes, N., Ahmed, M. U., Morberg, D., . . . Loutfi, A. (2020). Open-source data collection and data sets for activity recognition in smart homes. Sensors, 20(3), Article ID 879.
Open this publication in new window or tab >>Open-source data collection and data sets for activity recognition in smart homes
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2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Place, publisher, year, edition, pages
MDPI AG, 2020
Keywords
Data collection software, Prototype installation, Smart home data sets, Automation, Data acquisition, Intelligent buildings, Open source software, Pattern recognition, Software prototyping, Activities of Daily Living, Activity recognition, Configuration planning, Method development, Physical environments, Smart homes, Technical infrastructure, Open Data
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-47106 (URN)10.3390/s20030879 (DOI)000517786200303 ()32041376 (PubMedID)2-s2.0-85079189175 (Scopus ID)
Available from: 2020-02-21 Created: 2020-02-21 Last updated: 2020-04-09Bibliographically approved
Altarabichi, M. G., Ahmed, M. U., Ciceri, M. R., Balzarotti, S., Biassoni, F., Lombardi, D. & Perego, P. (2020). Reaction Time Variability Association with Unsafe Driving. In: Transport Research Arena TRA2020: . Paper presented at Transport Research Arena TRA2020, 27 Apr 2020, Helsinki, Finland. Helsinki, Finland
Open this publication in new window or tab >>Reaction Time Variability Association with Unsafe Driving
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2020 (English)In: Transport Research Arena TRA2020, Helsinki, Finland, 2020Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates several human factors including visual field, reaction speed, driving behavior and personality traits based on results of a cognitive assessment test targeting drivers in a Naturalistic Driving Study (NDS). Frequency of being involved in Near Miss event (fnm) and Frequency of committing Traffic Violation (ftv) are defined as indexes of safe driving in this work. Inference of association shows statistically significant correlation between Standard Deviation of Reaction Time (σRT) and both safe driving indexes fnm and ftv. Causal relationship analysis excludes age as confounding factor as variations in behavioral responses is observed in both younger and older drivers of this study.

Place, publisher, year, edition, pages
Helsinki, Finland: , 2020
Keywords
Road Safety, Naturalistic Driving, Vienna Test, Cognitive Assessment, Reaction Time Variability.
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45497 (URN)
Conference
Transport Research Arena TRA2020, 27 Apr 2020, Helsinki, Finland
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2020-03-12Bibliographically approved
Ahmed, M. U., Andersson, P., Andersson, T., Tomas Aparicio, E., Baaz, H., Barua, S., . . . Zambrano, J. (2019). A Machine Learning Approach for Biomass Characterization. In: Yan, J Yang, HX Li, H Chen, X (Ed.), INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS: . Paper presented at 10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG (pp. 1279-1287). ELSEVIER SCIENCE BV
Open this publication in new window or tab >>A Machine Learning Approach for Biomass Characterization
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2019 (English)In: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS / [ed] Yan, J Yang, HX Li, H Chen, X, ELSEVIER SCIENCE BV , 2019, p. 1279-1287Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Series
Energy Procedia, ISSN 1876-6102 ; 158
Keywords
Artificial Neural Network, Chemometrics, Gaussian Process Regression, Near Infrared Spectroscopy, Multiplicative Scatter Correction, Standard Normal Variate, Support Vector Regression, Partial Least Squares, Savitzky-Golay derivatives
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-44835 (URN)10.1016/j.egypro.2019.01.316 (DOI)000471031701100 ()2-s2.0-85063865772 (Scopus ID)
Conference
10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG
Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2019-10-14Bibliographically approved
Ahmed, M. U., Brickman, S., Dengg, A., Fasth, N., Mihajlovic, M. & Norman, J. (2019). A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS. In: International Conference on Modern Intelligent Systems Concepts MISC'18: . Paper presented at International Conference on Modern Intelligent Systems Concepts MISC'18, 12 Dec 2018, Rabat, Morocco.
Open this publication in new window or tab >>A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS
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2019 (English)In: International Conference on Modern Intelligent Systems Concepts MISC'18, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%. 

Keywords
Machine Learning (ML), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree(DT), Random Forest (RF), Extra Tree (ET), Gradient Boosted Trees (GBT), classification, Pedestrians’ events, Inertial Measurement Unit (IMU), Global Positioning System (GPS) signals
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-41724 (URN)
Conference
International Conference on Modern Intelligent Systems Concepts MISC'18, 12 Dec 2018, Rabat, Morocco
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2019-06-04Bibliographically approved
Ahmed, M. U., Brickman, S., Dengg, A., Fasth, N., Mihajlovic, M. & Norman, J. (2019). A machine learning approach to classify pedestrians’ events based on IMU and GPS. International Journal of Artificial Intelligence, 17(2), 154-167
Open this publication in new window or tab >>A machine learning approach to classify pedestrians’ events based on IMU and GPS
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2019 (English)In: International Journal of Artificial Intelligence, ISSN 0974-0635, E-ISSN 0974-0635, Vol. 17, no 2, p. 154-167Article in journal (Refereed) Published
Abstract [en]

This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%. 

Place, publisher, year, edition, pages
CESER Publications, 2019
Keywords
Artificial Neural Network (ANN), Classification, Decision Tree (DT), Extra Tree (ET), Gradient Boosted Trees (GBT), Machine Learning (ML), Pedestrians’ event, Random Forest (RF), Support Vector Machine (SVM)
National Category
Bioinformatics (Computational Biology) Signal Processing
Identifiers
urn:nbn:se:mdh:diva-45833 (URN)2-s2.0-85073358186 (Scopus ID)
Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2019-12-16Bibliographically approved
Ahmed, M. U., Altarabichi, M. G., Begum, S., Ginsberg, F., Glaes, R., Östgren, M., . . . Sorensen, M. (2019). A vision-based indoor navigation system for individuals with visual impairment. International Journal of Artificial Intelligence, 17(2), 188-201
Open this publication in new window or tab >>A vision-based indoor navigation system for individuals with visual impairment
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2019 (English)In: International Journal of Artificial Intelligence, ISSN 0974-0635, E-ISSN 0974-0635, Vol. 17, no 2, p. 188-201Article in journal (Refereed) Published
Abstract [en]

Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.

Place, publisher, year, edition, pages
CESER Publications, 2019
Keywords
Deep learning, Depth estimation, Indoor navigation, Object detection, Object recognition
National Category
Robotics Computer Sciences Computer Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mdh:diva-45835 (URN)2-s2.0-85073347243 (Scopus ID)
Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2020-03-19Bibliographically approved
Barua, S., Ahmed, M. U., Ahlström, C. & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert systems with applications, 115, 121-135
Open this publication in new window or tab >>Automatic driver sleepiness detection using EEG, EOG and contextual information
2019 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed) Published
Abstract [en]

The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Contextual information, Driver sleepiness, Electroencephalography, Electrooculography, Machine learning, Accidents, Case based reasoning, Decision trees, Electrophysiology, Fisher information matrix, Learning systems, Nearest neighbor search, Support vector machines, 10-fold cross-validation, Binary classification, Classification accuracy, Individual Differences, Multi-class classification, Physiological features, Classification (of information)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40526 (URN)10.1016/j.eswa.2018.07.054 (DOI)000448097700009 ()2-s2.0-85051410923 (Scopus ID)
Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2019-01-10Bibliographically approved
Ahmed, M. U., Boubezoul, A., Forsström, N. G., Sherif, N., Stenekap, D., Espie, S., . . . Södergren, R. (2019). Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning. In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019: . Paper presented at First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain. Barcelona, Spain
Open this publication in new window or tab >>Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning
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2019 (English)In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, Barcelona, Spain, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Analyzing powered two-wheeler rider behavior, i.e. classification of riding patterns based on 3-D accelerometer/gyroscope sensors mounted on motorcycles is challenging. This paper presents machine learning approach to classify four different riding events performed by powered two wheeler riders’ as a step towards increasing traffic safety. Three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used to classify riding patterns. The classification is conducted based on features extracted in time and frequency domains from accelerometer/gyroscope sensors signals. A comparison result between different filter frequencies, window sizes, features sets, as well as machine learning algorithms is presented. According to the results, the Random Forest method performs most consistently through the different data sets and scores best.

Place, publisher, year, edition, pages
Barcelona, Spain: , 2019
Keywords
machine learning, Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), powered two-wheeler, classification of riding patterns, accelerometer/gyroscope.
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-43909 (URN)
Conference
First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3802-4721

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