| 1. |
- Degas, A., et al.
(author)
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
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In: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Research review (peer-reviewed)abstract
- Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(author)
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A vision-based indoor navigation system for individuals with visual impairment
- 2019
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In: International Journal of Artificial Intelligence. - : CESER Publications. - 0974-0635. ; 17:2, s. 188-201
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Journal article (peer-reviewed)abstract
- 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.
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| 3. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(author)
-
Machine learning for cognitive load classification : A case study on contact-free approach
- 2020
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In: IFIP Advances in Information and Communication Technology. - Cham : Springer. - 9783030491604 ; , s. 31-42
-
Conference paper (peer-reviewed)abstract
- The most common ways of measuring Cognitive Load (CL) is using physiological sensor signals e.g., Electroencephalography (EEG), or Electrocardiogram (ECG). However, these signals are problematic in situations e.g., in dynamic moving environments where the user cannot relax with all the sensors attached to the body and it provides significant noises in the signals. This paper presents a case study using a contact-free approach for CL classification based on Heart Rate Variability (HRV) collected from ECG signal. Here, a contact-free approach i.e., a camera-based system is compared with a contact-based approach i.e., Shimmer GSR+ system in detecting CL. To classify CL, two different Machine Learning (ML) algorithms, mainly, Support Vector Machine (SVM) and k-Nearest-Neighbor (k-NN) have been applied. Based on the gathered Inter-Beat-Interval (IBI) values from both the systems, 13 different HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for binary and combined class classifications. According to the results, the highest average classification accuracy was achieved as 84% on the binary classification i.e. S0 vs S2 using k-NN. The highest F1 score was achieved 88% using SVM for the combined class considering S0 vs (S1 and S2) for contact-free approach i.e. the camera system. Thus, all the ML algorithms achieved a higher classification accuracy while considering the contact-free approach than contact-based approach. © IFIP International Federation for Information Processing 2020.
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| 4. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(author)
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Quality index analysis on camera- A sed R-eak identification considering movements and light illumination
- 2018
-
In: Studies in Health Technology and Informatics, vol 249. - : IOS Press. - 9781614998679 ; , s. 84-92
-
Conference paper (peer-reviewed)abstract
- This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.
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| 5. |
- Andersson, Alf, et al.
(author)
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Inline Process Control – a concept study of efficient in-line process control and process adjustment with respect to product geometry
- 2016
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In: Swedish Production Symposium 2016 SPS 2016. - Lund, Sweden.
-
Conference paper (peer-reviewed)abstract
- All manufacturing processes have variation which may violate the fulfillment of assembly, functional, geometrical or esthetical requirements and difficulties to reach desired form in all areas. The cost for geometry defects rises downstream in the process chain. Therefore, it is vital to discover these defects as soon as they appear. Then adjustments can be done in the process without losing products or time. In order to find a solution for this, a project with the overall scope “development of an intelligent process control system” has been initiated. This project consists of five different work packages: Inline measurement, Process Evaluation, Corrective actions, Flexible tooling and demonstrator cell. These work packages address different areas which are necessary to fulfill the overall scope of the project. The system shall both be able to detect geometrical defects, propose adjustments and adjust simple process parameters. The results are demonstrated in a demo cell located at Chalmers University of Technology. In the demonstrator all the different areas have been verified in an industrial case study – assembly of GOR Volvo S80. Efficient offline programming for robot based measurement, efficient process evaluation based on case base reasoning (CBR) methodology, flexible fixtures and process adjustments based on corrective actions regarding in going part positioning.
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| 6. |
- Rahman, Hamidur, et al.
(author)
-
A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals
- 2016
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Conference paper (peer-reviewed)abstract
- This paper presents a case-based classification system for alcohol detection using physiological parameters. Here, four physiological parameters e.g. Heart Rate Variability (HRV), Respiration Rate (RR), Finger Temperature (FT), and Skin Conductance (SC) are used in a Case-based reasoning (CBR) system to detect alcoholic state. In this study, the participants are classified into two groups as drunk or sober. The experimental work shows that using the CBR classification approach the obtained accuracy for individual physiological parameters e.g., HRV is 85%, RR is 81%, FT is 95% and SC is 86%. On the other hand, the achieved accuracy is 88% while combining the four parameters i.e., HRV, RR, FT and SC using the CBR system. So, the evaluation illustrates that the CBR system based on physiological sensor signal can classify alcohol state accurately when a person is under influence of at least 0.2 g/l of alcohol.
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| 7. |
- Rahman, Hamidur, 1984-
(author)
-
An Intelligent Non-Contact based Approach for Monitoring Driver’s Cognitive Load
- 2018
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Licentiate thesis (other academic/artistic)abstract
- The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations. So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters.The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc.
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| 8. |
- Rahman, Hamidur, et al.
(author)
-
Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
- 2022
-
In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 133001-133015
-
Research review (peer-reviewed)abstract
- For the last few years, cases of applying articial intelligence (AI) to engineering activitiestowards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematicallyreach higher and productivity levels, owing to its holistic perspective and consideration of economic andenvironmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE)we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AItechniques, (2) the current AI-improved LCE subelds and (3) the subelds with highly enhanced by AI.A specic set of inclusion and exclusion criteria were used to identify and select academic papers fromseveral elds, i.e. production, logistics, marketing and supply chain and after the selection process describedin the paper we ended up with 42 scientic papers. The study and analysis show that there are manyAI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, andInfrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall,the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance andRepair are the top explored LCE subelds whereas logistics and Procurement are the least explored subareas.Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strongresearch funding and focus on Industry 4.0; Germany is standing out with numbers of publications. Thein-depth analysis of selected and relevant scientic papers are helpful in getting a more correct picture ofthe area which enables a more systematic approach to AI-LCE in the future.
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| 9. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
-
Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
- 2022
-
In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 10, s. 133001-133015
-
Research review (peer-reviewed)abstract
- For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.
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| 10. |
- Rahman, Hamidur, Doctoral Student, 1984-
(author)
-
Artificial Intelligence for Non-Contact-Based Driver Health Monitoring
- 2021
-
Doctoral thesis (other academic/artistic)abstract
- In clinical situations, a patient’s physical state is often monitored by sensors attached to the patient, and medical staff are alerted if the patient’s status changes in an undesirable or life-threatening direction. However, in unsupervised situations, such as when driving a vehicle, connecting sensors to the driver is often troublesome and wired sensors may not produce sufficient quality due to factors such as movement and electrical disturbance. Using a camera as a non-contact sensor to extract physiological parameters based on video images offers a new paradigm for monitoring a driver’s health and mental state. Due to the advanced technical features in modern vehicles, driving is now faster, safer and more comfortable than before. To enhance transport security (i.e. to avoid unexpected traffic accidents), it is necessary to consider a vehicle driver as a part of the traffic environment and thus monitor the driver’s health and mental state. Such a monitoring system is commonly developed based on two approaches: driving behaviour-based and physiological parameters-based.This research work demonstrates a non-contact approach that classifies a driver’s cognitive load based on physiological parameters through a camera system and vehicular data collected from control area networks considering image processing, computer vision, machine learning (ML) and deep learning (DL). In this research, a camera is used as a non-contact sensor and pervasive approach for measuring and monitoring the physiological parameters. The contribution of this research study is four-fold: 1) Feature extraction approach to extract physiological parameters (i.e. heart rate [HR], respiration rate [RR], inter-beat interval [IBI], heart rate variability [HRV] and oxygen saturation [SpO2]) using a camera system in several challenging conditions (i.e. illumination, motion, vibration and movement); 2) Feature extraction based on eye-movement parameters (i.e. saccade and fixation); 3) Identification of key vehicular parameters and extraction of useful features from lateral speed (SP), steering wheel angle (SWA), steering wheel reversal rate (SWRR), steering wheel torque (SWT), yaw rate (YR), lanex (LAN) and lateral position (LP); 4) Investigation of ML and DL algorithms for a driver’s cognitive load classification. Here, ML algorithms (i.e. logistic regression [LR], linear discriminant analysis [LDA], support vector machine [SVM], neural networks [NN], k-nearest neighbours [k-NN], decision tree [DT]) and DL algorithms (i.e. convolutional neural networks [CNN], long short-term memory [LSTM] networks and autoencoders [AE]) are used. One of the major contributions of this research work is that physiological parameters were extracted using a camera. According to the results, feature extraction based on physiological parameters using a camera achieved the highest correlation coefficient of .96 for both HR and SpO2 compared to a reference system. The Bland Altman plots showed 95% agreement considering the correlation between the camera and the reference wired sensors. For IBI, the achieved quality index was 97.5% considering a 100 ms R-peak error. The correlation coefficients for 13 eye-movement features between non-contact approach and reference eye-tracking system ranged from .82 to .95.For cognitive load classification using both the physiological and vehicular parameters, two separate studies were conducted: Study 1 with the 1-back task and Study 2 with the 2-back task. Finally, the highest average accuracy achieved in terms of cognitive load classification was 94% for Study 1 and 82% for Study 2 using LR algorithms considering the HRV parameter. The highest average classification accuracy of cognitive load was 92% using SVM considering saccade and fixation parameters. In both cases, k-fold cross-validation was used for the validation, where the value of k was 10. The classification accuracies using CNN, LSTM and autoencoder were 91%, 90%, and 90.3%, respectively. This research study shows such a non-contact-based approach using ML, DL, image processing and computer vision is suitable for monitoring a driver’s cognitive state.
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| 11. |
- Rahman, Hamidur, et al.
(author)
-
Deep Learning based Person Identification using Facial Images
- 2018
-
In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225. - Cham : Springer International Publishing. - 9783319762128 ; , s. 111-115
-
Conference paper (peer-reviewed)abstract
- Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.
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| 12. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
-
Deep Learning in Remote Sensing : An Application to Detect Snow and Water in Construction Sites
- 2021
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In: Proceedings - 2021 4th International Conference on Artificial Intelligence for Industries, AI4I 2021. - 9781665434102 ; , s. 52-56
-
Conference paper (peer-reviewed)abstract
- It is important for a construction and property development company to know weather conditions in their daily operation. In this paper, a deep learning-based approach is investigated to detect snow and rain conditions in construction sites using drone imagery. A Convolutional Neural Network (CNN) is developed for the feature extraction and performing classification on those features using machine learning (ML) algorithms. Well-known existing deep learning algorithms AlexNet and VGG16 models are also deployed and tested on the dataset. Results show that smaller CNN architecture with three convolutional layers was sufficient at extracting relevant features to the classification task at hand compared to the larger state-of-the-art architectures. The proposed model reached a top accuracy of 97.3% in binary classification and 96.5% while also taking rain conditions into consideration. It was also found that ML algorithms,i.e., support vector machine (SVM), logistic regression and k-nearest neighbors could be used as classifiers using feature maps extracted from CNNs and a top accuracy of 90% was obtained using SVM algorithms.
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| 13. |
- Rahman, Hamidur, et al.
(author)
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Driver monitoring in the context of autonomous vehicle
- 2015
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In: Frontiers in Artificial Intelligence and Applications. - Amsterdam. - 9781614995883 ; , s. 108-117
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Conference paper (peer-reviewed)abstract
- Today research is going on within different essential functions need to bring automatic vehicles to the roads. However, there will be manual driven vehicles for many years before it is fully automated vehicles on roads. In complex situations, automated vehicles will need human assistance for long time. So, for road safety driver monitoring is even more important in the context of autonomous vehicle to keep the driver alert and awake. But, limited effort has been done in total integration between automatic vehicle and human driver. Therefore, human drivers need to be monitored and able to take over control within short notice. This papers provides an overview on autonomous vehicles and un-obstructive driver monitoring approaches that can be implemented in future autonomous vehicles to monitor driver e.g., to diagnose and predict stress, fatigue etc. in semi-automated vehicles.
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| 14. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
-
Driver’s Cognitive Load Classification based on Eye Movement through Facial Image using Machine Learning
-
Other publication (other academic/artistic)abstract
- The driver's cognitive load is considered a good indication if the driver is alert or distracted but determing cognitive load is challenging and the acceptance of wire sensor solutions like EEG and ECG are not not preferred in real-world driving scenario. The recent development of image processing, machine learning, and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. Two different wireless sensor systems, one commercial giving eye position (SmartEye) and one Microsoft LifeCam Studio with resolution 1920 x 1080 were used for data collection. In this paper, two eye movement parameters, saccade, and fixation are investigated through facial images and 13 features are manually extracted. Five machine learning algorithms, support vector machine (SVM), logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), and decision tree (DT), are investigated to classify the cognitive load. According to the results, the SVM model with linear kernel function outperforms the other four classification methods. Here, the achieved average accuracy is 92% using SVM. Again, three deep learning architectures, convolutional neural networks (CNN), long short-term memory (LSTM), and autoencoder (AE) are designed both for automatic feature extraction and cognitive load classification. The results show that CNN architecture achieves the highest classification accuracy which is 91%. Besides, the classification accuracy for the extracted eye movement parameters is compared with reference eye tracker signals. It is observed that the classification accuracies between the eye tracker and the camera are very similar.
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| 15. |
- Rahman, Hamidur, et al.
(author)
-
Falling Angel - a Wrist Worn Fall Detection System Using K-NN Algorithm
- 2016
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Conference paper (peer-reviewed)abstract
- A wrist worn fall detection system has been developed where the accelerometer data from an angel sensor is analyzed by a two-layered algorithm in an android phone. Here, the first layer uses a threshold to find potential falls and if the thresholds are met, then in the second layer a machine learning i.e., k-Nearest Neighbor (k-NN) algorithm analyses the data to differentiate it from Activities of Daily Living (ADL) in order to filter out false positives. The final result of this project using the k-NN algorithm provides a classification sensitivity of 96.4%. Here, the acquired sensitivity is 88.1% for the fall detection and the specificity for ADL is 98.1%.
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| 16. |
- Rahman, Hamidur, et al.
(author)
-
Ins and Outs of Big Data : A Review
- 2016
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Conference paper (peer-reviewed)abstract
- Today with the fast development of digital technologies and advance communications a gigantic amount of data sets with massive and complex structures called ‘Big data’ is being produced everyday enormously and exponentially. Again, the arrival of social media, advent of smart homes, offices and hospitals are connected as Internet of Things (IoT), this influence also a lot to Big data. According to the study, Big data presents data sets with large magnitude including structured, semi-structured or unstructured data. The study also presents the new technologies for data analyzing, collecting, fast searching, proper sharing, exact storing, speedy transferring, hidden pattern visualization and violations of privacy etc. This paper presents an overview of ins and outs of Big Data where the content, scope, samples, methods, advantages, challenges and privacy of Big data have been discussed. The goal of this article is to provide big data knowledge to the research community for the sake of its many real life applications such as traffic management, driver monitoring, health care in hospitals, meteorology and so on.
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| 17. |
- Rahman, Hamidur, et al.
(author)
-
Intelligent Driver Monitoring Based on Physiological Sensor Signals : Application Using Camera
- 2015
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In: IEEE 18th International Conference on Intelligent Transportation Systems ITSC2015. - Canary Islands, Spain. ; , s. 2637-2642
-
Conference paper (peer-reviewed)abstract
- Recently, there has been increasing interest in low-cost, non-contact and pervasive methods for monitoring physiological information for the drivers. For the intelligent driver monitoring system there has been so many approaches like facial expression based method, driving behavior based method and physiological parameters based method. Physiological parameters such as, heart rate (HR), heart rate variability (HRV), respiration rate (RR) etc. are mainly used to monitor physical and mental state. Also, in recent decades, there has been increasing interest in low-cost, non-contact and pervasive methods for measuring physiological information. Monitoring physiological parameters based on camera images is such kind of expected methods that could offer a new paradigm for driver’s health monitoring. In this paper, we review the latest developments in using camera images for non-contact physiological parameters that provides a resource for researchers and developers working in the area.
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| 18. |
- Rahman, Hamidur, et al.
(author)
-
Non-contact-based driver's cognitive load classification using physiological and vehicular parameters
- 2020
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In: Biomedical Signal Processing and Control. - : ELSEVIER SCI LTD. - 1746-8094 .- 1746-8108. ; 55
-
Journal article (peer-reviewed)abstract
- 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.
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| 19. |
- Rahman, Hamidur, et al.
(author)
-
Non-contact heart rate monitoring using lab color space
- 2016
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In: Studies in Health Technology and Informatics. - 9781614996521 ; , s. 46-53
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Conference paper (peer-reviewed)abstract
- Research progressing during the last decade focuses more on non-contact based systems to monitor Heart Rate (HR) which are simple, low-cost and comfortable to use. Most of the non-contact based systems are using RGB videos which is suitable for lab environment. However, it needs to progress considerably before they can be applied in real life applications. As luminance (light) has significance contribution on RGB videos HR monitoring using RGB videos are not efficient enough in real life applications in outdoor environment. This paper presents a HR monitoring method using Lab color facial video captured by a webcam of a laptop computer. Lab color space is device independent and HR can be extracted through facial skin color variation caused by blood circulation considering variable environmental light. Here, three different signal processing methods i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and blood volume pulse (BVP) has been extracted from the facial regions. In this study, HR is subsequently quantified and compare with a reference measurement. The result shows that high degrees of accuracy have been achieved compared to the reference measurements. Thus, this technology has significant potential for advancing personal health care, telemedicine and many real life applications such as driver monitoring.
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| 20. |
- Rahman, Hamidur, et al.
(author)
-
Non-Contact Physiological Parameters Extraction Using Camera
- 2016
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In: Internet of Things. IoT Infrastructures. - Cham : Springer International Publishing. - 9783319470627 ; , s. 448-453
-
Conference paper (peer-reviewed)abstract
- Physiological parameters such as Heart Rate (HR), Beat-to-Beat Interval (IBI) and Respiration Rate (RR) are vital indicators of people’s physiological state and important to monitor. However, most of the measurements methods are connection based, i.e. sensors are connected to the body which is often complicated and requires personal assistance. This paper proposed a simple, low-cost and non-contact approach for measuring multiple physiological parameters using a web camera in real time. Here, the heart rate and respiration rate are obtained through facial skin colour variation caused by body blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), independent component analysis (ICA) and Principal component analysis (PCA) have been applied on the colour channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR, IBI and RR are subsequently quantified and compared to corresponding reference measurements. High degrees of agreement are achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.
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| 21. |
- Rahman, Hamidur, et al.
(author)
-
Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration
- 2020
-
In: IEEE Transactions on Biomedical Engineering. - : IEEE Computer Society. - 0018-9294 .- 1558-2531. ; 67:1, s. 88-98
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Journal article (peer-reviewed)abstract
- 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.
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| 22. |
- Rahman, Hamidur, et al.
(author)
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Real Time Heart Rate Monitoring from Facial RGB Color Video using Webcam
- 2016
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In: The 29th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2016. - Malmö, Sweden.
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Conference paper (peer-reviewed)abstract
- Heart Rate (HR) is one of the most important Physiological parameter and a vital indicator of people’s physiological state and is therefore important to monitor. Monitoring of HR often involves high costs and complex application of sensors and sensor systems. Research progressing during last decade focuses more on noncontact based systems which are simple, low-cost and comfortable to use. Still most of the noncontact based systems are fit for lab environments in offline situation but needs to progress considerably before they can be applied in real time applications. This paper presents a real time HR monitoring method using a webcam of a laptop computer. The heart rate is obtained through facial skin color variation caused by blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR is subsequently quantified and compared to corresponding reference measurements. The obtained results show that there is a high degrees of agreement between the proposed experiments and reference measurements. This technology has significant potential for advancing personal health care and telemedicine. Further improvements of the proposed algorithm considering environmental illumination and movement can be very useful in many real time applications such as driver monitoring.
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| 23. |
- Rahman, Hamidur, et al.
(author)
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SmartMirror : An Embedded Non-contact System for Health Monitoring at Home
- 2016
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Conference paper (peer-reviewed)abstract
- The ‘Smart Mirror’ project introduces non-contact based technological innovations at our homes where its usage can be as ubiquitous as ‘looking at a mirror’ while providing critical actionable insights thereby leading to improved care and outcomes. The key objectives is to detect key physiological markers like Heart Rate (HR), Respiration Rate (RR), Inter-beat-interval (IBI) and Blood Pressure (BP) and also drowsiness using the video input of the individual standing in front of the mirror and display the results in real-time. A satisfactory level of accuracy has been attained with respect to the reference sensors signal.
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| 24. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
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Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
- 2021
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In: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Journal article (peer-reviewed)abstract
- Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.
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| 25. |
- Rahman, Hamidur, et al.
(author)
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Vision-Based Remote Heart Rate Variability Monitoring using Camera
- 2018
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In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225. - Cham : Springer International Publishing. - 9783319762128 ; , s. 10-18
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Conference paper (peer-reviewed)abstract
- Heart Rate Variability (HRV) is one of the important physiological parameter which is used to early detect many fatal disease. In this paper a non-contact remote Heart Rate Variability (HRV) monitoring system is developed using the facial video based on color variation of facial skin caused by cardiac pulse. The lab color space of the facial video is used to extract color values of skin and signal processing algorithms i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA), Principle Component Analysis (PCA) are applied to monitor HRV. First, R peak is detected from the color variation of skin and then Inter-Beat-Interval (IBI) is calculated for every consecutive R-R peak. HRV features are then calculated based on IBI both in time and frequency domain. MySQL and PHP programming language is used to store, monitor and display HRV parameters remotely. In this study, HRV is quantified and compared with a reference measurement where a high degree of similarities is achieved. This technology has significant potential for advancing personal health care especially for telemedicine.
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| 26. |
- Tomasic, Ivan, et al.
(author)
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Input-output Mapping and Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Processes
- 2016
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In: Swedish Production Symposium 2016 SPS 2016.
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Conference paper (peer-reviewed)abstract
- The assembly quality is affected by various factors within which fixture variations are the most important. For that reason an extensive research on fixture variations has already been done. In this work we propose a linear models (LMs) application for the purpose of analyzing sources of variation in the fixture as well as establishing a model between positions of ingoing parts and measured geometrical characteristics of the assemblies. Objectives: (1) To estimate the strengths of different sources of variation on the assembled parts. (2) Estimate a regression model between the positions of ingoing parts as inputs (that are defined by positions of pins holding them), and measured geometrical characteristics as outputs, that can be used to determine which measured characteristics are influenced by which input variable. Methods: The data was experimentally collected in a laboratory environment by intentionally changing positions of ingoing part, assembling the parts and subsequently measuring their geometrical characteristics. We use liner model to establish the relation between geometrical characteristics measured on the assembled parts, and the input variables of interest. Results: Presented is a modeling technique that can be used to establish which measured geometrical characteristics are influenced by input variables (i.e.pins’ positions) of interest. The natural variation in the system (i.e. not modeled variation) is quite high. The time passed between measurements has a significant influence on measured values.
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| 27. |
- Tomasic, Ivan, et al.
(author)
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Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process
- 2016
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In: Swedish Production Symposium 2016 SPS 2016.
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Conference paper (peer-reviewed)abstract
- The assembly quality is affected by various factors within which fixture variations are the most important. For that reason significant research on fixture variations has already been done. In this work we propose a linear mixed models (LMMs) application for the purpose of analyzing sources of variation in the fixture Objective: To estimate the strength of influences of different sources of variation on the control and assembly fixtures. The variables considered are: time, operator, default pin positions, shifts from the default pin positions . Methods: The data was collected through assembly and measurement for repeatability and experimental corrective actions. We use LMMs to model the relation between features measured on the assembled parts and the input variables of interest. The LMMs allow taking into account the correlation of observations contained in the dataset. We also use graphical data presentation methods to explore the data. Results: The expected results are the strengths of influences of the individual variables considered, and the pairwise interactions of between the variables, on the assembled parts variations.
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