| 1. |
- Barua, Shaibal, et al.
(författare)
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Automated EEG Artifact Handling with Application in Driver Monitoring
- 2017
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers Inc.. - 2168-2194 .- 2168-2208.
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Tidskriftsartikel (refereegranskat)abstract
- Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
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| 2. |
- Barua, Shaibal, et al.
(författare)
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Automatic driver sleepiness detection using EEG, EOG and contextual information
- 2019
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Ingår i: Expert systems with applications. - : Elsevier Ltd. - 0957-4174 .- 1873-6793. ; 115, s. 121-135
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Nilsson, Emma, et al.
(författare)
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Vehicle Driver Monitoring : sleepiness and cognitive load
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- To prevent road crashes, it is important to understand driver related contributing factors. The overall aim of the Vehicle Driver Monitoring project was to advance the understanding of two such factors; sleepiness and cognitive distraction. The project aimed at finding methods to measure the two states, with focus on physiological measures, and to study their effect on driver behaviour. The data collection was done in several laboratory and driving simulator experiments. Much new knowledge and insights were gained in the project. Significant effects of cognitive load as well as of sleepiness were found in several physiological measures. The results also showed that context, including individual and environmental factors, has a great impact on driver behaviours, measures and driver experiences.
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| 4. |
- Ahlström, Christer, 1977-, et al.
(författare)
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A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data
- 2017
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 18:11, s. 2929-2938
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Tidskriftsartikel (refereegranskat)abstract
- Indicators based on visual time-sharing have been used to investigate drivers' visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicle's coordinate system, but also for environmental and traffic targets that move independently of the driver's vehicle.
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| 5. |
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| 6. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Alcohol impairs driver attention and prevents compensatory strategies
- 2023
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Ingår i: Accident Analysis and Prevention. - : Elsevier. - 0001-4575 .- 1879-2057. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- While the negative effects of alcohol on driving performance are undisputed, it is unclear how driver attention, eye movements and visual information sampling are affected by alcohol consumption. A simulator study with 35 participants was conducted to investigate whether and how a driver's level of attention is related to self-paced non-driving related task (NDRT)-engagement and tactical aspects of undesirable driver behaviour under increasing levels of breath alcohol concentration (BrAC) up to 1.0 ‰. Increasing BrAC levels lead to more frequent speeding, short time headways and weaving, and higher NDRT engagement. Instantaneous distraction events become more frequent, with more and longer glances to the NDRT, and a general decline in visual attention to the forward roadway. With alcohol, the compensatory behaviour that is typically seen when drivers engage in NDRTs did not appear. These findings support the theory that alcohol reduces the ability to shift attention between multiple tasks. To conclude, the independent reduction in safety margins in combination with impaired attention and an increased willingness to engage in NDRTs is likely the reason behind increased crash risk when driving under the influence of alcohol. © 2023
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| 7. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Assessment of Suspected Aortic Stenosis by Auto Mutual Information Analysis of Murmurs
- 2007
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Ingår i: Engineering in Medicine and Biology Society, 2007. EMBS 2007. - 9781424407873 ; , s. 1945-1948
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Konferensbidrag (refereegranskat)abstract
- Mild sclerotic thickening of the aortic valve affects 25% of the population, and the condition causes aortic valve stenosis (AS) in 2% of adults above 65 years. Echocardiography is today the clinical standard for assessing AS. However, a cost effective and uncomplicated technique that can be used for decision support in the primary health care would be of great value. In this study, recorded phonocardiographic signals were analyzed using the first local minimum of the auto mutual information (AMI) function. The AMI method measures the complexity in the sound signal, which is related to the amount of turbulence in the blood flow and thus to the severity of the stenosis. Two previously developed phonocardiographic methods for assessing AS severity were used for comparison, the murmur energy ratio and the sound spectral averaging technique. Twenty-nine patients with suspected AS were examined with Doppler echocardiography. The aortic jet velocity was used as a reference of AS severity, and it was found to correlate with the AMI method, the murmur energy ratio and the sound spectral averaging technique with the correlation coefficient R = 0.82, R = 0.73 and R = 0.76, respectively.
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| 8. |
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| 9. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Distinguishing Innocent Murmurs from Murmurs caused by Aortic Stenosis by Recurrence Quantification Analysis
- 2006
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Ingår i: ROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 18. - Canakkale, Turkey : World Academy of Science, Engineering and Technology (W A S E T). ; , s. 40-45
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Konferensbidrag (refereegranskat)abstract
- It is sometimes difficult to differentiate between innocent murmurs and pathological murmurs during auscultation. In these difficult cases, an intelligent stethoscope with decision support abilities would be of great value. In this study, using a dog model, phonocardiographic recordings were obtained from 27 boxer dogs with various degrees of aortic stenosis (AS) severity. As a reference for severity assessment, continuous wave Doppler was used. The data were analyzed with recurrence quantification analysis (RQA) with the aim to find features able to distinguish innocent murmurs from murmurs caused by AS. Four out of eight investigated RQA features showed significant differences between innocent murmurs and pathological murmurs. Using a plain linear discriminant analysis classifier, the best pair of features (recurrence rate and entropy) resulted in a sensitivity of 90% and a specificity of 88%. In conclusion, RQA provide valid features which can be used for differentiation between innocent murmurs and murmurs caused by AS.
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| 10. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Effects of the road environment on the development of driver sleepiness in young male drivers
- 2018
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Ingår i: Accident Analysis and Prevention. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0001-4575 .- 1879-2057. ; 112, s. 127-134
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Tidskriftsartikel (refereegranskat)abstract
- Latent driver sleepiness may in some cases be masked by for example social interaction, stress and physical activity. This short-term modulation of sleepiness may also result from environmental factors, such as when driving in stimulating environments. The aim of this study is to compare two road environments and investigate how they affect driver sleepiness. Thirty young male drivers participated in a driving simulator experiment where they drove two scenarios: a rural environment with winding roads and low traffic density, and a suburban road with higher traffic density and a more built-up roadside environment. The driving task was essentially the same in both scenarios, i.e. to stay on the road, without much interaction with other road users. A 2 x 2 design, with the conditions rural versus suburban, and daytime (full sleep) versus night-time (sleep deprived), was used. The results show that there were only minor effects of the road environment on subjective and physiological indicators of sleepiness. In contrast, there was an increase in subjective sleepiness, longer blink durations and increased EEG alpha content, both due to time on task and to night-time driving. The two road environments differed both in terms of the demand on driver action and of visual load, and the results indicate that action demand is the more important of the two factors. The notion that driver fatigue should be countered in a more stimulating visual environment such as in the city is thus more likely due to increased task demand rather than to a richer visual scenery. This should be investigated in further studies.
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