Data Augmentation for Generating Synthetic Electrogastrogram Time Series

Objective: To address an emerging need for large amount of diverse datasets for proper training of artificial intelligence (AI) algorithms and for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram (EGG) time series. Methods: We used EGG data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG alterations caused by the simulator sickness. Results: Proposed data augmentation method generates synthetic EGG with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in >70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. Conclusion: The code for generation of synthetic EGG time series is freely available and can be further customized to assess signal processing algorithms or to increase diversity in datasets used to train AI algorithms. The proposed approach is customized for EGG data synthesis, but can be easily utilized for other biosignals with similar nature such as electroencephalogram.Comment: three figures and two table

Who is willing to share their AV? Insights about gender differences among seven countries

The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services

Detection-response task—uses and limitations

The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. Response times and hit rates are interpreted as indicators of the attentional effect of cognitive load. The stimuli can be visual, tactile and auditory, and are chosen based on the type of in-vehicle system or device that is being evaluated. Its biggest disadvantage is that the method itself also affects the driver’s performance and secondary task completion times. Nevertheless, this is an easy to use and implement method, which allows relevant assessment and evaluation of in-vehicle systems. By following the recommendations and taking into account its limitations, researchers can obtain reliable and valuable results on the attentional effects of cognitive load on drivers

Detection-Response Task—Uses and Limitations

The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. Response times and hit rates are interpreted as indicators of the attentional effect of cognitive load. The stimuli can be visual, tactile and auditory, and are chosen based on the type of in-vehicle system or device that is being evaluated. Its biggest disadvantage is that the method itself also affects the driver’s performance and secondary task completion times. Nevertheless, this is an easy to use and implement method, which allows relevant assessment and evaluation of in-vehicle systems. By following the recommendations and taking into account its limitations, researchers can obtain reliable and valuable results on the attentional effects of cognitive load on drivers

Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients

We present a method to automatically calculate sensing time (ST) from the eye tracker data in subjects with neurological impairment using a driving simulator. ST presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directed their look to the children. In comparison to the commonly used reaction time, ST does not require additional neuro-muscular responses such as braking and presents unique information on the sensory function. From 108 neurological patients recruited for the study, the analysis of ST was performed in overall 56 patients to assess fit-, unfit-, and conditionally-fit-to-drive patients. The results showed that the proposed method based on the YOLO (You Only Look Once) object detector is efficient for computing STs from the eye tracker data in neurological patients. We obtained discriminative results for fit-to-drive patients by application of Tukey's Honest Significant Difference post hoc test (p < 0.01), while no difference was observed between conditionally-fit and unfit-to-drive groups (p = 0.542). Moreover, we show that time-to-collision (TTC), initial gaze distance (IGD) from pedestrians, and speed at the hazard onset did not influence the result, while the only significant interaction is among fitness, IGD, and TTC on ST. Although the proposed method can be applied to assess fitness to drive, we provide directions for future driving simulation-based evaluation and propose processing workflow to secure reliable ST calculation in other domains such as psychology, neuroscience, marketing, etc.Comment: 24 pages, 4 figures, 2 table

The Architectural Design of a System for Interpreting Multilingual Web Documents in E-speranto

E-speranto is a formal language for generating multilingual texts on the World Wide Web. It is currently still under development. The vocabulary and grammar rules of E-speranto are based on Esperanto; the syntax of E-speranto, however, is based on XML (eXtensible Markup Language). The latter enables the integration of documents generated in E-speranto into web pages. When a user accesses a web page generated in E-speranto, the interpreter interprets the document into a chosen natural language, which enables the user to read the document in any arbitrary language supported by the interpreter. The basic parts of the E-speranto interpreting system are the interpreters and information resources, which complies with the principle of separating the interpretation process from the data itself. The architecture of the E-speranto interpreter takes advantage of the resemblance between the languages belonging to the same linguistic group, which consequently results in a lower production cost of the interpreters for the same linguistic group. We designed a proof-of-concept implementation for interpreting E-speranto in three Slavic languages: Slovenian, Serbian and Russian. These languages share many common features in addition to having a similar syntax and vocabulary. The content of the information resources (vocabulary, lexicon) was limited to the extent that was needed to interpret the test documents. The testing confirmed the applicability of our concept and also indicated the guidelines for future development of both the interpreters and E-speranto itself