Professional truck drivers are highly exposed to fatigue and work related injuries. Truck drivers are common victims of musculoskeletal disorders, frequently suffering from pain symptoms particularly in the neck, shoulder and lower back. This situation is believed to be a contributor to the high absenteeism in this job category. A high percentage of this problem is due to the adoption of an unhealthy driving posture resulting from inappropriate seat design. This incorrect and poor design is owing to the insufficient and obsolete anthropometrical data which has been used for decades for arranging and positioning components in the driver environment. The main objective of the present study was to create and construct a mathematical model which clarifies and predicts the drivers’ comfortable sitting posture and position. It was hypothesized that the length and height characteristics of some body segments as well as the body weight and waist circumference of the driver have a great impact on the selection of a specific sitting posture. The steering wheel positions as well as the pedal/floor locations were hypothesized to be highly correlated to the driver’s selected posture and the corresponding comfort. The effect of the seat position on posture selection and related comfort assessments constituted the other hypothesis of the study which received extra attention. A laboratory experiment on a Scania truck cab mock-up was conducted. The seat track travel along a vertical as well as horizontal forward-backward path was obtained by mounting the seat on the motorized rigid frame which allowed unrestricted vertical and fore-aft travel. The seat cushion angle and backrest angle were adjusted by pivoting the entire seat and backrest around a lateral axis and independently. The pedal components were mounted on a motorized platform, thus allowing unrestricted fore-aft and height travel without any changes in the pedal angles. The steering wheel was mounted on the instrument panel by two independent pneumatic axes which allowed a wide range of adjustments including tilting and moving along the sagittal plane for adjusting the height and distance. The test plan called for 55 international highly experienced heavy truck drivers. The drivers were recruited to span a large range of body weight and stature, in particular to ensure adequate representation of both the extreme as well as the normal group of drivers. The drivers filled in a general information questionnaire before undergoing the anthropometrical measurements and thereafter the test trials. The experiment contained a subset of test conditions with five different trials using random selection sampling procedure. Drivers were asked to adjust the components in a wide range of trajectory according to a written protocol. A sparse set of threedimensional body landmark locations and the corresponding comfort assessments were recorded. As the main part of the result, the mathematical models using multiple regression analyses on selected body landmarks as well as anthropometrical measures were developed which proposed a linear correlation between parameters. The differences between the observed data and the corresponding predicted data using the model were found to be minimal and almost dispensable. Additionally, the drivers preferred to sit in the rearmost position and at a rather high level relative to the rest of the available and adjustable area. Considering the normal adjustable seat area of the cab, only a very small part of the observed Hpoint data lies within this area while a large remaining amount of data lies outside of it. Moreover, the difference between the observation (plotted H-point data) and the neutral H-point was found to be significant. Furthermore, and since some of the data lies almost on the border of the adjustable area, it may indicate a reasonable tendency for even more seat adjustment in the backward direction. A conceptual model consisting of four different parameters was developed and presented in the end. These parameters of the model suggest being as key factors which play a central role on process of decision making regarding the selection of a desirable sitting posture. Any eventual modifications and adjustments for elimination or minimizing discrepancies, biases or obscured factors affecting the quality of the mathematical model would be a case for future study. The investigation of a complete assessment of comfort should be supplemented with an analysis of how many truck drivers are satisfied with the comfort in the end. / QC 20100824

Localization is an important problem that must be resolved in order for a robot to make an estimation of its location based on observation and odometry updates. Relying on artificial landmarks such as the lines, circles, and goalposts in the robot soccer domain, current robot localization requires prior knowledge and suffers from uncertainty problems due to partial observation, and thus is less generalizable compared to human beings, who refer to their surroundings for complimentary information. To improve the certainty of the localization model, we propose a framework that recognizes orientation by actively using natural landmarks from the off-field surroundings, extracting these visual features from raw images. Our approach involves identifying visual features and natural landmarks, training with localization information to understand the surroundings, and prediction based on matching of features. This approach can increase the precision of robot orientation and improve localization accuracy by eliminating uncertain hypotheses, and in addition, it is also a general approach that can be extended and applied to other localization problems as well. / text

Thesis (M.A.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 65-67). Also available on the Internet.

Thesis (M.A.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 65-67). Also available on the Internet.

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Deception detection has always been of subject of interest. After all, determining if a person is telling the truth or not could be detrimental in many real-world cases. Current methods to discern deceptions require expensive equipment that need specialists to read and interpret them. In this article, we carry out an exhaustive comparison between 9 different facial landmark recognition based recurrent deep learning models trained on a recent man-made database used to determine lies, comparing them by accuracy and AUC. We also propose two new metrics that represent the validity of each prediction. The results of a 5-fold cross validation show that out of all the tested models, the Stacked GRU neural model has the highest AUC of.9853 and the highest accuracy of 93.69% between the trained models. Then, a comparison is done between other machine and deep learning methods and our proposed Stacked GRU architecture where the latter surpasses them in the AUC metric. These results indicate that we are not that far away from a future where deception detection could be accessible throughout computers or smart devices. / Revisión por pares

In-vehicle navigation systems (IVNS) have the potential to benefit older drivers, reducing stress associated with way-finding and providing on-trip support, especially in unfamiliar locations. However, existing IVNS present challenges to usability, resulting in lack of uptake and over-reliance on pre-trip planning. This paper presents research aimed at identifying features that make IVNS user-friendly and appropriate for older drivers. Studying navigational performance within a simulated driving environment, it focuses on the use of landmarks with route guidance information, and the most appropriate method of information provision (audio only, visual only or a combination of audio and visual). It also assesses potential gender differences that might arise with landmark-based navigational information. Solutions include use of appropriate roadside landmarks, and information delivered through a combination of audio and icon-based visual format. These features result in lower workload and fewer navigational errors. The audio/visual modality reduces the hazard of distraction by landmarks resulting in fewer visual glances and lower glance duration to the roadside compared to other modalities. Design and provision of IVNS tailored to older drivers’ needs can make a considerable contribution to maintaining individual mobility for longer.

Aspects of the behavioral ecology of bird song learning are examined in three parts. First, an approach from image analysis is extended to allow rapid, quantitative description of animal sounds. In this approach, sounds are summarized as sets of time-frequency-amplitude landmarks. Second, the role of dawn song in tree swallow (Tachycineta bicolor) breeding biology is examined. Song syllable sharing among tree swallows was found to be high among birds nesting at the same site, but sharing was lower between birds nesting at different sites. When birds nested at different sites, the distance between those sites was not related to the amount of difference between the birds' syllable repertoire compositions. All tree swallow song repertoires did not remain constant during the breeding season; some individuals added new syllable types, others modified existing types. Singing performance was correlated with reproductive success in tree swallows: males that sang more precise repetitions of their syllable types attracted more extra-pair mates. Furthermore, pairwise comparisons between the social and genetic fathers of extra-pair young found that the genetic fathers averaged higher syllable consistency than the cuckolded males. Third, a comparative study of the phylogenetic distribution of vocal mimicry examined the evolutionary history of song learning in oscine passerines. Vocal mimicry, defined as the habitual incorporation of heterospecific sounds into song displays, was found in twenty-eight separate clades of oscines. These clades were found in every major oscine superfamily, but made up a higher proportion of daughter groups within the most ancient superfamilies of oscines. The most plesiomorphic lineages of oscines were found to contain many highly-skilled mimics. These observations support the hypothesis that the course of song learning in oscines has run repeatedly from permissive learning rules that permit mimicry to restrictive learning rules that limit mimicry.

The studies reported in this thesis aim to provide insights on the process of navigation while driving. Driving requires processing and monitoring multiple tasks and sources of information. Navigation while driving increases the cognitive load of the driving task. Offloading the task of navigation to navigation aid systems such as GPS has potential disadvantages for our spatial memory skills. In this thesis, we introduce useful cues and skills to improve the performance of drivers in a variety of situations where they must navigate without the help of GPS. We used a motion simulator with six degrees of freedom to simulate various virtual reality driving scenarios that combine both visual and vestibular cues. In the following chapters, we report the effects of landmark cues, vestibular cues, self-reported mood disorders (e.g., depression, anxiety, and stress), individual differences at the visual spatial level (e.g., working memory span and mental rotation skills), age, and self-reported navigation skills on drivers’ route learning. We showed that successful navigation in various navigational situations depends on the type of landmarks available in the environment and the specific visual-spatial skills of drivers. We showed that vestibular self-motion cues improve egocentric route learning. Depression, anxiety, and stress affected drivers' route learning ability and dependency on GPS. We observed no deficit in age-related navigation performance when older drivers were able to use an egocentric frame of reference, however there was less optimal navigation performance of older drivers when wayfinding required an allocentric frame of reference. Overall, the application of the findings of this thesis may lead to an increase in efficacy and success in navigation performance and wayfinding while driving. / Thesis / Candidate in Philosophy / This thesis focuses on enhancing our understanding of wayfinding while driving in young and older adults. Using a driving simulator, we ran various virtual reality experiments to examine the underlying mechanisms of navigation while driving and ways to improve wayfinding of drivers. We identified useful cues for route learning in different environments where there were no navigation aid systems. We examined correlations between various spatial skills and performance that may improve drivers' wayfinding in unfamiliar environments. Furthermore, we assessed age-related effects on route learning and potential interventions to improve navigation in older drivers. The findings from the experiments reported in this thesis introduce the principle of route learning while driving in terms of how various internal and external factors can affect it. Drivers can incorporate these findings into their navigation tasks to overcome the wayfinding challenges that they encounter when driving in unfamiliar environments.

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