Level of service of pedestrian facilities: Modelling human comfort perception in the evaluation of pedestrian behaviour patterns

The paper presents a new methodology for evaluating the quality of operation of pedestrian facilities: the methodology is based on the individual level of comfort perceived by each pedestrian that moves in the area.At each time instant, each pedestrian perceives a comfort level which is a function of the space they feel currently available and of his required space. The required space depends on the subject’s walking direction as well as on physical and psychological factors. The available space depends on the current positions of pedestrians. The proposed methodology quantifies the current discomfort due to pedestrian interactions as a continuous function of the interpersonal distances.The proposed methodology has been applied to empirical data. The experimental data are presented, discussed and compared with widely accepted level of service assessment methods.     

Whole Body Coordination and Knee Movement Control During Five Rehabilitation Exercises

Knee rehabilitation exercises to improve motor control, target movement fluency, and displacement variability. Although knee movement in the frontal plane during exercise is routinely assessed in clinical practice, optimal knee control remains poorly understood. In this study, 29 healthy participants (height: 1.73 ± 0.11 m, mass: 73.5 ± 16.4 kg, age: 28.0 ± 6.9 years) performed 4 repetitions of 5 rehabilitation exercises while motion data were collected using the VICON PlugInGait full-body marker set. Fluency and displacement variability were calculated for multiple landmarks, including center of mass (CoM) and knee joint centers. Fluency was calculated as the inverse of the average number of times a landmark velocity in the frontal plane crossed zero. Variability was defined as the standard deviation of the frontal plane movement trajectories. CoM fluency and displacement variability were significantly different between tasks (p < .001). CoM displacement variability was consistently smallest compared to the constituent landmarks (p < .005). This was interpreted as a whole body strategy of compensatory variability constraining CoM frontal plane movement. Ipsilateral knee fluency (p < .01) and displacement variability (p < .001) differed substantially between tasks. The role of the weight-bearing knee seemed dependent on task constraints of the overall movement and balance, as well as constraints specific for knee joint stability.     

Route navigating without place recognition: what is recognised in recognition-triggered responses?

The use of landmark information in a route-navigation task has been investigated in a virtual environment. After learning a route, subjects were released at intermediate points along the route and asked to indicate the next movement direction required to continue the route. At each decision point, three landmarks were present, one of which was viewed centrally and two which appeared in the periphery of the visual field when approaching the decision point. In the test phase, landmarks could be replaced either within or across places. If all landmarks combined into a new place had been associated with the same movement direction during training, subjects performed as in the control condition. This indicates that they did not need to recognise places as configurations of landmarks. If, however, landmarks that had been associated with conflicting movement directions during training were combined, subjects' performance was reduced. We conclude that local views and objects are recognised individually and that the associated directions are combined in a voting scheme. No evidence was found for a recognition of places as panoramic views or configurations of objects.     

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in which these arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show that the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology.     

Evidence of hierarchies in cognitive maps

Previous research suggested that the apparent hierarchical organization of landmarks in an environment will influence subjects’ judgments about spatial characteristics of that environment. We extended this previous work to a natural environment that has no predetermined, well-defined hierarchical structure. Using an algorithm that generates a hierarchy of landmarks from recall protocols, we constructed hypothesized clusterings of landmarks for a set of subjects familiar with the space. Then we tested these hypothesized clusters in a series of tasks, all of which required judgments about distances in the space. The results of these tests suggest that subjects do cluster landmarks on the basis of nonspatial attributes, and that the clusters have consequences for performance in various tasks that require access to spatial information.     

Motion cues that make an impression: Predicting perceived personality by minimal motion information

The current study presents a methodology to analyze first impressions on the basis of minimal motion information. In order to test the applicability of the approach brief silent video clips of 40 speakers were presented to independent observers (i.e., did not know speakers) who rated them on measures of the Big Five personality traits. The body movements of the speakers were then captured by placing landmarks on the speakers' forehead, one shoulder and the hands. Analysis revealed that observers ascribe extraversion to variations in the speakers' overall activity, emotional stability to the movements' relative velocity, and variation in motion direction to openness. Although ratings of openness and conscientiousness were related to biographical data of the speakers (i.e., measures of career progress), measures of body motion failed to provide similar results. In conclusion, analysis of motion behavior might be done on the basis of a small set of landmarks that seem to capture important parts of relevant nonverbal information.     

Detecting eye movements in dynamic environments

To take advantage of the increasing number of in-vehicle devices, automobile drivers must divide their attention between primary (driving) and secondary (operating in-vehicle device) tasks. In dynamic environments such as driving, however, it is not easy to identify and quantify how a driver focuses on the various tasks he/she is simultaneously engaged in, including the distracting tasks. Measures derived from the driver’s scan path have been used as correlates of driver attention. This article presents a methodology for analyzing eye positions, which are discrete samples of a subject’s scan path, in order to categorize driver eye movements. Previous methods of analyzing eye positions recorded in a dynamic environment have relied completely on the manual identification of the focus of visual attention from a point of regard superimposed on a video of a recorded scene, failing to utilize information regarding movement structure in the raw recorded eye positions. Although effective, these methods are too time consuming to be easily used when the large data sets that would be required to identify subtle differences between drivers, under different road conditions, and with different levels of distraction are processed. The aim of the methods presented in this article are to extend the degree of automation in the processing of eye movement data by proposing a methodology for eye movement analysis that extends automated fixation identification to include smooth and saccadic movements. By identifying eye movements in the recorded eye positions, a method of reducing the analysis of scene video to a finite search space is presented. The implementation of a software tool for the eye movement analysis is described, including an example from an on-road test-driving sample.     

Postmodeling Sensitivity Analysis to Detect the Effect of Missing Data Mechanisms

Incomplete or missing data is a common problem in almost all areas of empirical research. It is well known that simple and ad hoc methods such as complete case analysis or mean imputation can lead to biased and/or inefficient estimates. The method of maximum likelihood works well; however, when the missing data mechanism is not one of missing completely at random (MCAR) or missing at random (MAR), it too can result in incorrect inference. Statistical tests for MCAR have been proposed, but these are restricted to a certain class of problems. The idea of sensitivity analysis as a means to detect the missing data mechanism has been proposed in the statistics literature in conjunction with selection models where conjointly the data and missing data mechanism are modeled. Our approach is different here in that we do not model the missing data mechanism but use the data at hand to examine the sensitivity of a given model to the missing data mechanism. Our methodology is meant to raise a flag for researchers when the assumptions of MCAR (or MAR) do not hold. To our knowledge, no specific proposal for sensitivity analysis has been set forth in the area of structural equation models (SEM). This article gives a specific method for performing postmodeling sensitivity analysis using a statistical test and graphs. A simulation study is performed to assess the methodology in the context of structural equation models. This study shows success of the method, especially when the sample size is 300 or more and the percentage of missing data is 20% or more. The method is also used to study a set of real data measuring physical and social self-concepts in 463 Nigerian adolescents using a factor analysis model.     

Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy

In order to limit the consequences of infantile cerebral palsy (ICP), physiotherapy should start as early as possible. This requires that infants at risk are detected at the earliest age possible. Today, diagnosis is based on visual observation by physicians and as such is influenced by subjective impressions. Objective methods, quantifying the pathological deviation from normal spontaneous motor activity would be preferable as they, for example, allow an inter- and intra-individual comparison of movement. In this paper we have developed a methodology that allows the 3-dimensional acquisition of unconstrained movement in newborn babies, using a motion analysis system. From the recorded movement data we have extracted 53 quantitative parameters that describe the differences between healthy and affected participants. Considered individually, each of these parameters does not permit a conclusive statement to be made as to whether or not the patient is at risk. Cluster analysis based on Euclidian distances therefore has been used to find an optimal combination of eight parameters. The optimal combination has been subsequently applied to organize the participants' movement into preferably homogeneous classes labelled "healthy" or "at risk". Classification was performed utilising quadratic discriminant analysis. The methodology presented allows a reliable discrimination between healthy and affected participants. Overall detection rate reached 73%. This value is expected to rise with increasing patient and norm collective database size.     

Spatial reconstruction of human motion by means of a single camera and a biomechanical model

The value of the results of the inverse dynamic analysis procedures used in the study of human tasks is dependent on the quality of the kinematic and kinetic data supplied to the biomechanical model that supports it. The kinematic data, containing the position, velocity and acceleration of all anatomical segments of the biomechanical model, result from the reconstruction of human spatial motion by means of the evaluation of the anatomic points positions that enable to uniquely define the position of all anatomical segments. Furthermore, the motion data must be kinematically consistent with the structure of the biomechanical model used in the analysis. The traditional photogrammetric methodologies used for the spatial reconstruction of the human motion require images of two or more calibrated and synchronized cameras. This is due to the fact that the projection of each anatomical point is described by two linear equations relating its three spatial coordinates with the two coordinates of the projected point. The need for the image of another camera arises from the fact that a third equation is necessary to find the original spatial position of the anatomical point. The methodology proposed here substitutes the projection equations of the second camera with the kinematic constraint equations associated with a biomechanical model in the motion reconstruction process. In the formulation the system of equations arising from the point projections and biomechanical model kinematic constraints, representing the constant length of the anatomical segments, are solved simultaneously. Because the system of equations has multiple solutions for each image, a strategy based on the minimization of a cost function associated to the smoothness of the reconstructed motion is devised. It is shown how the process is implemented computationally avoiding any operator intervention during the motion reconstruction for a given time period. This leads to an automated computer procedure that ensures the uniqueness of the reconstructed motion. The result of the reconstruction process is a set of data that is kinematically consistent with the biomechanical model used. Through applications of the proposed methodology to several sports exercises its benefits and shortcomings are discussed.     

Multi-template supervised descent method for face alignment

Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it learns a sequence of descent directions to minimize the difference between the estimated shape and the ground truth in feature space. Then in the testing phase, it utilizes these descent directions to predict shape increment iteratively. However, when the facial expression or direction changes too much, the general SDM cannot obtain good performance due to the large variations between the initial shape and the target shape. In this paper, we propose a multi-template SDM (MtSDM) which can maintain high accuracy on training data and meanwhile improve the accuracy on testing data. Instead of only one model is constructed in the training phase, multiple different models are constructed by repeatedly inputting the images which have large variations on expressions or head poses. And in the testing phase, the distances between some specific landmarks are calculated to select an optimal model to update the point location. The experimental results show that our proposed method can improve the performance of traditional SDM and performs better than several existing state-of-the-art methods.     

Model uncertainty and multimodel inference in reliability estimation within a longitudinal framework

Laenen, Alonso, and Molenberghs (2007) and Laenen, Alonso, Molenberghs, and Vangeneugden (2009) proposed a method to assess the reliability of rating scales in a longitudinal context. The methodology is based on hierarchical linear models, and reliability coefficients are derived from the corresponding covariance matrices. However, finding a good parsimonious model to describe complex longitudinal data is a challenging task. Frequently, several models fit the data equally well, raising the problem of model selection uncertainty. When model uncertainty is high one may resort to model averaging, where inferences are based not on one but on an entire set of models. We explored the use of different model building strategies, including model averaging, in reliability estimation. We found that the approach introduced by Laenen et al. (2007, 2009) combined with some of these strategies may yield meaningful results in the presence of high model selection uncertainty and when all models are misspecified, in so far as some of them manage to capture the most salient features of the data. Nonetheless, when all models omit prominent regularities in the data, misleading results may be obtained. The main ideas are further illustrated on a case study in which the reliability of the Hamilton Anxiety Rating Scale is estimated. Importantly, the ambit of model selection uncertainty and model averaging transcends the specific setting studied in the paper and may be of interest in other areas of psychometrics.     

Unified framework for recognition, localization and mapping using wearable cameras

Monocular approaches to simultaneous localization and mapping (SLAM) have recently addressed with success the challenging problem of the fast computation of dense reconstructions from a single, moving camera. Thus, if these approaches initially relied on the detection of a reduced set of interest points to estimate the camera position and the map, they are currently able to reconstruct dense maps from a handheld camera while the camera coordinates are simultaneously computed. However, these maps of 3-dimensional points usually remain meaningless, that is, with no memorable items and without providing a way of encoding spatial relationships between objects and paths. In humans and mobile robotics, landmarks play a key role in the internalization of a spatial representation of an environment. They are memorable cues that can serve to define a region of the space or the location of other objects. In a topological representation of the space, landmarks can be identified and located according to its structural, perceptive or semantic significance and distinctiveness. But on the other hand, landmarks may be difficult to be located in a metric representation of the space. Restricted to the domain of visual landmarks, this work describes an approach where the map resulting from a point-based, monocular SLAM is annotated with the semantic information provided by a set of distinguished landmarks. Both features are obtained from the image. Hence, they can be linked by associating to each landmark all those point-based features that are superimposed to the landmark in a given image (key-frame). Visual landmarks will be obtained by means of an object-based, bottom-up attention mechanism, which will extract from the image a set of proto-objects. These proto-objects could not be always associated with natural objects, but they will typically constitute significant parts of these scene objects and can be appropriately annotated with semantic information. Moreover, they will be affine covariant regions, that is, they will be invariant to affine transformation, being detected under different viewing conditions (view-point angle, rotation, scale, etc.). Monocular SLAM will be solved using the accurate parallel tracking and mapping (PTAM) framework by Klein and Murray in Proceedings of IEEE/ACM international symposium on mixed and augmented reality, 2007.     

Localization of targets across saccades: Role of landmark objects

Saccadic eye movements are required to bring different parts of the visual world into the foveal region of the retina. With each saccade, the images of the objects drastically change their retinal positions—nevertheless, the visual world appears continuous and does not seem to jump. How does the visual system achieve this continuous and stable percept of the visual world, despite the gross changes of its retinal projection that occur with each saccade? The present paper argues that an important factor of this type of space constancy is formed by the reafferent information, i.e., the visual display that is found when the eyes land. Three experiments demonstrate that objects present across the saccade can serve as landmarks for postsaccadic relocalization. The basic experimental manipulation consisted of a systematic displacement of these landmark objects during the saccade. The effectiveness of the landmarks was determined by analysing to what degree they modify the perceived shift of a small saccade target that was blanked for 200 ms during and after the saccade. A first experiment studied the spatial range where objects become effective as landmarks. The data show that landmarks close to the saccade target and horizontally aligned with the target are specifically effective. The second experiment demonstrates that postsaccadic localization is normally based on relational information about relative stimulus positions transferred across the saccade. A third experiment studied the effect of a prominent background frame on transsaccadic localization; the results suggest that background structures contribute only little to transsaccadic localization.     

How to separate coordinate and categorical spatial relation components in integrated spatial representations: A new methodology for analysing sketch maps

Spatial relations between landmarks can be represented by means of categories and coordinates. In the present research, this paradigm was applied to sketch maps based on information acquired in goal-directed behaviour of exploration of a university campus area. The first aim was to investigate whether categorical and coordinate information can be considered conceptually independent in sketch maps. The second aim was to assess which kind of distance measure served better to represent coordinate information in the present case study, and finally to assess the factorial structure of coordinate and categorical data. Analytic methodology as well as statistical analysis were found to confirm that separating coordinate and categorical components was formally as well as empirically appropriate. A series of confirmatory factor analyses showed the best fit for the model with two correlated components, as well as an acceptable reliability of measures emerged. The two components were moderately correlated. Moreover, the adoption of Manhattan distance seemed to be the most effective method to represent coordinate spatial relations in spatial sketch maps of areas acquired through navigation.     

An artificial eye for evaluating videobased eye recording systems

Videobased corneal-reflection-to-pupil-center systems are widely used in eye movement research. In this paper, an artificial eye drawn on a computer screen is presented. The artificial eye provides a way to simulate measurements of eye position in human subjects. The method allows testing videobased systems on the level of the signal and on the level of the calibration algorithm used to map the eye position parameters to stimulus space. In addition, the artificial eye can be used to evaluate specific hypotheses concerning the functioning or malfunctioning of the eye recorder and as a help in developing data analysis programs.     

Knowledge space theory, formal concept analysis, and computerized psychological assessment

In the present study, the use of knowledge space theory (KST), jointly with formal concept analysis (FCA), is proposed for developing a formal representation of the relations between the items of a questionnaire and a set of psychodiagnostic criteria. This formal representation can be used to develop an efficient adaptive tool for psychological assessment. Rusch and Wille (1996) have shown some interesting connections between KST and FCA; these connections are applied in the construction of knowledge structures, starting from a formal context representing the relations between items and criteria. The proposed general methodology was applied, as an example, to the Maudsley Obsessional-Compulsive Questionnaire. We used a data set provided by a sample of patients with a diagnosis of obsessive-compulsive disorder to validate the obtained structures. The parameters of the basic local independence model (BLIM) were estimated for the obtained knowledge structures. The fit of each model was tested by parametric bootstrap because of the sparseness of the derived data matrix. The results are discussed in light of both psychological and methodological relapses. In particular, we propose a reinterpretation of the BLIM parameters that seems suitable for testing reliability and construct validity; furthermore, it is pointed out how the obtained structures could represent the starting point for the development of a computerized assessment tool.