Predictive models of human supervisory control behavioral patterns using hidden semi-Markov models

Behavioral models of human operators engaged in complex, time-critical high-risk domains, such as those typical in Human Supervisory Control (HSC) settings, are of great value because of the high cost of operator failure. We propose that Hidden Semi-Markov Models (HSMMs) can be employed to model behaviors of operators in HSC settings where there is some intermittent human interaction with a system via a set of external controls. While regular Hidden Markov Models (HMMs) can be used to model operator behavior, HSMMs are particularly suited to time-critical supervisory control domains due to their explicit representation of state duration. Using HSMMs, we demonstrate in an unmanned vehicle supervisory control environment that such models can accurately predict future operator behavior both in terms of states and durations.     

Using eye movement data to infer human behavioral intentions

Behavior-directed intentions can be revealed by certain biological signals that precede behaviors. This study used eye movement data to infer human behavioral intentions. Participants were asked to view pictures while operating under different intentions, which necessitated cognitive search and affective appraisal. Intentions regarding the pictures were non-specific or specific, specific intentions were cognitive or affective, and affective intentions were to evaluate either the positive or negative emotions expressed by the individuals depicted. The affective task group made more fixations and had a larger average pupil size than the cognitive task group. The positive appreciation group made more and shorter fixations, on average, than the negative appreciation group. However, support vector machine algorithms revealed low classification accuracy. This was due to large inter-individual variance and psychological factors underlying intentions. We demonstrated improvement in classification accuracy using individual repeated measures data, which helped infer participants’ self-selected intentions.     

Analyzing temporal patterns of topic diversity using graph clustering

The Journal of Supercomputing - During a disaster, social media can be both a source of help and of danger: Social media has a potential to diffuse rumors, and officials involved in disaster...     

Analyzing oriented patterns

Oriented patterns, such as those produced by propagation, accretion, or deformation, are common in nature and therefore an important class for visual analysis. Our approach to understanding such patterns is to decompose them into two parts: the flow field, describing the direction of anisotropy; and the residual pattern obtained by describing the image in a coordinate system built from the flow field. We develop a method for the local estimation of anisotropy and a method for combining the estimates to construct a flow coordinate system. Several examples of the use of these methods are presented. These include the use of the flow coordinates to provide preferred directions for edge detection, detection of anomalies, fitting simple models to the straightened pattern, and detecting singularities in the flow field.     

Analyzing surface sampling patterns using the localized pair correlation function

Computational Visual Media - Point distributions with different characteristics have a crucial influence on graphics applications. Various analysis tools have been developed in recent years, mainly...     

Effects of movement speed and predictability in human–robot collaboration

Human–robot collaboration (HRC) is characterized by a spatiotemporal overlap between the workspaces of the human and the robot and has become a viable option in manufacturing and other industries. However, for companies considering employing HRC it remains unclear how best to configure such a setup, because empirical evidence on human factors requirements remains inconclusive. As robots execute movements at high levels of automation, they adapt their speed and movement path to situational demands. This study therefore experimentally investigated the effects of movement speed and path predictability of an industrial collaborating robot on the human operator. Participants completed tasks together with a robot in an industrial workplace simulated in virtual reality. A lower level of predictability was associated with a loss in task performance, while faster movements resulted in higher‐rated values for task load and anxiety, indicating demands on the operator exceeding the optimum. Implications for productivity and safety and possible advancements in HRC workplaces are discussed.     

The recognition of human movement using temporal templates

A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms     

Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant messaging tool: A case study

Online discussions have been widely utilized as an educational activity, and much research has been conducted on the process and behaviors involved in asynchronous discussions. However, research on behavioral patterns in learners’ synchronous discussions, including the process of social knowledge construction and project coordination is limited. Through the examination of the behavioral patterns and differences between students with high- and low-quality discussions, it may be possible to understand the limitations of knowledge construction in synchronous discussions. Furthermore, these findings may help teachers design and guide synchronous discussions activities. This study is an empirical case study in which college students conducted synchronous discussions based on topics specified by the teacher. The students used a text-based instant messaging (IM) tool in a period of 98 days.     

Analyzing discretizations of continuous attributes given a monotonic discrimination function

This article addresses the problem of analyzing existing discretizations of continuous attributes with regard to their redundancy and minimality properties. The research was inspired by the increasing number of heuristic algorithms created for generating the discretizations using various methodologies, and apparent lack of any direct techniques for examining the solutions obtained as far as their basic properties, (e.g., the redundancy), are concerned. The proposed method of analysis fills this gap by providing a test for redundancy and enabling for a controlled reduction of the discretization's size within specified limits. Rough set theory techniques are used as the basic tools in this method. Exemplary results of discretization analyses for some known real-life data sets are presented for illustration.     

Interdependence and predictability of human mobility and social interactions

Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. The existing prediction techniques exploit only the past history of the person taken into consideration as input of the predictors.In this paper, we show that by means of multivariate nonlinear time series prediction techniques it is possible to increase the forecasting accuracy by considering movements of friends, people, or more in general entities, with correlated mobility patterns (i.e., characterised by high mutual information) as inputs. Finally, we evaluate the proposed techniques on the Nokia Mobile Data Challenge and Cabspotting datasets.     

A similarity measure between patterns with nonindependent attributes

A generalized version of a set-theoretical measure for obtaining similarities between patterns with nonindependent attributes is presented. The dependence here is given by the pairwise correlation. Since the proposed measure needs no assumption of attribute independence, the resulting similarity values can reflect directly the relationships between the attributes.     

Blind source separation using temporal predictability.

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O(N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, subgaussian, and gaussian probability density functions and on mixtures of voices and music.     

Health-status monitoring through analysis of behavioral patterns

With the rapid growth of the elderly population, there is a need to support the ability of elders to maintain an independent and healthy lifestyle in their homes rather than through more expensive and isolated care facilities. One approach to accomplish these objectives employs the concepts of ambient intelligence to remotely monitor an elder's activities and condition. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity; a prototype of the system is being tested within a subject's home. We examined whether the system could be used to detect behavioral patterns and report the results in this paper. Mixture models were used to develop a probabilistic model of behavioral patterns. The results of the mixture-model analysis were then evaluated by using a log of events kept by the occupant.     

Fusion of eye movement and mouse dynamics for reliable behavioral biometrics

This paper presents the first attempt to fuse two different kinds of behavioral biometrics: mouse dynamics and eye movement biometrics. Mouse dynamics were collected without any special equipment, while an affordable The Eye Tribe eye tracker was used to gather eye movement data at a frequency of 30 Hz, which is also potentially possible using a common web camera. We showed that a fusion of these techniques is quite natural and it is easy to prepare an experiment that collects both traits simultaneously. Moreover, the fusion of information from both signals gave 6.8 % equal error rate and 92.9 % accuracy for relatively short registration time (20 s on average). Achieving such results were possible using dissimilarity matrices based on dynamic time warping distance.     

Unsupervised extraction of patterns and trends within highway systems condition attributes data

Highway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.     

Analyzing behavioral properties of semantic business processes with parametric data

One of the basic problems in (semantic) business process management concerns the behavioral analysis at design time so that only correct processes are exposed. Although this analysis requires the semantic propagation of data and system preconditions through processes and between processes to be considered, some concrete data are usually obtained at runtime and cannot be used in early process development phases. This paper proposes Parametric Unary RDF Annotated Petri Net Systems (P‐U‐RDF‐PN Systems) as a formalism to alleviate this problem. The formalism allows analyzing behavioral properties of semantic business processes at design time. Parametric values are used to represent the data that will be obtained at runtime, allowing to also consider them in the analysis phase. The paper formally defines the semantics of the formalism in terms of enabling and firing of transitions as well as the concept of reachability graph. It also presents how to compute and analyze this parametric reachability graph. The analysis is based on an adaption of model checking techniques, being temporal logic the formalism used to specify the system properties to be analyzed. Finally, an implementation prototype using RDF and SPARQL tools and a Satisfiability Modulo Theories solver is presented, with some experiments evaluating how scalable the prototype is. Copyright © 2010 John Wiley & Sons, Ltd.     

Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm

Multimedia Tools and Applications - Human behavior pattern recognition (BPR) from accelerometer signals is a challenging problem due to variations in signal durations of different behaviors....