Studies of working memory in interrupted human-computer interaction

Distraction in the workplace is increasingly more common in the information age. Several tasks and sources of information compete for a worker's limited cognitive capacities in human-computer interaction (HCI). In some situations even very brief interruptions can have detrimental effects on memory. Nevertheless, in other situations where persons are continuously interrupted, virtually no interruption costs emerge. This dissertation attempts to reveal the mental conditions and causalities differentiating the two outcomes. The explanation, building on the theory of long-term working memory (LTWM; Ericsson and Kintsch, 1995), focuses on the active, skillful aspects of human cognition that enable the storage of task information beyond the temporary and unstable storage provided by short-term working memory (STWM). Its key postulate is called a retrieval structure an abstract, hierarchical knowledge representation built into long-term memory that can be utilized to encode, update, and retrieve products of cognitive processes carried out during skilled task performance. If certain criteria of practice and task processing are met, LTWM allows for the storage of large representations for long time periods, yet these representations can be accessed with the accuracy, reliability, and speed typical of STWM. The main thesis of the dissertation is that the ability to endure interruptions depends on the efficiency in which LTWM can be recruited for maintaing information. An observational study and a field experiment provide ecological evidence for this thesis. Mobile users were found to be able to carry out heavy interleaving and sequencing of tasks while interacting, and they exhibited several intricate time-sharing strategies to orchestrate interruptions in a way sensitive to both external and internal demands. Interruptions are inevitable, because they arise as natural consequences of the top-down and bottom-up control of multitasking. In this process the function of LTWM is to keep some representations ready for reactivation and others in a more passive state to prevent interference. The psychological reality of the main thesis received confirmatory evidence in a series of laboratory experiments. They indicate that after encoding into LTWM, task representations are safeguarded from interruptions, regardless of their intensity, complexity, or pacing. However, when LTWM cannot be deployed, the problems posed by interference in long-term memory and the limited capacity of the STWM surface. A major contribution of the dissertation is the analysis of when users must resort to poorer maintenance strategies, like temporal cues and STWM-based rehearsal. First, one experiment showed that task orientations can be associated with radically different patterns of retrieval cue encodings. Thus the nature of the processing of the interface determines which features will be available as retrieval cues and which must be maintained by other means. In another study it was demonstrated that if the speed of encoding into LTWM, a skill-dependent parameter, is slower than the processing speed allowed for by the task, interruption costs emerge. Contrary to the predictions of competing theories, these costs turned out to involve intrusions in addition to omissions. Finally, it was learned that in rapid visually oriented interaction, perceptual-procedural expectations guide task resumption, and neither STWM nor LTWM are utilized due to the fact that access is too slow. These findings imply a change in thinking about the design of interfaces. Several novel principles of design are presented, basing on the idea of supporting the deployment of LTWM in the main task.

Novel approach for region merging and image segmentation for human-computer interaction

For the purpose of human-computer interaction (HCI), a vision-based gesture segmentation approach is proposed. The technique essentially includes skin color detection and gesture segmentation. The skin color detection employs a skin-color artificial neural network (ANN). To merge and segment the region of interest, we propose a novel mountain algorithm. The details of the approach and experiment results are provided. The experimental segmentation accuracy is 96.25%. (C) 2003 Society of Photo-Optical Instrumentation Engineers.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Fast Head Tilt Detection for Human-Computer Interaction

Accurate head tilt detection has a large potential to aid people with disabilities in the use of human-computer interfaces and provide universal access to communication software. We show how it can be utilized to tab through links on a web page or control a video game with head motions. It may also be useful as a correction method for currently available video-based assistive technology that requires upright facial poses. Few of the existing computer vision methods that detect head rotations in and out of the image plane with reasonable accuracy can operate within the context of a real-time communication interface because the computational expense that they incur is too great. Our method uses a variety of metrics to obtain a robust head tilt estimate without incurring the computational cost of previous methods. Our system runs in real time on a computer with a 2.53 GHz processor, 256 MB of RAM and an inexpensive webcam, using only 55% of the processor cycles.

A Customizable Camera-based Human Computer Interaction System Allowing People With Disabilities Autonomous Hands Free Navigation of Multiple Computing Task

Many people suffer from conditions that lead to deterioration of motor control and makes access to the computer using traditional input devices difficult. In particular, they may loose control of hand movement to the extent that the standard mouse cannot be used as a pointing device. Most current alternatives use markers or specialized hardware to track and translate a user's movement to pointer movement. These approaches may be perceived as intrusive, for example, wearable devices. Camera-based assistive systems that use visual tracking of features on the user's body often require cumbersome manual adjustment. This paper introduces an enhanced computer vision based strategy where features, for example on a user's face, viewed through an inexpensive USB camera, are tracked and translated to pointer movement. The main contributions of this paper are (1) enhancing a video based interface with a mechanism for mapping feature movement to pointer movement, which allows users to navigate to all areas of the screen even with very limited physical movement, and (2) providing a customizable, hierarchical navigation framework for human computer interaction (HCI). This framework provides effective use of the vision-based interface system for accessing multiple applications in an autonomous setting. Experiments with several users show the effectiveness of the mapping strategy and its usage within the application framework as a practical tool for desktop users with disabilities.

Emotion Recognition in Human-Computer Interaction

The authors are concerned with the development of computer systems that are capable of using information from faces and voices to recognise people's emotions in real-life situations. The paper addresses the nature of the challenges that lie ahead, and provides an assessment of the progress that has been made in the areas of signal processing and analysis techniques (with regard to speech and face), and the psychological and linguistic analyses of emotion. Ongoing developmental work by the authors in each of these areas is described.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Human Computer Interaction using gestures for mobile devices and serious games: A review

The Human-Computer Interaction (HCI) with interfaces is an active challenge field in the industry over the past decades and has opened the way to communicate with the means of verbal, hand and body gestures using the latest technologies for a variety of different applications in areas such as video games, training and simulation. However, accurate recognition of gestures is still a challenge. In this paper, we review the basic principles and current methodologies used for collecting the raw gesture data from the user for recognize actions the users perform and the technologies currently used for gesture-HCI in games enterprise. In addition, we present a set of projects from various applications in games industry that are using gestural interaction.

Towards natural human computer interaction in BCI

BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2]. We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data. We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects. Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.

Human-computer interaction in ubiquitous computing environments

Purpose – The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings.

Design/methodology/approach – The paper quantifies the performance of human movement based on Fitt's Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium.

Findings – The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm.

Practical implications – In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user's body-based interaction styles.

Originality/value – The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings.

An Intelligent Human-Computer Interaction System for Decision Support

This paper proposes a novel architecture for
developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be
incorporated into a computerized system and, at the same time, to
preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking
process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first
employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed
approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.

A new perspective on competency management: Implemented through effective human-computer interaction

Making an informed decision on whether an individual is suited to undertake an educational course or industry training programme can be very frustrating. When dealing with young adults at different cognitive skill levels, it is important to be able to identify and distinguish between their knowledge/competency levels, mostly on the basis of the evidence gathered from test-items. The current absence of appropriate measurement tools to determine skill/competency/knowledge levels remains a practical issue. The main aim of this paper is to discuss the management of this important differentiation in cognitive skill performance. One of the dilemmas surrounding this type of competency evaluation is the time it takes to test an individual. Insisting for instance, that a novice undergoes a long and arduous test, including many difficult testing items, results in lowered self-esteem, reduced motivation for learning something new, may induce stress related disorders. Similarly, expecting a more competent individual to undergo numerous simple test-items can generate the same negative result. A Competency Management System (CMS) is presented to initiate effective human-computer interaction (HCI) for cognitive skills assessment. © 2009 Springer-Verlag US.