User-centered Virtual Environment Assessment And Design For Cognitive Rehabilitation Applications

Virtual environment (VE) design for cognitive rehabilitation necessitates a new methodology to ensure the validity of the resulting rehabilitation assessment. We propose that benchmarking the VE system technology utilizing a user-centered approach should precede the VE construction. Further, user performance baselines should be measured throughout testing as a control for adaptive effects that may confound the metrics chosen to evaluate the rehabilitation treatment. To support these claims we present data obtained from two modules of a user-centered head-mounted display (HMD) assessment battery, specifically resolution visual acuity and stereoacuity. Resolution visual acuity and stereoacuity assessments provide information about the image quality achieved by an HMD based upon its unique system parameters. When applying a user-centered approach, we were able to quantify limitations in the VE system components (e.g., low microdisplay resolution) and separately point to user characteristics (e.g., changes in dark focus) that may introduce error in the evaluation of VE based rehabilitation protocols. Based on these results, we provide guidelines for calibrating and benchmarking HMDs. In addition, we discuss potential extensions of the assessment to address higher level usability issues. We intend to test the proposed framework within the Human Experience Modeler (HEM), a testbed created at the University of Central Florida to evaluate technologies that may enhance cognitive rehabilitation effectiveness. Preliminary results of a feasibility pilot study conducted with a memory impaired participant showed that the HEM provides the control and repeatability needed to conduct such technology comparisons. Further, the HEM affords the opportunity to integrate new brain imaging technologies (i.e., functional Near Infrared Imaging) to evaluate brain plasticity associated with VE based cognitive rehabilitation

Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning

It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite

Reentrant Processing in Intuitive Perception

The process of perception requires not only the brain's receipt of sensory data but also the meaningful organization of that data in relation to the perceptual experience held in memory. Although it typically results in a conscious percept, the process of perception is not fully conscious. Research on the neural substrates of human visual perception has suggested that regions of limbic cortex, including the medial orbital frontal cortex (mOFC), may contribute to intuitive judgments about perceptual events, such as guessing whether an object might be present in a briefly presented fragmented drawing. Examining dense array measures of cortical electrical activity during a modified Waterloo Gestalt Closure Task, results show, as expected, that activity in medial orbital frontal electrical responses (about 250 ms) was associated with intuitive judgments. Activity in the right temporal-parietal-occipital (TPO) region was found to predict mOFC (∼150 ms) activity and, in turn, was subsequently influenced by the mOFC at a later time (∼300 ms). The initial perception of gist or meaning of a visual stimulus in limbic networks may thus yield reentrant input to the visual areas to influence continued development of the percept. Before perception is completed, the initial representation of gist may support intuitive judgments about the ongoing perceptual process

Physically-based Deformation of High-Resolution 3D Lung Models for Augmented Reality based Medical Visualization

Visualization tools using Augmented Reality Environments are effective in applications related to medical training, prognosis and expert interaction. Such medical visualization tools can also provide key visual insights on the physiology of deformable anatomical organs (e.g. lungs). In this paper we propose a deformation method that facilitates physically-based elastostatic deformations of 3D high-resolution polygonal models. The implementation of the deformation method as a pre-computation approach is shown for a 3D high-resolution lung model. The deformation is represented as an integration of the applied force and the local elastic property assigned to the 3D lung model. The proposed deformation method shows faster convergence to equilibrium as compared to other physically-based simulation methods. The proposed method also accounts for the anisotropic tissue elastic properties. The transfer functions are formulated in such a way that they overcome stiffness effects during deformations

\u3ci\u3eMedicine Meets Virtual Reality 21\u3c/i\u3e

Editors: James D. Westwood, Susan W. Westwood, Li Felländer-Tsai, Cali M. Fidopiastis, Randy S. Haluck, Richard A. Robb, Steven Senger, Kirby G. Vosburgh. Chapter, Varying the Speed of Perceived Self-Motion Affects Postural Control During Locomotion, co-authored by Joshua Pickhinke, Jung Hung Chien, Mukul Mukherjee, UNO faculty and staff members. Virtual reality environments have been used to show the importance of perception of self-motion in controlling posture and gait. In this study, the authors used a virtual reality environment to investigate whether varying optical flow speed had any effect on postural control during locomotion. Healthy young adult participants walked under two conditions, with optical flow matching their preferred walking speed, and with a randomly varying optic flow speed compared to their preferred walking speed. Exposure to the varying optic flow increased the variability in their postural control as measured by area of COP when compared with the matched speed condition. If perception of self-motion becomes less predictable, postural control during locomotion becomes more variable and possibly riskier.https://digitalcommons.unomaha.edu/facultybooks/1261/thumbnail.jp

User-centered virtual environment design for virtual rehabilitation

<p>Abstract</p> <p>Background</p> <p>As physical and cognitive rehabilitation protocols utilizing virtual environments transition from single applications to comprehensive rehabilitation programs there is a need for a new design cycle methodology. Current human-computer interaction designs focus on usability without benchmarking technology within a user-in-the-loop design cycle. The field of virtual rehabilitation is unique in that determining the efficacy of this genre of computer-aided therapies requires prior knowledge of technology issues that may confound patient outcome measures. Benchmarking the technology (e.g., displays or data gloves) using healthy controls may provide a means of characterizing the "normal" performance range of the virtual rehabilitation system. This standard not only allows therapists to select appropriate technology for use with their patient populations, it also allows them to account for technology limitations when assessing treatment efficacy.</p> <p>Methods</p> <p>An overview of the proposed user-centered design cycle is given. Comparisons of two optical see-through head-worn displays provide an example of benchmarking techniques. Benchmarks were obtained using a novel vision test capable of measuring a user's stereoacuity while wearing different types of head-worn displays. Results from healthy participants who performed both virtual and real-world versions of the stereoacuity test are discussed with respect to virtual rehabilitation design.</p> <p>Results</p> <p>The user-centered design cycle argues for benchmarking to precede virtual environment construction, especially for therapeutic applications. Results from real-world testing illustrate the general limitations in stereoacuity attained when viewing content using a head-worn display. Further, the stereoacuity vision benchmark test highlights differences in user performance when utilizing a similar style of head-worn display. These results support the need for including benchmarks as a means of better understanding user outcomes, especially for patient populations.</p> <p>Conclusions</p> <p>The stereoacuity testing confirms that without benchmarking in the design cycle poor user performance could be misconstrued as resulting from the participant's injury state. Thus, a user-centered design cycle that includes benchmarking for the different sensory modalities is recommended for accurate interpretation of the efficacy of the virtual environment based rehabilitation programs.</p

Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements

User-Centered Virtual . . .

Virtual environment (VE) design for cognitive rehabilitation necessitates a new methodology to ensure the validity of the resulting rehabilitation assessment. We propose that benchmarking the VE system technology utilizing a user-centered approach should precede the VE construction. Further, user performance baselines should be measured throughout testing as a control for adaptive effects that may confound the metrics chosen to evaluate the rehabilitation treatment. To support these claims we present data obtained from two modules of a user-centered head-mounted display (HMD) assessment battery, specifically resolution visual acuity and stereoacuity. Resolution visual acuity and stereoacuity assessments provide information about the image quality achieved by an HMD based upon its unique system parameters. When applying a user-centered approach, we were able to quantify limitations in the VE system components (e.g., low microdisplay resolution) and separately point to user characteristics (e.g., changes in dark focus) that may introduce error in the evaluation of VE based rehabilitation protocols. Based o