Catálogo de publicaciones - libros

In pursuing the ultimate goal of enabling intelligent conversation with a virtual human, two key challenges are selecting nonverbal behaviors to implement and realizing those behaviors practically and reliably. In this paper, we explore the signals interlocutors use to display uncertainty face to face. Peoples’ signals were identified and annotated through systematic coding and then implemented onto our ECA (Embodied Conversational Agent), RUTH. We investigated whether RUTH animations were as effective as videos of talking people in conveying an agent’s level of uncertainty to human viewers. Our results show that people could pick up on different levels of uncertainty not only with another conversational partner, but also with the simulations on RUTH. In addition, we used animations containing different subsets of facial signals to understand in more detail how nonverbal behavior conveys uncertainty. The findings illustrate the promise of our methodology for creating specific inventories of fine-grained conversational behaviors from knowledge and observations of spontaneous human conversation.

In this paper part of the work of the research project ’IntExMa’ is presented, that aims at the development of an interactive, in-store sales support system focusing on the virtual ’try-on’ of clothing. Customers are provided with highly realistic, individualized, three-dimensional virtual counterparts, derived from a small number of body measurements. The paper describes in detail the approach followed for the required automatic generation of avatars. It is based on a sophistication of the well-known concept of parametric deformation. The solution presented here is characterized by a range of means that provide highly flexible, fine-grained adjustments of the linear transformation process leading to precisely controllable results.

Physiological measures have found reliable sensitivity to the variation of mental efforts to tasks of different difficulty levels. The sensitivity needs to be enhanced for further application. This paper proposed a composite measure consisting of three physiological measures, facial skin temperature, eye blinks and pupil dilation. The facial skin temperature will be measured by an infrared camera. One dimensional iris image will be used for the measurement of eye activities. All measurement will be done in real-time and unobtrusively. A preliminary experiment will be conducted for each measure to demonstrate their sensitivity. The combination then will be accomplished by factor analysis and regression analysis. Last, the analysis will focus on the improvement in sensitivity from the combination of individual measures.

Brain-Computer Interface (BCI) technology is widely used in rehabilitation field. There are two main applications of BCI systems in assistive technology: regain the movements or communications for people with motor disability and neurofeedback for training the subject to emit a specific brain activity. In this study, we introduce two typical applications of BCI systems in our lab. For the first case, the BCI system based on mental tasks classification for people with motor disability is described. An effective features extraction and classification methods of EEG signals were proposed. For the second case, a neurofeedback (NFB) system was established, which utilized Virtual Reality (VR) to create appropriate feedback information which is more interesting, imaginative and interactive than traditional graphical presentations. Visual & auditory (IVA)-continuous performance test (CPT) results show that it can provide an effective therapy for treating attention deficit hyperactivity disorder (ADHD) children.

Existing research on human skills acquisition studies has shown that learning follows a non-linear pattern, but the exact form remains unknown due to the limitation of traditional experimental methods and lack of systematic modeling of tasks. We applied a non-linear fractal analysis on the time series data produced by human subjects on target-tracking motor learning tasks. Tracking of a non-fractal sinusoid-cosinusoid signal was used as the platform. Our preliminary results suggest that fractal models may prove effective in investigating details of the human learning process.

Somatosensory function declines with age, and such changes have been associated with diminished motor performance. Noise input can enhance sensory and motor function.We tested the effects of the vibrating stimulation applied at the feet on balance control of 6 healthy elderly and 8 young volunteers. Balance performance was characterized using a FASTRAK System. We calculated four traditional measures of postural sway parameters and five stabilogram-diffusion analysis variables.Among the 14 participants, application of noise resulted in a reduction of seven of nine sway parameters in young participants and eight of nine sway variables in elderly participants. The results suggested that the imperceptible noise, when applied to the feet, could enhance the balance performance of healthy older adults. Therefore, using the noise-based devices may be an effective way to improve the balance control of elderly people.

This paper presents the background and history of the virtual human Santosdeveloped by the Virtual Soldier Research (VSR) Program at The University of Iowa. The early virtual human environment was called Mira. This 15-degree-of-freedom (DOF) upper-body model with posture and motion prediction was funded by John Deere Inc. and US Army TACOM Automotive Research Center. In 2003 US Army TACOM began funding VSR to develop a new generation of virtual humans called Santos (109 DOFs), which was to be another generation of Mira. Later on, Caterpillar Inc., Honda R&D North Americas, Natick Soldier System Center, and USCAR (GM, Ford, and Chrysler) joined the VSR partnership. The objective is to develop a of digital humans comprising realistic human models including anatomy, biomechanics, physiology, and intelligence in , and to test digital mockups of products and systems before they are built, thus reducing the significant costs and time associated with making prototypes. The philosophy is based on a novel optimization-based approach for empowering these digital humans to perform, un-aided, in a physics-based world. The research thrusts include the following areas: (1) predictive dynamics, (2) modeling of cloth, (3) hand model, (4) intuitive interface, (5) motion capture, (6) muscle and physiology modeling, (7) posture and motion prediction, (8) spine modeling, and (9) real-time simulation and virtual reality (VR). Currently, the capabilities of Santos include whole-body posture prediction, advanced inverse kinematics, reach envelope analysis, workspace zone differentiation, muscle force and stress analysis, muscle fatigue prediction, simulation of walking and running, dynamic motion prediction, physiologic assessment, a user-friendly interface, a hand model and grasping capability, clothing modeling, thermo discomfort assessment, muscle wrapping and sliding, whole-body vibration analysis, and collision avoidance.

Digital human modeling and simulation plays an important role in product design, prototyping, and manufacturing: it reduces the number of design iterations and increases the safety and design quality of products. Posture prediction is one of the key capabilities. It is especially useful in the design of vehicle interiors for checking the reachability of buttons and determining comfort levels. This paper presents the validation of predicted posture for the virtual human Santos. The predicted posture is a physics-based model and is formulated as a multi-objective optimization (MOO) problem. The hypothesis is that human performance measures (cost functions) govern how humans move. We chose 12 subjects from four different percentiles, all Americans (female 5%, female 50%, male 50%, and male 95%). Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen: reaching a point at the top of the A-pillar, the radio tuner button, the glove box handle, and a point on the driver’s B-pillar seatbelt adjuster. The subjects were asked to reach the four target points, and the joint centers for wrist, elbow, and shoulder and the joint angle of elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. The results show that the predicted postures match well with the experiment results, and are realistic postures.

To response severe disaster, such as hurricane, tsunami, or terrorists attack, multi-organization, such as government, companies, media, and public are involved. How to make these inter- or intra dependent organizations cooperate timely and accurately when they face uncertainty and stressful situation is the primary concerns. In this paper, we proposed multi-organization performance model, which introduced organizational factors, individual factors and focused on communication and collaboration among different organizations. Based on it we developed multi-agent emergency response system. This IT solution has capability to integrate drills into simulated response activities and reproduce the drills. To demonstrate how the system can be used as a powerful tool for supporting and evaluating the performance of multi-organization in disaster management and response, the test simulation is carried out using a scenario extracted from real-life emergency drills.

In this paper, two different mental fatigue experiments, sleep deprivation and prolonged time of work, were designed to investigate their effect on mental fatigue respectively. Three parameters of multichannel linear descriptors i.e. , and are used to measure the level of mental fatigue for the first time. When mental fatigue level increases, the brain neurons are restrained, this results in the increase of synchrony degree between the distributed electroencephalogram (EEG). Therefore, spatial complexity , field strength and field changes of regional brain region would reduce. The experimental results also show that the average values of , and of EEG decrease with the prolonging of sleep deprivation and working time, and then also decrease with the increase of mental fatigue level. The average values of , and of EEG strongly correlate with mental fatigue. The multichannel linear descriptors of EEG are expected to serve as the indexes to evaluate mental fatigue level objectively.