Virtual environments (VEs) can be modulated and adapted to the needs of each user, but in the case of patients, performance can be affected by several factors that are difficult to expertise. The objective of the study is to explore the relationship between workload and the interaction techniques used during selection in a virtual apartment. Fifty-six participants performed tasks in a VE with 2D or, more immersive, 3D interaction techniques. The VE used was the Virtual Multitasking Test (Banville et al., 2018 ) where participants realized several everyday tasks in a virtual apartment. Workload and variables describing how individuals felt in the VE were measured using questionnaires, and performance in VE has been assessed. Results showed that 2D selection techniques have a better usability than 3D ones. The performance (success in task realization) on the virtual tasks was not impacted by the interaction techniques. Our results suggest that the easier it is to use an interaction technique, the less workload is associated with it. Sense of presence and cybersickness were affected by 3D interaction techniques. Thus, future VEs for cognitive assessment and rehabilitation, based on instrumental activities of daily living, will have to adapt their interaction techniques to different users.

Virtual reality (VR) is a technology covering a large field of applications among which are sports and video games. In both gaming and sporting VR applications, interaction techniques involve specific gestures such as catching or striking. However, such dynamic gestures are not currently being recognized as elementary task primitives, and have therefore not been investigated as such. In this paper, we propose a framework for the analysis of interaction in dynamic virtual environments (DVEs). This framework is based on three dynamic interaction primitives (DIPs) that are common to many sporting activities: catching, throwing, and striking. For each of these primitives, an original modeling approach is proposed. Furthermore, we introduce and formalize the concept of dynamic virtual fixtures (DVFs). These fixtures aim to assist the user in tasks involving interaction with moving objects or with objects to be set in movement. Two experiments have been carried out to investigate the influence of different DVFs on human performance in the context of ball catching and archery. The results reveal a significant positive effect of the DVFs, and that DVFs could be either classified as “performance-assisted” or “learning-assisted.”

Everyday action impairment is one of the diagnostic criteria of Alzheimer's disease and is associated with many serious consequences, including loss of functional autonomy and independence. It has been shown that the (re)learning of everyday activities is possible in Alzheimer's disease by using error reduction teaching approaches in naturalistic clinical settings. The purpose of this study is to develop a dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease and to establish its value as a training tool for everyday activities in these patients. Two everyday tasks and two error reduction learning methods were implemented within a virtual kitchen. Two patients with Alzheimer's disease and two healthy elderly controls were tested. All subjects were trained in two learning sessions on two comparable cooking tasks. Within each group (i.e., patients and controls), the order of the training methods was counterbalanced. Repeated measure analysis before and after learning was performed. A questionnaire of presence and a verbal interview were used to obtain information about the subjective responses of the participants to the VR experience. The results in terms of errors, omissions, and perseverations (i.e., repetitive behaviors) indicate that the patients performed worse than the controls before learning, but that they reached a level of performance similar to that of the controls after a short learning session, regardless of the learning method employed. This finding provides preliminary support for the value of the dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease. However, further work is needed before it is ready for clinical application.

Humans possess the ability to perform complex manipulations without the need to consciously perceive detailed motion plans. When a large number of trials and tests are required for techniques such as learning by imitation and programming by demonstration, the virtual reality approach provides an effective method. Indeed, virtual environments can be built economically and quickly, and can be automatically reinitialized. In the fields of robotics and virtual reality, this has now become commonplace. Rather than imitating human actions, our focus is to develop an intuitive and interactive method based on user demonstrations to create humanlike, autonomous behavior for a virtual character or robot. Initially, a virtual character is built via real-time virtual simulation in which the user demonstrates the task by controlling the virtual agent. The necessary data (position, speed, etc.) to accomplish the task are acquired in a Cartesian space during the demonstration session. These data are then generalized off-line by using a neural network with a back-propagation algorithm. The objective is to model a function that represents the studied task, and by so doing, to adapt the agent to deal with new cases. In this study, the virtual agent is a 6-DOF arm manipulator, Kuka Kr6, and the task is to grasp a ball thrown into its workspace. Our approach is to find a minimum number of necessary demonstrations while maintaining adequate task efficiency. Moreover, the relationship between the number of dimensions of the estimated function and the number of human trials is studied, depending on the evolution of the learning system.

Research on virtual environments (VE) produced significant advances in computer hardware (graphics boards and i/o tools) and software (real-time distributed simulations). However, fundamental questions remain about how user performance is affected by such factors as graphics refresh rate, resolution, control latencies, and multimodal feedback. This article reports on two experiments performed to examine dextrous manipulation of virtual objects. The first experiment studies the effect of graphics frame rate and viewing mode (monoscopic vs. stereoscopic) on the time required to grasp a moving target. The second experiment studies the effect of direct force feedback, pseudoforce feedback, and redundant force feedback on grasping force regulation. The trials were performed using a partially-immersive environment (graphics workstation and LCD glasses), a DataGlove, and the Rutgers Master with force feedback. Results of the first experiment indicate that stereoscopic viewing is beneficial for low refresh rates (it reduced task completion time by about 50% vs. monoscopic graphics). Results of the second experiment indicate that haptic feedback increases performance and reduces error rates, as compared to the open loop case (with no force feedback). The best performance was obtained when both direct haptic and redundant auditory feedback were provided to the user. The large number of subjects participating in these experiments (over 160 male and female) indicates good statistical significance for the above results.