Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Advanced Content and Interface Personalization through Conversational Behavior and Affective Embodied Conversational Agents

Conversation is becoming one of the key interaction modes in HMI. As a result, the conversational agents (CAs) have become an important tool in various everyday scenarios. From Apple and Microsoft to Amazon, Google, and Facebook, all have adapted their own variations of CAs. The CAs range from chatbots and 2D, carton-like implementations of talking heads to fully articulated embodied conversational agents performing interaction in various concepts. Recent studies in the field of face-to-face conversation show that the most natural way to implement interaction is through synchronized verbal and co-verbal signals (gestures and expressions). Namely, co-verbal behavior represents a major source of discourse cohesion. It regulates communicative relationships and may support or even replace verbal counterparts. It effectively retains semantics of the information and gives a certain degree of clarity in the discourse. In this chapter, we will represent a model of generation and realization of more natural machine-generated output

Navigation Instruction Validation Tool and Indoor Wayfinding Training System for People with Disabilities

According to World Health Survey, there are 785 million (15.6%) people in the world that live with a disability. It is a well-known fact that lack of access to public transportation is a barrier for people with disabilities in seeking work or accessing health care. In this research, we seek to increase access to public transportation by introducing a virtual pre-travel training system that enables people with disabilities to get familiar with a public transportation venue prior to arriving at the venue. Using this system, users establish a mental map of the target environment prior to their arrival to the physical space, increasing their confidence and therefore increasing their chances of using public transportation. First, we have to guarantee that all navigation instructions sent to our training system are correct. Since the number of navigation instruction increases dramatically, instruction validation becomes a challenge. We propose a video game based validation tool which includes a game scene that represents in 2D the physical environment and uses a game avatar to verify the navigation instructions automatically in the game scene. The avatar traverses the virtual space following the corresponding navigation instructions. Only in case that it successfully reaches the planned destination, the current navigation instruction can be considered as correct. Then, we introduce a virtual reality based pre-travel wayfinding training system to assist people with disabilities to get familiar with a venue prior to their arrival at the physical space, which provides two modes: 1) Self-Guided mode in which the path between a source and a destination is shown to the user from third person perspective, and 2) Exploration mode in which the user explores and interacts with the environment. In the end, we have implemented visual analytics tools that track and evaluate trainees’ performance and help us optimize the game. These tools identify the difficulties faced by the trainees as well as obtain overall statistics on the trainees’ behavior in the indoor environment, helping us understand how to modify the system and adjust it to different classes of disabilities