学生の満足度に付随したヴァーチャルラーニングにおける有効性向上の創造と非言語的行動についての調査

国立大学法人長岡技術科学大

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

The influence of pedagogical experience on assessing student comprehension from nonverbal communication

textThis report details the development and execution of a pilot study investigating the influence of pedagogical experience on assessing student comprehension from nonverbal communication. The literary review identifies gaps in the current body of knowledge pertaining to teacher decoding of student nonverbal communication. The literary review also identifies instruments and procedures used in current nonverbal behavior research which will benefit the pilot study. After describing the instruments and procedure, the report presents the pilot study's results from interviewing six subjects. Using the results and recommendations from the study's subjects, the report recommends an instrument and procedure to conduct a full experiment.Mechanical Engineerin

Emotional recognition in computing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University 8/4/2010.Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user’s emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users’ behaviour, I developed a novel affective behaviour coding system called ‘affectemes’. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to ‘repair’ the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.EPSRC project grant (R81374/01

Development of the huggable social robot Probo: on the conceptual design and software architecture

This dissertation presents the development of a huggable social robot named Probo. Probo embodies a stuffed imaginary animal, providing a soft touch and a huggable appearance. Probo's purpose is to serve as a multidisciplinary research platform for human-robot interaction focused on children. In terms of a social robot, Probo is classified as a social interface supporting non-verbal communication. Probo's social skills are thereby limited to a reactive level. To close the gap with higher levels of interaction, an innovative system for shared control with a human operator is introduced. The software architecture de nes a modular structure to incorporate all systems into a single control center. This control center is accompanied with a 3D virtual model of Probo, simulating all motions of the robot and providing a visual feedback to the operator. Additionally, the model allows us to advance on user-testing and evaluation of newly designed systems. The robot reacts on basic input stimuli that it perceives during interaction. The input stimuli, that can be referred to as low-level perceptions, are derived from vision analysis, audio analysis, touch analysis and object identification. The stimuli will influence the attention and homeostatic system, used to de ne the robot's point of attention, current emotional state and corresponding facial expression. The recognition of these facial expressions has been evaluated in various user-studies. To evaluate the collaboration of the software components, a social interactive game for children, Probogotchi, has been developed. To facilitate interaction with children, Probo has an identity and corresponding history. Safety is ensured through Probo's soft embodiment and intrinsic safe actuation systems. To convey the illusion of life in a robotic creature, tools for the creation and management of motion sequences are put into the hands of the operator. All motions generated from operator triggered systems are combined with the motions originating from the autonomous reactive systems. The resulting motion is subsequently smoothened and transmitted to the actuation systems. With future applications to come, Probo is an ideal platform to create a friendly companion for hospitalised children

Social Interactions in Immersive Virtual Environments: People, Agents, and Avatars

Immersive virtual environments (IVEs) have received increased popularity with applications in many fields. IVEs aim to approximate real environments, and to make users react similarly to how they would in everyday life. An important use case is the users-virtual characters (VCs) interaction. We interact with other people every day, hence we expect others to appropriately act and behave, verbally and non-verbally (i.e., pitch, proximity, gaze, turn-taking). These expectations also apply to interactions with VCs in IVEs, and this thesis tackles some of these aspects. We present three projects that inform the area of social interactions with a VC in IVEs, focusing on non-verbal behaviours. In our first study on interactions between people, we collaborated with the Social Neuroscience group at the Institute of Cognitive Neuroscience from UCL on a dyad multi-modal interaction. This aims to understand the conversation dynamics, focusing on gaze and turn-taking. The results show that people have a higher frequency of gaze change (from averted to direct and vice versa) when they are being looked at compared to when they are not. When they are not being looked at, they are also directing their gaze to their partners more compared to when they are being looked at. Another contribution of this work is the automated method of annotating speech and gaze data. Next, we consider agents’ higher-level non-verbal behaviours, covering social attitudes. We present a pipeline to collect data and train a machine learning (ML) model that detects social attitudes in a user-VC interaction. Here we collaborated with two game studios: Dream Reality Interaction and Maze Theory. We present a case study for the ML pipeline on social engagement recognition for the Peaky Blinders narrative VR game from Maze Theory studio. We use a reinforcement learning algorithm with imitation learning rewards and a temporal memory element. The results show that the model trained with raw data does not generalise and performs worse (60% accuracy) than the one trained with socially meaningful data (83% accuracy). In IVEs, people embody avatars and their appearance can impact social interactions. In collaboration with Microsoft Research, we report a longitudinal study in mixed-reality on avatar appearance in real-work meetings between co-workers comparing personalised full-body realistic and cartoon avatars. The results imply that when participants use realistic avatars first, they may have higher expectations and they perceive their colleagues’ emotional states with less accuracy. Participants may also become more accustomed to cartoon avatars as time passes and the overall use of avatars may lead to less accurately perceiving negative emotions. The work presented here contributes towards the field of detecting and generating nonverbal cues for VCs in IVEs. These are also important building blocks for creating autonomous agents for IVEs. Additionally, this work contributes to the games and work industry fields through an immersive ML pipeline for detecting social attitudes and through insights into using different avatar styles over time in real-world meetings

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202