Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.
This paper reports on the design and evaluation of a co-creative drawing partner called the Drawing Apprentice, which was designed to improvise and collaborate on abstract sketches with users in real time. The system qualifies as a new genre of creative technologies termed casual creators that are meant to creatively engage users and provide enjoyable creative experiences rather than necessarily helping users make a higher quality creative product. We introduce the conceptual framework of participatory sense-making and describe how it can help model and understand open-ended collaboration. We report the results of user studies evaluating different prototypes of the system during an iterative design process. Based on insights from the user studies, we present design recommendations for co-creative agents.
This paper presents a novel smart eyewear that recognizes a wearer's facial expression in daily scenes. We evaluated our device and showed the robustness to the noise from a wearer's facial direction change, repeatability and the positional drift of the glasses. Our device uses embedded photo reflective sensors and machine learning to recognize a wearer's facial expressions. We leverage the skin deformation when a wearer changes their facial expressions. With small photo reflective sensors, we measure the proximity between the skin surface on a face and the eyewear frame where 17 sensors are integrated. A Support Vector Machine (SVM) algorithm was applied for the sensor information. The sensors can cover various facial muscle movements and can be integrated into everyday glasses.There are various possible scenarios of our devices such as a care system for older adults and mental management. The main contributions of our work are as follows. (1) We evaluated the recognition accuracy in daily scenes. We showed 92.8% accuracy regardless of facial direction, taking on/off by learning those data. Our device can recognize facial expressions with 78.1% accuracy for repeatability, with 87.7% accuracy in case of its positional drift. (2) It is designed and implemented considering social acceptability. The device looks like normal eyewear, so users can wear it anytime, anywhere. (3) Initial field trials in daily life were undertaken. Our work is one of the first attempts to recognize and evaluate a variety of facial expressions in the form of an unobtrusive wearable.
Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable to exhibit and recognize in others movement. In sports (e.g., martial arts) as well as in performing arts (e.g., dance), the same sequence of movements can be performed in a wide range of ways characterized by different qualities, often in terms of subtle (spatial and temporal) perturbations of the movement. Even a non-expert observer can distinguish between a top-level and an average performance by a dancer or martial artist. The difference is not in the performed movements - the same in both cases - but in the quality of their performance. In this paper, we present a computational framework aiming at an automated approximate measure of movement quality in full-body physical activities. Starting from motion capture data, the framework computes low-level (e.g., a limb velocity) and high-level (e.g., synchronization between different limbs) movement features. Then, this vector of features is integrated to compute a value aiming at providing a quantitative assessment of movement quality, approximating the evaluation an external expert observer would give of the same sequence of movements. Next, a system representing a concrete implementation of the framework is proposed. Karate is adopted as a testbed. We selected two different katas (i.e., detailed choreographies of movements in karate), characterized by different overall attitude and expression (aggressiveness, meditation), and we asked seven athletes, having various levels of experience and age, to perform them. Motion capture data were collected from the performances and were analyzed with the system. The results of the automated analysis were compared with the scores given by fourteen karate experts who rated the same performances. Results show that the movement quality scores computed by the system and the ratings given by the human observers are highly correlated (Pearsons correlations r = 0.84, p = 0.001 and r = 0.75, p = 0.005).
Research impact plays a critical role in evaluating the research quality and influence of a scholar, a journal, or a conference. Many researchers have attempted to quantify research impact by introducing different types of metrics based on citation data, such as h-index, citation count, and impact factor. These metrics are widely used in academic community. However, quantitative metrics are highly aggregated in most cases and sometimes biased, which probably results in the loss of impact details that are important for comprehensively understanding research impact. For example, which research area does a researcher have great research impact on? How does the research impact change over time? How do the collaborators take effect on the research impact of an individual? Simple quantitative metrics can hardly help answer such kind of questions, since more detailed exploration of the citation data is needed. Previous work on visualizing citation data usually only shows limited aspects of research impact and may suffer from other problems including visual clutter and scalability issues. To fill this gap, we propose an interactive visualization tool ImpactVis for better exploration of research impact through citation data. Case studies and in-depth expert interviews are conducted to demonstrate the effectiveness of ImpactVis.