Coding the gestures people use when interacting with tangible devices can aid interface design by making interfaces more intuitive and consistent. Past work devotes very little space to developing coding schemes for user-defined gesture sets. A generalizable coding scheme for gestures made by the human hand is developed for tangible devices using inspiration from predetermined time systems (PTS) from human factors engineering. The coding scheme is generic, adaptable to any hand motion proposal and has features such as duration invariance. Case study examples demonstrate its ability to provide consistent labels for hand interactions with tangible devices. Coding scheme labels are also balanced, having neither too many nor too few distinct gestures. This paper can help teach and create coding schemes for gesture elicitation studies performed by human-computer interaction researchers. The larger societal impact of this work is to help advance scientific methods for beneficial interface design within HCI/HRI by combination with human factors theory.

This work focuses on the effect of voice familiarity in voice reminders between elders and their caregivers. A voice application (for desktops and smartphones) was created to study these effects. Seniors, and their family care providers along with medical providers were consulted for voice application design and improvement opportunities using two user tests with 17 care dyads and 15 medical care providers. Results from qualitative content analysis show that care dyads generally prefer familiar voice reminders for routine tasks (such as taking medication) and generally find caregiver-voiced reminders to be acceptable, while medical care providers have mixed opinions about older adults using recorded reminders.

We review HCI history from both the perspective of its 1980s split with human factors and its nature as a discipline. We then revisit human augmentation as an alternative to user friendliness that seems particularly relevant in the areas of inclusive design and artificial intelligence. Viewing human-AI interaction as a kind of human augmentation raises issues such as how to promote trust and situation awareness. We also pose the question: Can HCI and human factors engineering work together to solve the increasingly urgent challenges of human-AI technology? In an initial look at this question, we contrast the different approaches of HCI and human factors on emerging AI research. This article concludes by considering other potentially promising paths for HCI. We propose more collaboration between HCI and human factors, or related disciplines, in the future to address the massive challenges posed by the rapid growth in data science and artificial intelligence.

Audio messaging and voice-based interactions are growing in popularity. Lexical features of a manually-curated dataset of real-world audio tweets, as well as text and video/image tweets from the same user accounts, are analyzed to explore how user-generated audio differs from text. The toxicity, sentiment, topic and length of audio tweet transcripts are compared with their accompanying text, date-matched text tweets from the same users and date-matched video/image tweets and their accompanying text. Audio tweets were significantly less toxic than both text tweets and text that accompanied the audio tweet, as well as significantly lower sentiment than their accompanying text. The topics and word counts of audio, text and video/image tweets also differed. These findings are then used to derive design implications for audio and conversational agent interaction. This research contributes preliminary insights about audio social media messages that may help researchers and designers of audio- and agent-based interaction better understand and design for different media formats.

Aerial manipulation systems are an emerging subclass of unmanned aerial vehicles (UAVs or “drones”) that use a mobile arm to manipulate their environment. Public opinion is an important consideration for drones, but past public opinion polls have focused on drones without attached arms and have relied on text-based surveys. Study 1 (N= 190) assessed participants’ perceived benefit, risk, and acceptance of aerial manipulation systems across five applications, using an animation-based online public opinion survey. Study 2 (N= 194) assessed the influence of stimulus sampling on public opinion of aerial manipulation systems by replicating Study 1 using an alternative set of animations. Study 3 (N= 396) assessed the influence of using animations versus a text control on perceptions of the poll and aerial manipulation systems, as well as the influence of survey platform (YouGov Direct versus Mechanical Turk). Results show that delivery applications are perceived as more beneficial than several other applications; that people’s opinions and imagining of drones were different if they watched animations versus read text; and that stimulus and population sampling influence the results of public opinion polls about drones.

Adolescents with depression who participate in behavioral activation therapy may find it hard to be motivated to perform tasks at home that their therapists recommend. We describe the initial design and usability evaluation of a home device (“PlantBot”) that could be used to remind young adults with depression at home of their behavioral activation therapy-related tasks. The prototype features electronics in a two-layer base, with a fake plant on top and supported using the Amazon Echo voice agent. We use an online panel study to evaluate the usability of our system with youth with past depression (N= 30). Initial findings highlight the device’s usability, potential benefit, and attractiveness of the plant component, as well as multiple improvements to be made.

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.

A growing number of studies use a "ghost-driver" vehicle driven by a person in a car seat costume to simulate an autonomous vehicle. Using a hidden-driver vehicle in a field study in the Netherlands, Study 1 (N = 130) confirmed that the ghostdriver methodology is valid in Europe and confirmed that European pedestrians change their behavior when encountering a hidden-driver vehicle. As an important extension to past research, we find pedestrian group size is associated with their behavior: groups look longer than singletons when encountering an autonomous vehicle, but look for less time than singletons when encountering a normal vehicle. Study 2 (N = 101) adapted and extended the hidden-driver method to test whether it is believable as online video stimuli and whether car characteristics and participant feelings are related to the beliefs and behavior of pedestrians who see hidden-driver vehicles. As expected, belief rates were lower for hidden-driver vehicles seen in videos compared to in a field study. Importantly, we found noticing no driver was the only significant predictor of belief in car autonomy, which reinforces prior justification for the use of the ghostdriver method. Our contributions are a replication of the hidden-driver method in Europe and comparisons with past US and Mexico data; an extension and evaluation of the ghostdriver method in video form; evidence of the necessity of the hidden driver in creating the illusion of vehicle autonomy; and an extended analysis of how pedestrian group size and feelings relate to pedestrian behavior when encountering a hidden-driver vehicle.

HRI researchers have explored how people behave toward technology agents, advancing the concept that people can attain “closeness” with technology itself in addition to a living social partner. Yet the topic of closeness with robots has not been fully explored or organized into a discrete area of study. This seems particularly important to the design of robots that are expressive, to the implementation of technologies that use new social signal processing or reciprocal social touch, and to the study of how people respond to robots. This half-day workshop is a forum to discuss the future of “closeness” with robots, conversational agents, autonomous vehicles, Internet of Things devices and other technologies that act as social partners-designs, applications, responses and societal concerns.

Dominance is a key aspect of interpersonal relationships. To what extent do nonverbal indicators related to dominance status translate to a nonanthropomorphic robot? An experiment ( N = 25) addressed whether a mobile robot's motion style can influence people's perceptions of its status. Using concepts from improv theater literature, we developed two motion styles across three scenarios (robot makes lateral motions, approaches, and departs) to communicate a robot's dominance status through nonverbal expression. In agreement with the literature, participants described a motion style that was fast, in the foreground, and more animated as higher status than a motion style that was slow, in the periphery, and less animated. Participants used fewer negative emotion words to describe the robot with the purportedly high-status movements versus the purportedly low-status movements, but used more negative emotion words to describe the robot when it made departing motions that occurred in the same style. This result provides evidence that guidelines from improvisational theater for using nonverbal expression to perform interpersonal status can be applied to influence perception of a nonanthropomorphic robot's status, thus suggesting that useful models for more complicated behaviors might similarly be derived from performance literature and theory.

Researchers and designers of in-vehicle interactions and interfaces currently have to choose between performing evaluation and human factors experiments in laboratory driving simulators or on-road experiments. To enjoy the benefit of customizable course design in controlled experiments with the immediacy and rich sensations of on-road driving, we have developed a new method and tools to enable VR driving simulation in a vehicle as it travels on a road. In this paper, we describe how the cost-effective and flexible implementation of this platform allows for rapid prototyping. A preliminary pilot test (N = 6), centered on an autonomous driving scenario, yields promising results, illustrating proof of concept and indicating that a basic implementation of the system can invoke genuine responses from test participants.

One of the major reasons behind traffic accidents is misinterpretation among road users. Self-driving vehicles are expected to reduce these accidents, given that they are designed with all road users in mind. Recently, research on the design of vehicle-pedestrian communication has emerged, but to our knowledge, there is no research published that investigates the design of interfaces for intent communication towards child pedestrians. This paper reports the initial steps towards the examination of children's views and understandings about the appearance and intention communication of self-driving vehicles. It adopts a design inclusive methodological approach for the development of a prototype for the communication of two basic intentions: "I am going to stop" and "I am going to proceed". The initial results indicate children's need to be aware about the autonomy of the vehicle and the use of their previous experience with traffic signs for the interpretation of communicative signs of the vehicle.

In this note, we present minimal robot movements for robotic technology for children. Two types of minimal gaze movements were designed: social-gaze movements to communicate social engagement and deictic-gaze movements to communicate task-related referential information. In a two (social-gaze movements vs. none) by two (deictic-gaze movements vs. none) video-based study (n=72), we found that social-gaze movements significantly increased children's perception of animacy and likeability of the robot. Deictic-gaze and social-gaze movements significantly increased children's perception of helpfulness. Our findings show the compelling communicative power of social-gaze movements, and to a lesser extent deictic-gaze movements, and have implications for designers who want to achieve animacy, likeability and helpfulness with simple and easily implementable minimal robot movements. Our work contributes to human-robot interaction research and design by providing a first indication of the potential of minimal robot movements to communicate social engagement and helpful referential information to children.

Developing systems that motivate people to change their behaviors, such as an exercise application for the smartphone, is challenging. One solution is to implement motivational strategies from existing behavior change theory and tailor these strategies to preferences based on personal characteristics, like personality and gender. We operationalized strategies by collecting representative motivational text messages and aligning the messages to ten theory-based behavior change strategies. We conducted an online survey with 350 participants, where the participants rated 50 of our text messages (each aligned to one of the ten strategies) on how motivating they found them. Results show that differences in personality and gender relate to significant differences in the evaluations of nine out of ten strategies. Eight out of ten strategies were perceived as either more or less motivating in relation to scores on the personality traits Openness, Extraversion, and Agreeableness. Four strategies were perceived as more motivating by men than by women. These findings show that personality and gender influence how motivational strategies are perceived. We conclude that our theory-based behavior change strategies can be more motivating by tailoring them to personality and gender of users of behavior change systems.

A large literature describes the use of robots' physical bodies to support communication with people. Touch is a natural channel for physical interaction, yet it is not understood how principles of interpersonal touch might carry over to a robot. Exploring how interpersonal rules surrounding body accessibility and touch apply to a robot is critical toward understanding the extent to which people treat the act of touching body regions as a sign of closeness---even if the body belongs to a robot---and is important to the field of humanoid social robotics. Thirty-one students participated in an interactive anatomy lesson with a small, humanoid robot. Participants either touched or pointed to an anatomical region of the robot in each of 26 trials while their skin conductance response was measured. Touching less accessible regions of a robot's body (e.g., its buttocks and genitals) was more physiologically arousing than touching more accessible regions (e.g., its hands and feet). No differences in physiological arousal were found when just pointing to those same anatomical regions. A social robot elicited tactile responses in human physiology, a result that signals people treat touching body parts as an act of closeness in itself that does not require a human recipient. The power of touching a humanoid body with identifiable body parts should caution mechanical and interaction designers about the positive and negative effects of human-robot interaction.

How people respond to social robots as new interactive media is an important consideration for their design and implementation. A series of experimental studies uses exploratory assessment to evaluate three applications of social robots: pedagogical social robots that help deliver online content; conversational social robots that speak with people; and robotic furniture that move around the room. My overall goal is to explore how a robot's social status influences participant response and the limits of that response. This research provides concrete suggestions for robot design and technology assessment.

Instructional video content is being created in many different languages. A robot is a generic interface that can deliver translated lecture content in a person's space. In an exploratory study, 40 participants viewed a lecture delivered by a robot lecturer that was either located in front of them or displayed on a screen, in either a real-world environment or while wearing an immersive virtual reality headset. Initial results did not find differences in test performance or in how much participants liked a robot lecturer that was in front of them compared to on a screen, perhaps because the robot spoke the lecture but did not interact with the person.

How will pedestrians and bicyclists interact with autonomous vehicles when there is no human driver? In this paper, we outline a novel method for performing observational field experiments to investigate interactions with driverless cars. We provide a proof-of-concept study (N=67), conducted at a crosswalk and a traffic circle, which applies this method. In the study, participants encountered a vehicle that appeared to have no driver, but which in fact was driven by a human confederate hidden inside. We constructed a car seat costume to conceal the driver, who was specially trained to emulate an autonomous system. Data included video recordings and participant responses to post-interaction questionnaires. Pedestrians who encountered the car reported that they saw no driver, yet they managed interactions smoothly, except when the car misbehaved by moving into the crosswalk just as they were about to cross. This method is the first of its kind, and we believe that it contributes a valuable technique for safely acquiring empirical data and insights about driverless vehicle interactions. These insights can then be used to design vehicle behaviors well in advance of the broad deployment of autonomous technology.

How people view relationships between humans and robots is an important consideration for the design and acceptance of social robots. Two studies investigated the effect of relational behavior in a human-robot dyad. In Study 1, participants watched videos of a human confederate discussing the Desert Survival Task with either another human confederate or a humanoid robot. Participants were less trusting of both the robot and the person in a human-robot relationship where the robot was dominant toward the person than when the person was dominant toward the robot; these differences were not found for a human pair. In Study 2, participants watched videos of a human confederate having an everyday conversation with either another human confederate or a humanoid robot. Participants who saw a confederate mirror the gestures of a robot found the robot less attractive than when the robot mirrored the confederate; the opposite effect was found for a human pair. Exploratory findings suggest that human-robot relationships are viewed differently than human dyads.

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.

This research agenda aims to understand how people treat robots along two dialectics. In the mechanical-living dialectic, fabricated entities are assessed against their organic counterparts to see if people respond differently to robots versus other people. Multiple experiments are conducted that compare human-robot relationships to human-human relationships by manipulating roles in videos of dyadic conversations shown to participants. In the physical-digital dialectic, a physically embodied robotic agent is compared to either a digitally-presented virtual agent (such as an animated character on a computer screen) or a digitally-presented robotic agent (such as a live video feed of the robot). The role of physical and digital embodiment and display medium are explored through a comprehensive survey and analysis of existing experimental works comparing physical and digital agents. Key research questions, related work, scope, research approach, current findings and remaining work are outlined.