Companies like Meta, Apple, Microsoft, Neuralink, and emerging startups are rapidly developing physiological computing devices that read muscle, brain, and/or eye movements. This industry boom creates urgent demand for professionals trained in neural interfaces and physiological computing, yet most computing programs lack standardized curricula in this area. This BOF will discuss two critical questions: How can we develop a framework for standardized physiological computing courses across computing curricula? What training and resources do faculty need to confidently teach these interdisciplinary topics?

This report presents several main collaborative design scenarios involved in designing a week-long summer camp program to engage elementary students in physiological computing. The design team consists of the research team (the authors of this report) and four elementary school teachers from a school district with a majority of historically underrepresented students in a southeastern state in the U.S. We present the goals for the co-design, followed by an account of the nature of the conversations and discussions during the synchronous co-design sessions. With the research team leading with questions, teachers contributed with abundant knowledge and experience with pedagogies to engage upper elementary-grade students, including activities that relate to students' everyday learning, scaffolding strategies to bridge potential learning gaps, and suggestions for software development and updates. These insights are expected to help enable adoptions by researchers and practitioners in collaborative design for K-12 students.

Previous research has incorporated physiological data such as heart rate and footsteps to enrich K-12 students' STEM and computing learning experiences. This qualitative study piloted a series of lessons leveraging a novel physiological computing environment with a small group of fifth-grade girls (n=5). The purpose of this study was to understand (1) how the physiological computing lesson activities promoted changes in the students' conceptual understanding of conditional logic and variables and their diverse perspectives in computing and (2) how the students approached problem-solving and what their visual attention looked like during physiological computing. We analyzed multiple sources of data, including students' artifacts, recorded classroom conversations, think-aloud verbalizations, and eye-tracking metrics data. Data analyses revealed that the girls demonstrated an improved understanding of the two computing concepts (i.e., variables and conditional logic), employed different problem-solving strategies, and encountered common challenges such as translating the task instruction to building code with the conditional block. Eye-tracking revealed that the students rarely attended to program output during their programming and debugging processes.

Educational sensor-based visual programming environments (VPEs) tend to focus on directly connecting input and output sources. However, limited educational tools are designed to expose novice programmers to basic preprocessing techniques often needed to convert raw sensor data into interpretable commands. This paper presents our preliminary design process for LITI, a VPE designed to expose students to basic processing techniques used for time-series data analysis. We leveraged an iterative design process that began with focus group sessions involving local high school teachers and students. Based on educators' and students' feedback, LITI was designed to reinforce graphing and data processing concepts used in physiological computing. Afterward, we conducted usability studies with high school students in grades 10 - 12. Results from our initial user studies suggest that LITI may increase students' physiological computing self-efficacy.

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, deaf and hard-of-hearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This article considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RF-sensor-based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and three gross motor activities.

The human sense of smell is a primal ability that has the potential to reveal unexplored relationships between user behaviors and technology. Humans use millions of olfactory receptor cells to observe the environment around them. Olfaction studies are gaining popularity with the progression of scent delivering (commercial and prototype) devices. This influx of research features various software and hardware designs. Additionally, previous studies have explored numerous target audiences and evaluation methodologies. This article presents a systematic review of pertinent literature that investigates olfactory-based computing (OBC) systems in the field of Human-Computer Interaction. Last, this article highlights state-of-the-art study/system designs, evaluation methods, and offers insights on ways to address current challenges/contributions relevant to OBC technologies.

Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate.

In the data age, everyday devices and applications implement machine learning (ML) to enhance user experiences. However, everyday users usually do not completely understand the technology. Moving forward with ML education will require support for new forms of digital literacies involving machine learning. Physiological computing and gamification techniques can present engaging opportunities for dynamic personally-relevant data collection and manipulation. Our research proposes a system design that applies both real-time physiological data and gamification elements to provide novice users the opportunity to learn about ML concepts.

Software is a critical component of brain-computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.

Previous human-robot interaction (HRI) research has shown that trust, disclosure, and companionship may be influenced by a robot's verbal behavior. Measures used to interpret these key aspects of HRI commonly include surveys, observations, and user interviews. In this preliminary work, we aim to extend previous research by exploring the use of electroencephalography (EEG) to augment our understanding of participants' responses to vulnerable robot behaviors. We tested this method by obtaining EEG data from participants while they interacted with a robotic tutor. The robotic tutor was designed to exhibit high vulnerability (HV) or low vulnerability (LV) behaviors similar to a previous HRI study. Our preliminary results show that event-related potentials (ERPs) may provide insights into participants' early affective processing of vulnerable robot behaviors.

Brain-Computer Interface (BCI) hardware is becoming more affordable and accessible. However, there is limited work investigating ways to design software that broadens participation with BCI technology. In this article, we present a block-based programming environment designed to assist novice programmers with creating BCI applications. We also discuss learning barriers encountered by novice programmers developing neurofeedback applications. Our findings suggest that visual programming assists novice programmers with building basic BCI applications; however, students may experience understanding and learning barriers initially.

As CS + X courses become more common, it is important for us to investigate ways to leverage interdisciplinary learning tools to expand the types of experiences available to students. This paper discusses our experiences introducing CS undergraduates to basic Brain-Computer Interface (BCI) concepts using NeuroBlock. Neuroblock is a visual programming environment that allows users to build applications driven by near-real-time neurophysiological (i.e., brainwaves) data. Brainwave data is captured using a commercial-grade BCI device. Students use brainwave data from the BCI device to create interactive hybrid-BCI applications (e.g., games) featuring objects that respond to students' affective states (e.g. engagement, relaxation, and attention) and keyboard events. In this paper, we describe NeuroBlock, three example activities, and results from an exploratory empirical study that suggests exposure to NeuroBlock increased students' confidence in their ability to develop applications that leverage neurophysiological signals. NeuroBlock and the discussed activities have the potential to supplement future CS + X courses by providing students hands-on experiences with emerging physiological devices.

Brain-Computer Interface (BCI) applications are gaining popularity as Electroencephalography (EEG) hardware becomes more accessible. BCI technology is used for various purposes such as neurophysiological evaluation, device control, user-state monitoring, and cognitive improvement. Although BCI software platforms exist, there are few systems designed to assist novice programmers with creating BCI applications. We present “NeuroBlock”, a block-based programming approach to neurofeedback application development. Motivated by insights presented in BCI and visual languages literature, our system enables novice programmers to build applications that adapt to users' affective state. We evaluated the system's appropriateness by tasking novice programmers with developing neurofeedback applications inspired by previous BCI studies. Our exploratory study with 40 participants demonstrates that novice programmers are capable of developing neurofeedback applications using NeuroBlock. Furthermore, participants had a positive perception of NeuroBlock's usability.

Homeland safety and security will increasingly depend upon autonomous unmanned vehicles as a method of assessing and maintaining situational awareness. As autonomous team algorithms evolve toward requiring less human intervention, it may be that having an “operator-in-the-loop” becomes the ultimate goal in utilizing autonomous teams for surveillance. However studies have shown that trust plays a factor in how effectively an operator can work with autonomous teammates. In this work, we study mechanisms that look at autonomy as a system and not as the sum of individual actions. First, we conjecture that if the operator understands how the team autonomy is designed that the user would better trust that the system will contribute to the overall goal. Second, we focus on algorithm input criteria as being linked to operator perception and trust. We focus on adding a time-varying spatial projection of areas in the ROI that have been unseen for more than a set duration (STEC). Studies utilize a custom test bed that allows users to interact with a surveillance team to find a target in the region of interest. Results show that while algorithm training had an adverse effect, projecting salient team/surveillance state had a statistically significant impact on trust and did not negatively affect workload or performance. This result may point at a mechanism for improving trust through visualizing states as used in the autonomous algorithm.