arXiv subjects

Albrecht Schmidt

Publications and source records attributed to Albrecht Schmidt.

At least 37 records · Page 2Linked to original sources

arXiv2023

Understanding the Uncertainty Loop of Human-Robot Interaction

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that the human did not expect their response, or even showing the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can, through verbal and non-verbal cues, communicate these uncertainties.

cs.HC↗

arXiv2023

The Walking Talking Stick: Understanding Automated Note-Taking in Walking Meetings

While walking meetings offer a healthy alternative to sit-down meetings, they also pose practical challenges. Taking notes is difficult while walking, which limits the potential of walking meetings. To address this, we designed the Walking Talking Stick -- a tangible device with integrated voice recording, transcription, and a physical highlighting button to facilitate note-taking during walking meetings. We investigated our system in a three-condition between-subjects user study with thirty pairs of participants ($N$=60) who conducted 15-minute outdoor walking meetings. Participants either used clip-on microphones, the prototype without the button, or the prototype with the highlighting button. We found that the tangible device increased task focus, and the physical highlighting button facilitated turn-taking and resulted in more useful notes. Our work demonstrates how interactive artifacts can incentivize users to hold meetings in motion and enhance conversation dynamics. We contribute insights for future systems which support conducting work tasks in mobile environments.

cs.HC↗

arXiv2023

Feeling the Temperature of the Room: Unobtrusive Thermal Display of Engagement during Group Communication

Thermal signals have been explored in HCI for emotion-elicitation and enhancing two-person communication, showing that temperature invokes social and emotional signals in individuals. Yet, extending these findings to group communication is missing. We investigated how thermal signals can be used to communicate group affective states in a hybrid meeting scenario to help people feel connected over a distance. We conducted a lab study (N=20 participants) and explored wrist-worn thermal feedback to communicate audience emotions. Our results show that thermal feedback is an effective method of conveying audience engagement without increasing workload and can help a presenter feel more in tune with the audience. We outline design implications for real-world wearable social thermal feedback systems for both virtual and in-person communication that support group affect communication and social connectedness. Thermal feedback has the potential to connect people across distances and facilitate more effective and dynamic communication in multiple contexts.

cs.HC↗

arXiv2023

Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory

Social media platforms use short, highly engaging videos to catch users' attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment (N=60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (i.e., executing a previously planned action). The study required participants to remember intentions over interruptions. We found that the TikTok condition significantly degraded the users' performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution. We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers to not harm the users' memory and wellbeing.

cs.HC↗

arXiv2022

Supporting Electronics Learning through Augmented Reality

Understanding electronics is a critical area in the maker scene. Many of the makers' projects require electronics knowledge to connect microcontrollers with sensors and actuators. Yet, learning electronics is challenging, as internal component processes remain invisible, and students often fear personal harm or component damage. Augmented Reality (AR) applications are developed to support electronics learning and visualize complex processes. This paper reflects on related work around AR and electronics that characterize open research challenges around the four characteristics functionality, fidelity, feedback type, and interactivity.

cs.HC↗

arXiv2022

Challenges and Opportunities for Inclusive Space Missions

People with impairments are not able to participate in space missions. However, this is not because they cannot; instead, spacecraft have not been designed for them. Therefore, instead of simply excluding people with impairments, they should be included in the design process. This may also help astronauts suffering from fatigue or accidents. Indeed, several impairments can occur due to the effects of microgravity on the body and psychological factors during long space missions. In this paper, we describe the idea of including people with all types of impairments in the design process of interactive space systems to obtain, as a result, systems that can also be used by astronauts suffering from a temporary or situational impairment during a long space flight. To this end, we have described solutions for some types of impairments to ensure the use of interactive systems with permanent, temporary, or situational impairments. The benefits from the participation of people with impairments also bring the idea of inclusion in space a bit closer: supporting people with impairments in space by designing appropriate systems that make space accessible.

cs.HC↗

arXiv2022

EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders

There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant- and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demonstrate that our model is suitable for unsupervised EEG modeling, classification of three distinct emotion categories (positive, neutral, negative) based on the latent representation achieves a robust performance of 68.49%, and generated synthetic EEG sequences resemble real EEG data inputs to particularly reconstruct low-frequency signal components. Our work advances areas where affective EEG representations can be useful in e.g., generating artificial (labeled) training data or alleviating manual feature extraction, and provide efficiency for memory constrained edge computing applications.

cs.LG↗

arXiv2022

Domain-Invariant Representation Learning from EEG with Private Encoders

Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.

cs.LG↗

arXiv2022

Designing Trustworthy User Interfaces for the Voluntary Carbon Market: A Randomized Online Experiment

The voluntary carbon market is an important building block in the fight against climate change. However, it is not trivial for consumers to verify whether carbon offset projects deliver what they promise. While technical solutions for measuring their impact are emerging, there is a lack of understanding of how to translate this data into interface designs that mediate the establishment of trust. With interaction between users and offset projects mainly happening online, it is critical to meet this design challenge. To this end, we designed and evaluated interfaces with varying trust cues for carbon offset projects in a randomized online experiment (n=244). Our results show that content design, particularly financial and forest-related quantitative data presented at the right detail level, increases the perceived trustworthiness, while images have no significant effect. We contribute the first specific guidance for interface designers for carbon offsets and discuss implications for interaction design.

cs.HC↗

arXiv2022

Blockchain and Cryptocurrency in Human Computer Interaction: A Systematic Literature Review and Research Agenda

We present a systematic literature review of cryptocurrency and blockchain research in Human-Computer Interaction (HCI) published between 2014 and 2021. We aim to provide an overview of the field, consolidate existing knowledge, and chart paths for future research. Our analysis of 99 articles identifies six major themes: (1) the role of trust, (2) understanding motivation, risk, and perception of cryptocurrencies, (3) cryptocurrency wallets, (4) engaging users with blockchain, (5) using blockchain for application-specific use cases, and (6) support tools for blockchain. We discuss the focus of the existing research body and juxtapose it to the changing landscape of emerging blockchain technologies to highlight future research avenues for HCI and interaction design. With this review, we identify key aspects where interaction design is critical for the adoption of blockchain systems. Doing so, we provide a starting point for new scholars and designers and help them position future contributions.

cs.HC↗

arXiv2022

Exploiting Multiple EEG Data Domains with Adversarial Learning

Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, (i.e. domain). This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data. Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains (e.g., subjects, sessions, experimental setups) grow massively. We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain-computer interfaces. We unify EEG recordings from different source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP, DREAMER), and demonstrate the feasibility of our invariant representation learning approach in suppressing data-source-relevant information leakage by 35% while still achieving stable EEG-based emotion classification performance.

eess.SP↗

arXiv2022

The Placebo Effect of Artificial Intelligence in Human-Computer Interaction

In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant's own task performance, sustained after interaction. These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.

cs.HC↗

arXiv2021

Remote VR Studies -- A Framework for Running Virtual Reality Studies Remotely Via Participant-Owned HMDs

We investigate the opportunities and challenges of running virtual reality (VR) studies remotely. Today, many consumers own head-mounted displays (HMDs), allowing them to participate in scientific studies from their homes using their own equipment. Researchers can benefit from this approach by being able to reach a more diverse study population and to conduct research at times when it is difficult to get people into the lab (cf. the COVID pandemic). We first conducted an online survey (N=227), assessing HMD owners' demographics, their VR setups, and their attitudes towards remote participation. We then identified different approaches to running remote studies and conducted two case studies for an in-depth understanding. We synthesize our findings into a framework for remote VR studies, discuss the strengths and weaknesses of the different approaches, and derive best practices. Our work is valuable for HCI researchers conducting VR studies outside labs.

cs.HC↗

arXiv2020

Enabling Tangible Interaction through Detection and Augmentation of Everyday Objects

Digital interaction with everyday objects has become popular since the proliferation of camera-based systems that detect and augment objects "just-in-time". Common systems use a vision-based approach to detect objects and display their functionalities to the user. Sensors, such as color and depth cameras, have become inexpensive and allow seamless environmental tracking in mobile as well as stationary settings. However, object detection in different contexts faces challenges as it highly depends on environmental parameters and the conditions of the object itself. In this work, we present three tracking algorithms which we have employed in past research projects to track and recognize objects. We show, how mobile and stationary augmented reality can be used to extend the functionalities of objects. We conclude, how common items can provide user-defined tangible interaction beyond their regular functionality.

cs.HC↗

arXiv2020

SAFER: Development and Evaluation of an IoT Device Risk Assessment Framework in a Multinational Organization

Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity.

cs.CR↗

arXiv2019

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

cs.LG↗

arXiv2019

Designing for Reproducibility: A Qualitative Study of Challenges and Opportunities in High Energy Physics

Reproducibility should be a cornerstone of scientific research and is a growing concern among the scientific community and the public. Understanding how to design services and tools that support documentation, preservation and sharing is required to maximize the positive impact of scientific research. We conducted a study of user attitudes towards systems that support data preservation in High Energy Physics, one of science's most data-intensive branches. We report on our interview study with 12 experimental physicists, studying requirements and opportunities in designing for research preservation and reproducibility. Our findings suggest that we need to design for motivation and benefits in order to stimulate contributions and to address the observed scalability challenge. Therefore, researchers' attitudes towards communication, uncertainty, collaboration and automation need to be reflected in design. Based on our findings, we present a systematic view of user needs and constraints that define the design space of systems supporting reproducible practices.

cs.HC↗

arXiv2019

Gamification in Science: A Study of Requirements in the Context of Reproducible Research

The need for data preservation and reproducible research is widely recognized in the scientific community. Yet, researchers often struggle to find the motivation to contribute to data repositories and to use tools that foster reproducibility. In this paper, we explore possible uses of gamification to support reproducible practices in High Energy Physics. To understand how gamification can be effective in research tools, we participated in a workshop and performed interviews with data analysts. We then designed two interactive prototypes of a research preservation service that use contrasting gamification strategies. The evaluation of the prototypes showed that gamification needs to address core scientific challenges, in particular the fair reflection of quality and individual contribution. Through thematic analysis, we identified four themes which describe perceptions and requirements of gamification in research: Contribution, Metrics, Applications and Scientific practice. Based on these, we discuss design implications for gamification in science.

cs.HC↗