Generative AI enables designers to more broadly and rapidly explore design spaces. However, the sheer scale at which designs can be generated makes it difficult to identify which generated concepts deserve further attention. To support this identification process, we explore pupillometry—specifically pupil dilation—as a support signal for design evaluation. We conducted a study with 40 participants who viewed AI-generated bicycle designs while wearing eye tracking glasses to measure pupil dilation and rated designs on perceived surprise, valence, and feasibility.

With advances in electronic-skin and wearable technologies, it is possible to continuously measure stress markers from the skin and sweat to monitor and improve wellbeing and health. Understandably, the sensor’s engineering and resolution are important towards its function. However, we find that people looking for an e-skin stress sensor may look beyond measurement precision, demanding a private and stealth design to reduce, for example, social stigmatization. We introduce the idea of a stress sensing "wear index," created from the combination of human-centered design (n=24), physiological (n=10), and biochemical (n=16) data. This wear index can inform the design of stress wearables to fit specific applications, e.g., human factors may be relevant for a wellbeing application, versus a relapse prevention application that may require more sensing precision. Our wear index idea can be further generalized as a method to close gaps between design and engineering practices.

Modern stress management techniques have been shown to be effective, particularly when applied systematically and with the supervision of an instructor. However, online workers usually lack sufficient support from therapists and learning resources to self-manage their stress. To better assist these users, we implemented a browser-based application, Home Sweet Office (HSO), to administer a set of stress micro-interventions which mimic existing therapeutic techniques, including somatic, positive psychology, meta cognitive, and cognitive behavioral categories. In a four-week field study, we compared random and machine-recommended interventions to interventions that were self-proposed by participants in order to investigate effective content and recommendation methods. Our primary findings suggest that both machine-recommended and self-proposed interventions had significantly higher momentary efficacy than random selection, whereas machine-recommended interventions offer more activity diversity compared to self-proposed interventions. We conclude with reflections on these results, discuss features and mechanisms which might improve efficacy, and suggest areas for future work.

During the last two decades, a small set of distributed computing models for networks have emerged, among which LOCAL, CONGEST, and Broadcast Congested Clique (BCC) play a prominent role. We consider hybrid models resulting from combining these three models. That is, we analyze the computing power of models allowing to, say, perform a constant number of rounds of CONGEST, then a constant number of rounds of LOCAL, then a constant number of rounds of BCC, possibly repeating this figure a constant number of times. We specifically focus on 2-round models, and we establish the complete picture of the relative powers of these models. That is, for every pair of such models, we determine whether one is (strictly) stronger than the other, or whether the two models are incomparable. The separation results are obtained by approaching communication complexity through an original angle, which may be of an independent interest. The two players are not bounded to compute the value of a binary function, but the combined outputs of the two players are constrained by this value. In particular, we introduce the XOR-Index problem, in which Alice is given a binary vector x ∈ {0,1}ⁿ together with an index i ∈ [n], Bob is given a binary vector y ∈ {0,1}ⁿ together with an index j ∈ [n], and, after a single round of 2-way communication, Alice must output a boolean out_A, and Bob must output a boolean out_B, such that out_A ∧ out_B = x_j⊕ y_i. We show that the communication complexity of XOR-Index is Ω(n) bits.

In this paper, we study how a co-located robot affects human motivation and emotion. In particular, we examine the role of the co-located robot’s anthropomorphism, as well as the effects of the human’s personality and gender. To study this, we conducted an online experiment, where 182 participants completed a repetitive task, either easy or hard, in one of the four conditions: in the presence of a non-anthropomorphic robot, an anthropomorphic robot, another human, or alone. For each condition, we analyzed the number of repetitions and the total time users spent, which we treated as the proxy of their motivation, as well as their self-reported emotional states. The study results suggest that the presence of a non-anthropomorphic robot has the potential to lead to a higher level of motivation and a more desirable affective state for users than the presence of an anthropomorphic robot or another human, especially for introverts and female users during difficult tasks.

During the last two decades, a small set of distributed computing models for networks have emerged, among which LOCAL, CONGEST, and Broadcast Congested Clique (BCC) play a prominent role. We consider hybrid models resulting from combining these three models. That is, we analyze the computing power of models allowing to, say, perform a constant number of rounds of CONGEST, then a constant number of rounds of LOCAL, then a constant number of rounds of BCC, possibly repeating this figure a constant number of times. We specifically focus on 2-round models, and we establish the complete picture of the relative powers of these models. That is, for every pair of such models, we determine whether one is (strictly) stronger than the other, or whether the two models are incomparable.

Stress affects the lives of millions of people every day. In-situ sensing could enable just-in-time stress management interventions. We present the first work to detect stress using the movements of a car's existing steering wheel. We extend prior work on PC peripherals and demonstrate that stress, expressed through muscle tension in the limbs, can be measured through the way we drive a car. We collected data in a driving simulator under controlled circumstances to vary the levels of induced stress, within subjects. We analyze angular displacement data to estimate coefficients related to muscle tension using an inverse filtering technique. We prove that the damped frequency of a mass spring damper model representing the arm is significantly higher during stress. Stress can be detected with only a few turns during driving. We validate these measures against a known stressor and calibrate our sensor against known stress measurements.

We introduce Inquire, a tool designed to enable qualitative exploration of utterances in social media and large-scale texts. As opposed to keyword search, Inquire allows the effective use of sentences as queries to quickly explore millions of documents to retrieve semantically-similar sentences. We apply Inquire to LiveJournal.com (LJ) database, which contains millions of personal diaries, and we use semantic embeddings trained in LJ or Google News (GN) datasets. We present the system design through iterative evaluations with qualitative researchers. We show how queries become a part of the inductive process, enabling researchers to try multiple ideas while gaining intuition and discovering less-obvious insights. We discuss the choice of LJ as a rich source of public posts, the preference for GN embeddings which link formal language (e.g. "reminiscence triggers") with colloquial expressions (e.g. "music brings back memories"), the interplay between tool and user, and potential qualitative and social research opportunities.

We fuse science and design thinking to create a novel, IoT interactive urban lights system focused on increasing positive affect among pedestrians. Our contributions are three-fold. First, the design, construction, and evaluation of an efficient interactive lighting system focused on well-being, as opposed to systems focused on utility or landscaping. Second, we used scientific methods to discover basic design parameters for affective outcomes. Third, we optimized user experiences for low energy profiles, positive affect, and interactivity. Tested interactions show positive and some unexpected negative responses. Optimal interactive designs cut energy consumption by 75% while maintaining positive affect. Furthermore, card sorting design exercises revealed an inverse relationship between perceived pleasant feelings and interactivity. We conclude by discussing the implications of our research for the design of coherent, attractive, and efficient urban lighting.

Little is known about the affective expressivity of multisensory stimuli in wearable devices. While the theory of emotion has referenced single stimulus and multisensory experiments, it does not go further to explain the potential effects of sensorial stimuli when utilized in combination. In this paper, we present an analysis of the combinations of two sensory modalities - haptic (more specifically, vibrotactile) stimuli and auditory stimuli. We present the design of a wrist-worn wearable prototype and empirical data from a controlled experiment (N=40) and analyze emotional responses from a dimensional (arousal + valence) perspective. Differences are exposed between “matching” the emotions expressed through each modality, versus "mixing" auditory and haptic stimuli each expressing different emotions. We compare the effects of each condition to determine, for example, if the matching of two negative stimuli emotions will render a higher negative effect than the mixing of two mismatching emotions. The main research question that we study is: When haptic and auditory stimuli are combined, is there an interaction effect between the emotional type and the modality of the stimuli? We present quantitative and qualitative data to support our hypotheses, and complement it with a usability study to investigate the potential uses of the different modes. We conclude by discussing the implications for the design of affective interactions for wearable devices.

Recognizing when computer users are stressed can help reduce their frustration and prevent a large variety of negative health conditions associated with chronic stress. However, measuring stress non-invasively and continuously at work remains an open challenge. This work explores the possibility of using a pressure-sensitive keyboard and a capacitive mouse to discriminate between stressful and relaxed conditions in a laboratory study. During a 30 minute session, 24 participants performed several computerized tasks consisting of expressive writing, text transcription, and mouse clicking. During the stressful conditions, the large majority of the participants showed significantly increased typing pressure (>79% of the participants) and more contact with the surface of the mouse (75% of the participants). We discuss the potential implications of this work and provide recommendations for future work.

Stress causes and exacerbates many physiological and mental health problems. Routine and unobtrusive monitoring of stress would enable a variety of treatments, from break-taking to calming exercises. It may also be a valuable tool for assessing effects (frustration, difficulty) of using interfaces or applications. Custom sensing hardware is a poor option, because of the need to buy/wear/use it continuously, even before stress-related problems are evident. Here we explore stress measurement from common computer mouse operations. We use a simple model of arm-hand dynamics that captures muscle stiffness during mouse movement. We show that the within-subject mouse-derived stress measure is quite strong, even compared to concurrent physiological sensor measurements. While our study used fixed mouse tasks, the stress signal was still strong even when averaged across widely varying task geometries. We argue that mouse sensing "in the wild" may be feasible, by analyzing frequently-performed operations of particular geometries.