Conventional navigation systems, fixated on metrics such as time and distance, neglect the driver’s emotional well-being, despite driving routes being inherent emotional triggers. This raises a critical question for the Intelligent User Interface community: How can intelligent systems successfully route information based on emotion? To address this gap, we introduce HappyRouting, an empathic car interface designed as an initial attempt to guide drivers through real-world traffic while actively optimizing for positive emotional states. Our core technical contribution is a machine learning-based emotion map layer that predicts the affective valence along various routes using both static and dynamic contextual data. HappyRouting enables the generation of “happy routes” integrated into a functional vehicular interface prototype. We explored the efficacy of this approach in a preliminary, small-scale driving study (N = 13). Our initial findings provide provocative evidence: Emotion-optimized routes successfully increased the subjectively perceived valence by 11% (p =.007) compared to standard routes. Furthermore, despite taking 1.25 times longer on average, participants consistently perceived the travel duration as shorter. This result suggests that integrating emotional optimization could fundamentally challenge the speed-first paradigm. However, recognizing the constraints of our initial, limited sample, we conclude by discussing ethical and computational challenges that must be resolved before emotion-based routing can be safely and scalably integrated into next-generation intelligent navigation apps.

Although the point-and-select interaction method has been shown to lead to user and system-initiated errors, it is still prevalent in VR scenarios.Current solutions to facilitate selection interactions exist, however they do not address the challenges caused by targeting inaccuracy.To reduce the effort required to target objects, we developed a model that quickly detected targeting errors after they occurred.The model used implicit multimodal user behavioral data to identify possible targeting outcomes.Using a dataset composed of 23 participants engaged in VR targeting tasks, we then trained a deep learning model to differentiate between correct and incorrect targeting events within 0.5 seconds of a selection, resulting in an AUC-ROC of 0.9.The utility of this model was then evaluated in a user study with 25 participants that identified that participants recovered from more errors and faster when assisted by the model.These results advance our understanding of targeting errors in VR and facilitate the design of future intelligent error-aware systems.

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.

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.

Driving requires high cognitive capabilities in which drivers need to be able to focus on first-level driving tasks. However, each interaction with the User Interface (UI) system presents a potential distraction. Designing UIs based on insights from field-collected user interaction logs, as well as real-time estimation of the most probable interaction modality, can contribute to engineering focus-supporting UIs. However, the question arises of how user interactions can be predicted in in-the-wild driving scenarios. In this paper, we present HMInference, an automotive machine-learning framework which exploits user interaction log data. HMInference analyzes the interaction sequences of users based on UI domains (e.g., navigation, media, settings) and driving context (e.g., vehicle trajectory) to predict different interaction modalities (e.g., touch, speech). In 10-fold cross-validation, HMInference achieves a mean accuracy of 73.2% (SD: 0.02). Our work advances areas where user interaction prediction for in-car scenarios is required e.g., to enable adaptive system designs.

Despite the advances in machine perception, semantic scene understanding is still a limiting factor in mixed reality scene composition. In this paper, we present TransforMR, a video see-through mixed reality system for mobile devices that performs 3D-pose-aware object substitution to create meaningful mixed reality scenes. In real-time and for previously unseen and unprepared real-world environments, TransforMR composes mixed reality scenes so that virtual objects assume behavioral and environment-contextual properties of replaced real-world objects. This yields meaningful, coherent, and humaninterpretable scenes, not yet demonstrated by today’s augmentation techniques. TransforMR creates these experiences through our novel pose-aware object substitution method building on different 3D object pose estimators, instance segmentation, video inpainting, and pose-aware object rendering. TransforMR is designed for use in the real-world, supporting the substitution of humans and vehicles in everyday scenes, and runs on mobile devices using just their monocular RGB camera feed as input. We evaluated TransforMR with eight participants in an uncontrolled city environment employing different transformation themes. Applications of TransforMR include real-time character animation analogous to motion capturing in professional film making, however without the need for preparation of either the scene or the actor, as well as narrative-driven experiences that allow users to explore fictional parallel universes in mixed reality. We make all of our source code and assets available1.1TransforMR code release: https://github.com/MohamedKari/transformr

Detecting emotions while driving remains a challenge in Human-Computer Interaction. Current methods to estimate the driver’s experienced emotions use physiological sensing (e.g., skin-conductance, electroencephalography), speech, or facial expressions. However, drivers need to use wearable devices, perform explicit voice interaction, or require robust facial expressiveness. We present VEmotion (Virtual Emotion Sensor), a novel method to predict driver emotions in an unobtrusive way using contextual smartphone data. VEmotion analyzes information including traffic dynamics, environmental factors, in-vehicle context, and road characteristics to implicitly classify driver emotions. We demonstrate the applicability in a real-world driving study (N = 12) to evaluate the emotion prediction performance. Our results show that VEmotion outperforms facial expressions by 29% in a person-dependent classification and by 8.5% in a person-independent classification. We discuss how VEmotion enables empathic car interfaces to sense the driver’s emotions and will provide in-situ interface adaptations on-the-go.

Music is a central instrument in video gaming to attune a player’s attention to the current atmosphere and increase their immersion in the game. We transfer the idea of scene-adaptive music to car drives and propose SoundsRide, an in-car audio augmented reality system that mixes music in real-time synchronized with sound affordances along the ride. After exploring the design space of affordance-synchronized music, we design SoundsRide to temporally and spatially align high-contrast events on the route, e. g., highway entrances or tunnel exits, with high-contrast events in music, e. g., song transitions or beat drops, for any recorded and annotated GPS trajectory by a three-step procedure. In real-time, SoundsRide 1) estimates temporal distances to events on the route, 2) fuses these novel estimates with previous estimates in a cost-aware music-mixing plan, and 3) if necessary, re-computes an updated mix to be propagated to the audio output. To minimize user-noticeable updates to the mix, SoundsRide fuses new distance information with a filtering procedure that chooses the best updating strategy given the last music-mixing plan, the novel distance estimations, and the system parameterization. We technically evaluate SoundsRide and conduct a user evaluation with 8 participants to gain insights into how users perceive SoundsRide in terms of mixing, affordances, and synchronicity. We find that SoundsRide can create captivating music experiences and positively as well as negatively influence subjectively perceived driving safety, depending on the mix and user.