Affective Interaction in Smart Environments

AbstractWe present a concept where the smart environments of the future will be able to provide ubiquitous affective communication. All the surfaces will become interactive and the furniture will display emotions. In particular, we present a first prototype that allows people to share their emotional states in a natural way. The input will be given through facial expressions and the output will be displayed in a context-aware multimodal way. Two novel output modalities are presented: a robotic painting that applies the concept of affective communication to the informative art and an RGB lamp that represents the emotions remaining in the user's peripheral attention. An observation study has been conducted during an interactive event and we report our preliminary findings in this paper

Internet of tangibles:Exploring the interaction-attention continuum

There is an increasing interest in the HCI research community to design richer user interactions with the Internet of Things (IoT). This studio will allow exploring the design of tangible interaction with the IoT, what we call Internet of Tangibles. In particular, we aim at investigating the full interaction-attention continuum, with the purpose of designing IoT tangible interfaces that can switch between peripheral interactions that do not disrupt everyday routines, and focused interactions that support user's reflections. This investigation will be conducted through hands-on activities where participants will prototype tangible IoT objects, starting by a paper prototyping phase, supported by design cards, and followed by an Arduino prototype phase. The purpose of the studio is also establishing a community of researchers and practitioners, from both academy and industry, interested in the field of tangible interaction with the Internet of Things

Matching optical flow to motor speed in virtual reality while running on a treadmill

We investigated how visual and kinaesthetic/efferent information is integrated for speed perception in running. Twelve moderately trained to trained subjects ran on a treadmill at three different speeds (8, 10, 12 km/h) in front of a moving virtual scene. They were asked to match the visual speed of the scene to their running speed–i.e., treadmill’s speed. For each trial, participants indicated whether the scene was moving slower or faster than they were running. Visual speed was adjusted according to their response using a staircase until the Point of Subjective Equality (PSE) was reached, i.e., until visual and running speed were perceived as equivalent. For all three running speeds, participants systematically underestimated the visual speed relative to their actual running speed. Indeed, the speed of the visual scene had to exceed the actual running speed in order to be perceived as equivalent to the treadmill speed. The underestimation of visual speed was speed-dependent, and percentage of underestimation relative to running speed ranged from 15% at 8km/h to 31% at 12km/h. We suggest that this fact should be taken into consideration to improve the design of attractive treadmill-mediated virtual environments enhancing engagement into physical activity for healthier lifestyles and disease prevention and care

Automated prediction of crack propagation using H2O AutoML

Crack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications. The accurate prediction of crack propagation behavior is paramount for ensuring the performance and durability of engineering components, as extensively explored in prior research. Nevertheless, there is a pressing demand for automated models capable of efficiently and precisely forecasting crack propagation. In this study, we address this need by developing a machine learning-based automated model using the powerful H2O library. This model aims to accurately predict crack propagation behavior in various materials by analyzing intricate crack patterns and delivering reliable predictions. To achieve this, we employed a comprehensive dataset derived from measured instances of crack propagation in Acrylonitrile Butadiene Styrene (ABS) specimens. Rigorous evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, were applied to assess the model’s predictive accuracy. Cross-validation techniques were utilized to ensure its robustness and generalizability across diverse datasets. Our results underscore the automated model’s remarkable accuracy and reliability in predicting crack propagation. This study not only highlights the immense potential of the H2O library as a valuable tool for structural health monitoring but also advocates for the broader adoption of Automated Machine Learning (AutoML) solutions in engineering applications. In addition to presenting these findings, we define H2O as a powerful machine learning library and AutoML as Automated Machine Learning to ensure clarity and understanding for readers unfamiliar with these terms. This research not only demonstrates the significance of AutoML in future-proofing our approach to structural integrity and safety but also emphasizes the need for comprehensive reporting and understanding in scientific discourse

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving

Effect of Obstacle Type and Cognitive Task on Situation Awareness and Takeover Performance in Conditionally Automated Driving

In conditionally automated driving, several factors can affect the driver’s situation awareness and ability to take over control. To better understand the influence of some of these factors, 88 participants spent 20 minutes in a conditionally automated driving simulator. They had to react to four obstacles that varied in danger and movement. Half of the participants were required to engage in a verbal cognitive non-driving-related task. Situation awareness, takeover performance and physiological responses were measured for each situation. The results suggest that obstacle movement influences obstacle danger perception, situation awareness, and response time, while the latter is also influenced by obstacle danger. The cognitive verbal task also had an effect on the takeover response time. These results imply that the driver’s cognitive state and the driving situation (e.g. the movement/danger of entities present around the vehicle) must be considered when conveying information to drivers through in-vehicle interfaces

Conserver la conscience de l'environnement en conduite semi-autonome grâce à un siège haptique

National audienceSemi-autonomous driving is rapidly evolving and one of its major issues is the reduction of the driver's attention to his/her environment. After a brief analysis of the factors limiting autonomous systems and a study of current interactions increasing this situational awareness, and more particularly haptic interactions, this article proposes the use of vibrations in the seat. Vibrations, due to their location and variations in frequency and amplitude, make it possible to convey different information to the driver such as the position of obstacles around his/her vehicle as well as the state of deterioration of the road markings. The results of initial exploratory tests are promising on the use of haptic interactions. They make it possible to set up the design and procedure for future experiments.La conduite semi-autonome évolue rapidement et l'une de ses principales problématiques est la réduction de l'attention du conducteur vis-à-vis de son environnement. Après une brève analyse des facteurs limitant les systèmes autonomes ainsi qu'une étude des interactions actuelles augmentant cette conscience de la situation, et plus particulièrement des interactions haptiques, cet article propose l'utilisation de vibrations dans le siège. Les vibrations, de par leur localisation et variations en fréquence et amplitude, permettent de transmettre différentes informations au conducteur comme la position d'obstacles autour de son véhicule ainsi que l'état de dégradation du marquage au sol. Les résultats de premiers tests exploratoires sont prometteurs sur l'utilisation d'interactions haptiques. Ils permettent de mettre en place le design et la procédure des futures expériences

Favoriser l'apprentissage de la traversée d'un passage piéton pour des jeunes avec une déficience intellectuelle grâce à la réalité virtuelle

National audienceVirtual Reality (VR) has three main advantages: allowing safe simulations, experimenting different conditions for the same scenario, and providing the perfect replicability of the scenarios. We present a feasibility study on the use of VR and VR immersive headsets to assist young adults (10-18) with intellectual disabilities in learning new skills. We focused on the scenario of a pedestrian crossing without traffic lights, and we considered several environmental conditions (day/night, weather, kindness of drivers, etc.). Our study is not limited to young people with autism spectrum disorder but takes into account young adults with intellectual disability with an associated disorder. 15 young people participated in the study showing a very good acceptability of immersive headsets and a noticeable learning effect already after a short training session. However, a longer and more extensive study is needed to evaluate the transfer of learning to reality.La réalité virtuelle (VR) a trois avantages : simuler en toute sécurité, proposer différentes conditions pour une action identique et rejouer un scénario de manière contrôlée. Nous présentons une étude de faisabilité concernant l'utilisation de la VR (casques immersifs) pour accompagner dans l'apprentissage de nouvelles compétences des jeunes adultes (10-18) avec une déficience intellectuelle (DI). Nous nous focalisons sur le scénario d'un passage piéton, en considérant plusieurs conditions environnementales (jour/nuit, gentillesse des conducteurs, etc.). Notre étude ne se limite pas aux jeunes avec trouble du spectre de l'autisme mais prend en compte les 10-18 ans ayant une DI avec un trouble associé. 15 jeunes ont participé à l'étude en montrant une bonne acceptabilité du casque immersif et un effet d'apprentissage déjà après une courte session d'entrainement. Une étude plus étendue et de longue durée est nécessaire pour évaluer le transfert de l'apprentissage dans la réalité