Predicting upcoming values of stress while driving

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, we evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. We use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. We have used data from four different drivers with three different car models and a motorbike and more than 220 test drives. Results show that upcoming stress levels can be accurately predicted for a single user ( correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited ( correlation r = 0.92 and classification accuracy 46.9%).This work was supported in part by HERMES-SMART DRIVER Project through Spanish MINECO under Project TIN2013-46801-C4-2-R, in part by the Ministerio de Educación Cultura y Deporte under Grant PRX15/0003

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication

In this paper, a minimalist, completely distributed freeway traffic information system is introduced. It involves an autonomous, vehicle-based jam front detection, the information transmission via inter-vehicle communication, and the forecast of the spatial position of jam fronts by reconstructing the spatiotemporal traffic situation based on the transmitted information. The whole system is simulated with an integrated traffic simulator, that is based on a realistic microscopic traffic model for longitudinal movements and lane changes. The function of its communication module has been explicitly validated by comparing the simulation results with analytical calculations. By means of simulations, we show that the algorithms for a congestion-front recognition, message transmission, and processing predict reliably the existence and position of jam fronts for vehicle equipment rates as low as 3%. A reliable mode of operation already for small market penetrations is crucial for the successful introduction of inter-vehicle communication. The short-term prediction of jam fronts is not only useful for the driver, but is essential for enhancing road safety and road capacity by intelligent adaptive cruise control systems.Comment: Published in the Proceedings of the Annual Meeting of the Transportation Research Board 200

Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information

The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In this paper, we present a novel mechanism to predict upcoming values for stress levels based on current and past values for both the driving behavior and environmental factors. First, we analyze the relationship between stress levels and different variables that model the driving behavior (accelerations, decelerations, positive kinetic energy, standard deviation of speed, and road shape). Stress levels are obtained utilizing a Polar H7 heart rate strap. Vehicle telemetry is captured using a smartphone. Second, we study the accuracy of several machine learning algorithms (Support Vector Machine, Multilayer Perceptron, Naive Bayes, J48, and Deep Belief Network) when used to estimate the stress based on our input data. Finally, an experiment was conducted in a real environment. We considered three different scenarios: home-workplace route, workplace-home route, and driving under heavy traffic. The results show that the proposal can estimate the upcoming stress with high accuracy. This algorithm could be used to develop driving assistants that recommend actions to prevent the stress.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte and from a sabbatical leave by the Carlos III of Madrid University

Early galaxy formation in warm dark matter cosmologies

We present a framework for high-redshift ($z \geq 7$) galaxy formation that traces their dark matter (DM) and baryonic assembly in four cosmologies: Cold Dark Matter (CDM) and Warm Dark Matter (WDM) with particle masses of $m_x =$ 1.5, 3 and 5 ${\rm keV}$. We use the same astrophysical parameters regulating star formation and feedback, chosen to match current observations of the evolving ultra violet luminosity function (UV LF). We find that the assembly of observable (with current and upcoming instruments) galaxies in CDM and $m_x \geq 3 {\rm keV}$ WDM results in similar halo mass to light ratios (M/L), stellar mass densities (SMDs) and UV LFs. However the suppression of small-scale structure leads to a notably delayed and subsequently more rapid stellar assembly in the $1.5 {\rm keV}$ WDM model. Thus galaxy assembly in $m_x \leq 2 {\rm keV}$ WDM cosmologies is characterized by: (i) a dearth of small-mass halos hosting faint galaxies; and (ii) a younger, more UV bright stellar population, for a given stellar mass. The higher M/L ratio (effect ii) partially compensates for the dearth of small-mass halos (effect i), making the resulting UV LFs closer to CDM than expected from simple estimates of halo abundances. We find that the redshift evolution of the SMD is a powerful probe of the nature of DM. Integrating down to a limit of $M_{UV} =-16.5$ for the James Webb Space Telescope (JWST), the SMD evolves as $\log$(SMD)$\propto -0.63 (1+z)$ in $m_x = 1.5 {\rm keV}$ WDM, as compared to $\log$(SMD)$\propto -0.44 (1+z)$ in CDM. Thus high-redshift stellar assembly provides a powerful testbed for WDM models, accessible with the upcoming JWST.Comment: Accepted for publication in Ap

Toward safer highways: predicting driver stress in varying conditions on habitual routes

Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is proposed to predict driver stress. This type of algorithm detects complex relationships among variables. At the same time, it avoids overfitting. The prediction of the upcoming stress level is made by taking into account driving behavior (acceleration, deceleration, speed) and the previous stress level.This work was supported by the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R, funded by the Spanish Ministry of Economy, Industry and Competitiveness, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte, and from a sabbatical leave by the Carlos III University of Madrid.Publicad

Threats to soil quality in Denmark - A review of existing knowledge in the context of the EU Soil Thematic Strategy

The EU Commission is preparing a proposal for a Soil Framework Directive with the purpose of protecting the soil resources in Europe. The proposal identifies six major threats to the sustained quality of soils in Europe. This report addresses the threats that are considered most important under the prevailing soil and climatic conditions in Denmark: compaction, soil organic matter decline, and erosion by water and tillage. For each of these threats, the relevance and damage to soil functions as well as the geographic distribution in Denmark are outlined. We suggest a procedure for identifying areas at risk. This exercise involves an explicit identification of: i) the disturbing agent (climate / management) exerting the pressures on soil, and ii) the vulnerability of the soil to those stresses. Risk reduction targets, measures required to reach these targets, and the knowledge gaps and research needs to effectively cope with each threat are discussed. Our evaluation of the threats is based on soil resilience to the imposed stresses. Subsoil compaction is considered a severe threat to Danish soils due to frequent traffic with heavy machinery in modern agriculture and forestry. The soil content of organic matter is critically low for a range of Danish soils, which should be counteracted by appropriate management options. Soil erosion by tillage, and to a lesser degree by water, adversely affects soil quality on much of the farmland because degradation rates are much higher than generation of soil

Estimating the stress for drivers and passengers using deep learning

Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016The number of vehicles in circulation has become a problem both for safety and for the citizens health. Public transport is a solution to reduce its impact on the environment. One of the keys to encourag e users to use it is to improve comfort. On the other hand, numerous studies highlight that drivers are more likely to suffer physical and psychological illnesses due to the sedentary nature of this work and workload . In this paper, we propose a model to p redict the stress level on drivers and passengers. The solution is based on deep learning algorithms. The proposal employs the Heart Rate Variability (HRV) and telemetry from the vehicle in order to anticipate the incoming stress . It has been validated in a real environment on distinct routes. The results show that it predict s the stress by 86 % on drivers and 92% on passengers. This algorithm could be used to develop driving assistants that recommend actions to smooth driving, reducing the work load and the passenger stress.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte