Analysis and use of the emotional context with wearable devices for games and intelligent assistants

In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

An adaptive general type-2 fuzzy logic approach for psychophysiological state modeling in real-time human–machine interfaces

In this article, a new type-2 fuzzy-based modeling approach is proposed to assess human operators’ psychophysiological states for both safety and reliability of human–machine interface systems. Such a new modeling technique combines type-2 fuzzy sets with state tracking to update the rule base through a Bayesian process. These new configurations successfully lead to an adaptive, robust, and transparent computational framework that can be utilized to identify dynamic (i.e., real time) features without prior training. The proposed framework is validated on mental arithmetic cognitive real-time experiments with ten participants. It is found that the proposed framework outperforms other paradigms (i.e., an adaptive neuro-fuzzy inference system and an adaptive general type-2 fuzzy c-means modeling approach) in terms of disturbance rejection and learning capabilities. The proposed framework achieved the best performance compared to other models that have been presented in the related literature. Therefore, the new framework can be a promising development in human–machine interface systems. It can be further utilized to develop advanced control mechanisms, investigate the origins of human compromised task performance, and identify and remedy psychophysiological breakdown in the early stages

A Context-Aware mHealth System for Online Physiological Monitoring in Remote Healthcare

Physiological or biological stress is an organism’s response to a stressor such as an environmental condition or a stimulus. The identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. Activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. This is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. This paper presents a context-aware telemonitoring platform, IPM-mHealth, that receives vital parameters from multiple sensors for online, real-time analysis. IPM-mHealth provides the technical basis for effectively evaluating patients’ physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. The two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. Finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. The test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis

Biosignal controlled recommendation in entertainment systems

With the explosive growth of the entertainment contents and the ubiquitous access of them via fixed or mobile computing devices, recommendation systems become essential tools to help the user to find the right entertainment at the right time and location. I envision that by integrating the bio signal input into the recommendation process, it will help the users not only to find interesting contents, but also to increase one’s comfort level by taking into account the biosginal feedback from the users. The goal of this project was to develop a biosignal controlled entertainment recommendation system that increases the user’s comfort level by reducing the level of stress. As the starting point, this project aims to contribute to the field of recommendation systems with two points. The first is the mechanism of embedding the biosignal non-intrusively into the recommendation process. The second is the strategy of the biosignal controlled recommendation to reduce stress. Heart rate controlled in-flight music recommendation is chosen as its application domain. The hypothesis of this application is that, the passenger's heart rate deviates from the normal due to unusual long haul flight cabin environment. By properly designing a music recommendation system to recommend heart rate controlled personalized music playlists to the passenger, the passengers' heart rate can be uplifted, down-lifted back to normal or kept within normal, thus their stress can be reduced. Four research questions have been formulated based on this hypothesis. After the literature study, the project went mainly through three phases: framework design, system implementation and user evaluation to answer these research questions. During the framework design phase, the heart rate was firstly modeled as the states of bradycardia, normal and tachycardia. The objective of the framework is that, if the user's heart rate is higher or lower than the normal heart rate, the system recommends a personalized music playlist accordingly to transfer the user’s heart rate back to normal, otherwise to keep it at normal. The adaptive framework integrates the concepts of context adaptive systems, user profiling, and the methods of using music to adjust the heart rate in a feedback control system. In the feedback loop, the playlists were composed using a Markov decision process. Yet, the framework allows the user to reject the recommendations and to manually select the favorite music items. During this process, the system logs the interactions between the user and the system for later learning the user’s latest music preferences. The designed framework was then implemented with platform independent software architecture. The architecture has five abstraction levels. The lowest resource level contains the music source, the heart rate sensors and the user profile information. The second layer is for resource management. In this layer are the manager components to manage the resources from the first layer and to modulate the access from upper layers to these resources. The third layer is the database, acting as a data repository. The fourth layer is for the adaptive control, which includes the user feedback log, the inference engine and the preference learning component. The top layer is the user interface. In this architecture, the layers and the components in the layers are loosely coupled, which ensures the flexibility. The implemented system was used in the user experiments to validate the hypothesis. The experiments simulated the long haul flights from Amsterdam to Shanghai with the same time schedule as the KLM flights. Twelve subjects were invited to participate in the experiments. Six were allocated to the controlled group and others were allocated to the treatment group. In addition to a normal entertainment system for the control group, the treatment group was also provided with the heart rate controlled music recommendation system. The experiments results validated the hypothesis and answered the research questions. The passenger's heart rate deviates from normal. In our user experiments, the passenger's heart rate was in the bradycardia state 24.6% of time and was in the tachycardia state 7.3% of time. The recommended uplifting music reduces the average bradycardia state duration from 14.78 seconds in the control group to 6.86 seconds in the treatment group. The recommended keeping music increases the average normal state duration from 24.66 seconds in the control group to 29.79 seconds in the treatment group. The recommended down-lifting music reduces the average tachycardia state duration from 13.89 seconds in the control group to 6.53 seconds in the treatment group. Compared to the control group, the stress of the treatment group has been reduced significantly

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

A Framework for Profiling based on Music and Physiological State

The IoT (Internet of Things) is an emergent technological area with distinct chal-lenges, which has been addressed by the world research community. This disser-tation proposes the use of a knowledge-based framework capable of supporting the representation and handling of devices along with some autonomous inter-action with the human being, for creating added value and opportunities in IoT. With usability in mind, the objective lays in an attempt to characterize users’ physiological status mainly through music in a profiling approach. The idea is to produce a solution able to customize the environment by musical suggestions to the actual scenarios or mood that the users lie in. Such system can be trained to understand different physiological data to then infer musical suggestions to the users. One of the adopted methods in this work explores that thought, on whether the usage of a person’s physiological state can wield adequate sensorial stimulation to be usefully used thereafter. Another question considered in this work is whether it is possible to use such collected data to build user’s musical playlists and profile that tries to use the user’s physiological state to predict his or her emotional state with the objective to reach a well-being situation