A hybrid intelligent agent for notification of users distracted by mobile phones in an urban environment

Mobile devices are now ubiquitous in daily life and the number of activities that can be performed using them is continually growing. This implies increased attention being placed on the device and diverted away from events taking place in the surrounding environment. The impact of using a smartphone on pedestrians in the vicinity of urban traffic has been investigated in a multimodal, fully immersive, virtual reality environment. Based on experimental data collected, an agent to improve the attention of users in such situations has been developed. The proposed agent uses explicit, contextual data from experimental conditions to feed a statistical learning model. The agent’s decision process is aimed at notifying users when they become unaware of critical events in their surroundings

Non-Intrusive Computing

Pervasive computing is an important trend today. It concerns devices and services in a smart space that interact with users in a simple, natural, and harmonious way. Many problems in this domain have been studied from different perspectives in various projects. However, one important characteristic of pervasive computing, which is how to make it non-intrusive so that users can focus on their tasks, has received little formal attention. Nowadays, many computing entities including smart devices, and software components, are involved in our daily lives, and users need to deal with them as well as with other people. Besides, people are easy to reach with multiple devices. We believe there should be a systematic way to help users avoid intrusive ones. We propose a model for posing and answering two questions: will an interaction intrude on its receiver if delivered, and given that the interaction is deliverable, how can it be delivered effectively and not too overtly? With this model, the intrusion problem is analyzed and the essential factors are identified. A quantitative approach is used, so that factors have quantitative values for comparison and computation. We also apply context to refine them in order to achieve better results. We then illustrate how to materialize the model and build a system whose design is inspired by the Jabber framework that includes a collection of standards, technologies, and projects for instant messaging. The discussion is at a general level that does not depend on Jabber. However, by choosing Jabber in implementation, we reuse existing software and technologies, and benefit from Jabber/XMPP standardization, its low entry barrier for application developers, and its rich community support. The main contributions of our work are two-fold. First, we propose a model for intrusiveness in pervasive computing. Second, we address the problem at the system level by designing and realizing it. We also make use of standardized instant-messaging technologies, more precisely Jabber, in the system instantiation to reuse existing software, making the system more flexible and extensible

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

(VANET IR-CAS): Utilizing IR Techniques in Building Context Aware Systems for VANET

Most of the available context aware dissemination systems for the Vehicular Ad hoc Network (VANET) are centralized systems with low level of user privacy and preciseness. In addition, the absence of common assessment models deprives researchers from having fair evaluation of their proposed systems and unbiased comparison with other systems. Due to the importance of the commercial, safety and convenience services, three IR-CAS systems are developed to improve three applications of these services: the safety Automatic Crash Notification (ACN), the convenience Congested Road Notification (CRN) and the commercial Service Announcement (SA). The proposed systems are context aware systems that utilize the information retrieval (IR) techniques in the context aware information dissemination. The dispatched information is improved by deploying the vector space model for estimating the relevance or severity by calculating the Manhattan distance between the current situation context and the severest context vectors. The IR-CAS systems outperform current systems that use machine learning, fuzzy logic and binary models in decentralization, effectiveness by binary and non-binary measures, exploitation of vehicle processing power, dissemination of informative notifications with certainty degrees and partial rather than binary or graded notifications that are insensitive to differences in severity within grades, and protection of privacy which achieves user satisfaction. In addition, the visual-manual and speech-visual dual-mode user interface is designed to improve user safety by minimizing distraction. An evaluation model containing ACN and CRN test collections, with around 500,000 North American test cases each, is created to enable fair effectiveness comparisons among VANET context aware systems. Hence, the novelty of VANET IR-CAS systems is: First, providing scalable abstract context model with IR based processing that raises the notification relevance and precision. Second, increasing decentralization, user privacy, and safety with the least distracting user interface. Third, designing unbiased performance evaluation as a ground for distinguishing significantly effective VANET context aware systems

Análise de dados aplicada para consciência situacional de pedestres

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.O número de acidentes próximos a vias urbanas, principalmente em ruas com grande movimento, tem aumentado consideravelmente. Este fato está sendo associado com o crescimento contínuo do uso de dis- positivos móveis, como smartphones, tablets e smartwatches, em áreas urbanas. A fim de ajudar na diminuição dos acidentes causados pela distração por estes dispositivos, o projeto Awareness propõe um mo- delo de consciência situacional. Este modelo foi construído através de dados coletados em um ambiente de realidade virtual simulando um ambiente urbano, no qual um usuário é submetido a testes para avaliar sua consciência. Este trabalho terá uma abordagem de comparação en- tre diversas técnicas de análise com base nos dados obtidos no projeto, utilizando inteligência artificial e estatística. A partir dos resultados, realizou-se a inferência de um nível de consciência situacional do usuá- rio de acordo com dados sobre o ambiente que está inserido, através de busca por padrões que originem distrações e ocasionam uma circuns- tância de risco e, então, foi construído um modelo preditivo para auxílio na tomada de decisões de pedestres. Outras análises foram realizadas anteriormente baseada nos dados do projeto, mas utilizaram o método de redes bayesianas. A proposta deste trabalho é compreender como outras abordagens estatísticas podem modelar esse problema e avaliar seus desempenhos

4th International Symposium on Ambient Intelligence (ISAmI 2013)

Ambient Intelligence (AmI) is a recent paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyone’s life more comfortable. Another main concern of AmI originates from the human computer interaction domain and focuses on offering ways to interact with systems in a more natural way by means user friendly interfaces. This field is evolving quickly as can be witnessed by the emerging natural language and gesture based types of interaction. The inclusion of computational power and communication technologies in everyday objects is growing and their embedding into our environments should be as invisible as possible. In order for AmI to be successful, human interaction with computing power and embedded systems in the surroundings should be smooth and happen without people actually noticing it. The only awareness people should have arises from AmI: more safety, comfort and wellbeing, emerging in a natural and inherent way. ISAmI is the International Symposium on Ambient Intelligence and aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons learned, namely in terms of software and applications, and aims to bring together researchers from various disciplines that are interested in all aspects of this area

Contextual awareness, messaging and communication in nomadic audio environments

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1998.Includes bibliographical references (p. 119-122).Nitin Sawhney.M.S

Smartphones as steady companions: device use in everyday life and the economics of attention

This thesis investigates smartphone use in naturally occurring contexts with a dataset comprising 200 hours of audio-visual first-person recordings from wearable cameras, and self-confrontation interview video footage (N = 41 users). The situated context in which smartphone use takes place has often been overlooked because of the technical difficulty of capturing context of use, actual action of users, and their subjective experience simultaneously. This research project contributes to filling this gap, with a detailed, mixed-methods analysis of over a thousand individual phone engagement behaviours (EB). We observe that (a) the smartphone is a key structuring element in the flow of daily activities. Participants report complex strategies on how they manage engaging with or avoiding their devices. (b) Unexpectedly, we find that the majority of EB (89%) are initiated by users, not devices; users engage with the phone roughly every five minutes regardless of the context they are in. (c) A large portion of EB seems to stem from contextual cues and an unconscious urge to pick up the device, even when there is no clear reason to do so. d) Participants are surprised about, and often unhappy with how frequently they mindlessly reach for the phone. Our in-depth analysis unveils several overlapping layers of motivations and triggers driving EB. Monitoring incoming notifications, managing time use, responding to social pressures, actually completing a task with the phone, design factors, unconscious urges, as well as the accessibility of the device, and most importantly its affordance for distraction all contribute to picking up the phone. This user drive for EB is used by providers to feed the attention economy. So far, keeping the smartphone outside of the visual field and immediate reach has appeared as the only efficient strategy to prevent overuse