Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique

The tremendous growth of mobile devices, IOT devices, applications and many other services have placed high demand on mobile and wireless network infrastructures. Much research and development of 5G mobile networks have found the way to support the huge volume of traffic, extracting of fine-gained analytics and agile management of mobile network elements, so that it can maximize the user experience. It is very challenging to accomplish the tasks as mobile networks increase the complexity, due to increases in the high volume of data penetration, devices, and applications. One of the solutions, advance machine learning techniques, can help to mitigate the large number of data and algorithm driven applications. This work mainly focus on extensive analysis of mobile traffic for improving the performance, key performance indicators and quality of service from the operations perspective. The work includes the collection of datasets and log files using different kind of tools in different network layers and implementing the machine learning techniques to analyze the datasets to predict mobile traffic activity. A wide range of algorithms were implemented to compare the analysis in order to identify the highest performance. Moreover, this thesis also discusses about network slicing architecture its use cases and how to efficiently use network slicing to meet distinct demands

Human Activity Recognition & Mobily Path Prediction

Individual Mobility is the study that depicts how individuals move inside a region or system. As of late a few researches have been accomplished for this reason and there has been a flood in enormous informational accessible in individual developments. Most of these information’s are gathered from cellphone or potentially GPS with variable accuracy relying upon the distance from the tower. Enormous scope information, for example, cell phone follows are significant hotspot for urban modeling. The individual travel designs breakdown into a solitary likelihood distribution however despite the assorted variety of their travel history people follow basic reproducible examples. This similitude in movement example can help us in an extremely different zones of utilizations, for example, city arranging, traffic building, spread of disease and versatile infections. The motive of this project is to show that by utilizing a measure of direct estimation that human directions do follow a few high reproducible scaling designs. Activity recognition expects to perceive the activities and objectives of at least one operator from a progression of perceptions on the specialists\u27 activities and the natural conditions. Human movement acknowledgment, which is one of the developing fields of research, plans to figure out which action is finished by people. Some true applications, for example, health monitoring, abnormal behavior detection, and sport. In this way, it is a troublesome issue given the enormous number of perceptions delivered each second, the fleeting idea of the perceptions, and the absence of an unmistakable method to relate accelerometer information to known developments. Keen PDAs presently fuse numerous different and ground-breaking sensors, for example, GPS sensors, vision sensors, sound sensors, light sensors, temperature sensors, course sensors and speeding up sensors. This project is about utilizations telephone-based accelerometers to perform activity recognition, which includes identifying the physical movement a user is performing

Geobase Information System Impacts on Space Image Formats

As Geobase Information Systems increase in number, size and complexity, the format compatability of satellite remote sensing data becomes increasingly more important. Because of the vast and continually increasing quantity of data available from remote sensing systems the utility of these data is increasingly dependent on the degree to which their formats facilitate, or hinder, their incorporation into Geobase Information Systems. To merge satellite data into a geobase system requires that they both have a compatible geographic referencing system. Greater acceptance of satellite data by the user community will be facilitated if the data are in a form which most readily corresponds to existing geobase data structures. The conference addressed a number of specific topics and made recommendations

Usability, Efficiency and Security of Personal Computing Technologies

New personal computing technologies such as smartphones and personal fitness trackers are widely integrated into user lifestyles. Users possess a wide range of skills, attributes and backgrounds. It is important to understand user technology practices to ensure that new designs are usable and productive. Conversely, it is important to leverage our understanding of user characteristics to optimize new technology efficiency and effectiveness. Our work initially focused on studying older users, and personal fitness tracker users. We applied the insights from these investigations to develop new techniques improving user security protections, computational efficiency, and also enhancing the user experience. We offer that by increasing the usability, efficiency and security of personal computing technology, users will enjoy greater privacy protections along with experiencing greater enjoyment of their personal computing devices. Our first project resulted in an improved authentication system for older users based on familiar facial images. Our investigation revealed that older users are often challenged by traditional text passwords, resulting in decreased technology use or less than optimal password practices. Our graphical password-based system relies on memorable images from the user\u27s personal past history. Our usability study demonstrated that this system was easy to use, enjoyable, and fast. We show that this technique is extendable to smartphones. Personal fitness trackers are very popular devices, often worn by users all day. Our personal fitness tracker investigation provides the first quantitative baseline of usage patterns with this device. By exploring public data, real-world user motivations, reliability concerns, activity levels, and fitness-related socialization patterns were discerned. This knowledge lends insight to active user practices. Personal user movement data is captured by sensors, then analyzed to provide benefits to the user. The dynamic time warping technique enables comparison of unequal data sequences, and sequences containing events at offset times. Existing techniques target short data sequences. Our Phase-aware Dynamic Time Warping algorithm focuses on a class of sinusoidal user movement patterns, resulting in improved efficiency over existing methods. Lastly, we address user data privacy concerns in an environment where user data is increasingly flowing to manufacturer remote cloud servers for analysis. Our secure computation technique protects the user\u27s privacy while data is in transit and while resident on cloud computing resources. Our technique also protects important data on cloud servers from exposure to individual users

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

Access Anytime Anyplace: An Empircal Investigation of Patterns of Technology Use in Nomadic Computing Environments

With the increasing pervasiveness of mobile technologies such as cellular phones, personal digital assistants and hand held computers, mobile technologies promise the next major technological and cultural shift. Like the Internet, it is predicted that the greatest impact will not come from hardware devices or software programs, but from emerging social practices, which were not possible before. To capitalize on the benefits of mobile technologies, organizations have begun to implement nomadic computing environments. Nomadic computing environments make available the systems support needed to provide computing and communication capabilities and services to the mobile work force as they move from place to place in a manner that is transparent, integrated, convenient and adaptive. Already, anecdotes suggest that within organizations there are social implications occurring with both unintended and intended consequences being perpetuated. The problems of nomadic computing users have widely been described in terms of the challenges presented by the interplay of time, space and context, yet a theory has yet to be developed which analyzes this interplay in a single effort. A temporal human agency perspective proposes that stakeholders’ actions are influenced by their ability to recall the past, respond to the present and imagine the future. By extending the temporal human agency perspective through the recognition of the combined influence of space and context on human action, I investigated how the individual practices of eleven nomadic computing users changed after implementation. Under the umbrella of the interpretive paradigm, and using a cross case methodology this research develops a theoretical account of how several stakeholders engaged with different nomadic computing environments and explores the context of their effectiveness. Applying a literal and theoretical replication strategy to multiple longitudinal and retrospective cases, six months were spent in the field interviewing and observing participants. Data analysis included three types of coding: descriptive, interpretive and pattern coding. The findings reveal that patterns of technology use in nomadic computing environments are influenced by stakeholders’ temporal orientations; their ability to remember the past, imagine the future and respond to the present. As stakeholders all have different temporal orientations and experiences, they exhibit different practices even when engaging initially with the same organizational and technical environments. Opposing forces emerge as users attempt to be effective by resolving the benefits and disadvantages of the environment as they undergo different temporal, contextual and spatial experiences. Insights about the ability to predict future use suggest that because they are difficult to envisage in advance, social processes inhibit the predictability of what technologies users will adopt. The framework presented highlights the need to focus on understanding the diversity in nomadic computing use practices by examining how they are influenced by individual circumstances as well as shared meanings across individuals