Prospects of Mobile Search

Search faces (at least) two major challenges. One is to improve efficiency of retrieving relevant content for all digital formats (images, audio, video, 3D shapes, etc). The second is making relevant information retrievable in a range of platforms, particularly in high diffusion ones as mobiles. The two challenges are interrelated but distinct. This report aims at assessing the potential of future Mobile Search. Two broad groups of search-based applications can be identified. The first one is the adaptation and emulation of web search processes and services to the mobile environment. The second one is services exploiting the unique features of the mobile devices and the mobile environments. Examples of these context-aware services include location-based services or interfacing to the internet of things (RFID networks). The report starts by providing an introduction to mobile search. It highlights differences and commonalities with search technologies on other platforms (Chapter 1). Chapter 2 is devoted to the supply side of mobile search markets. It describes mobile markets, presents key figures and gives an outline of main business models and players. Chapter 3 is dedicated to the demand side of the market. It studies users¿ acceptance and demand using the results on a case study in Sweden. Chapter 4 presents emerging trends in technology and markets that could shape mobile search. It is the author's view after discussing with many experts. One input to this discussion was the analysis of on forward-looking scenarios for mobile developed by the authors (Chapter 5). Experts were asked to evaluate these scenarios. Another input was a questionnaire to which 61 experts responded. Drivers, barriers and enablers for mobile search have been synthesised into SWOT analysis. The report concludes with some policy recommendations in view of the likely socio-economic implications of mobile search in Europe.JRC.DG.J.4-Information Societ

Methods for monitoring the human circadian rhythm in free-living

Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, we estimate light exposure from smartphone sensor and weather in- formation. We applied the Kronauer model to compare the phase shift resulting from head light measurements, wrist measurements, and smartphone estimations. We found it was possible to monitor circadian phase shift from light estimation based on smartphone sensor and weather information with a weekly error of 32±17min, which outperformed wrist measurements in 11 out of 12 participants. Sleep could be detected from smartphone use with an onset error of 40±48 min and wake error of 42±57 min. Screen use could be detected smart eyeglasses with 0.9 ROC AUC for ambient light intensities below 200lux. Nine clusters of ADLs were distinguished using Gaussian mixture models with an average accuracy of 77%. In conclusion, a combination of the proposed smartphones and smart eyeglasses applications could support users in synchronising their circadian clock to the external clocks, thus living a healthier lifestyle

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Promoting Informal Learning Using a Context-Sensitive Recommendation Algorithm For a QRCode-based Visual Tagging System

Structured Abstract Context: Previous work in the educational field has demonstrated that Informal Learning is an effective way to learn. Due to its casual nature it is often difficult for academic institutions to leverage this method of learning as part of a typical curriculum. Aim: This study planned to determine whether Informal Learning could be encouraged amongst learners at Durham University using an object tagging system and a context-sensitive recommendation algorithm. Method: This study creates a visual tagging system using a type of two-dimensional barcode called the QR Code and describes a tool designed to allow learners to use these ‘tags’ to learn about objects in a physical space. Information about objects features audio media as well as textual descriptions to make information appealing. A collaboratively-filtered, user-based recommendation algorithm uses elements of a learner’s context, namely their university records, physical location and data on the activities of users similar to them to create a top-N ranked list of objects that they may find interesting. The tool is evaluated in a case study with thirty (n=30) participants taking part in a task in a public space within Durham University. The evaluation uses quantitative and qualititative data to make conclusions as to the use of the proposed tool for individuals who wish to learn informally. Results: A majority of learners found learning about the objects around them to be an interesting practice. The recommendation system fulfilled its purpose and learners indicated that they would travel a significant distance to view objects that were presented to them. The addition of audio clips to largely textual information did not serve to increase learner interest and the implementation of this part of the system is examined in detail. Additionally there was found to be no apparent correlation between prior computer usage and the ability to comprehend an informal learning tool such as the one described. Conclusion: Context-sensitive, mobile tools are valuable for motivating Informal Learning. Interaction with tagged objects outside of the experimental setting indicates significant learner interest even from those individuals that did not participate in the study. Learners that did participate in the experiment gained a better understanding of the world around them than they would have without the tool and would use such software again in the future

Machine learning and mixed reality for smart aviation: applications and challenges

The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency