1,721,025 research outputs found
Möglichkeiten und Anwendungen von Ultraschall Messungen auf nicht modifizierten Smartphones für Endverbraucher
No full textA person's smartphone is a cornucopia of information. Be it personal data extracted from contacts and calendar entries or the current location via GPS. The huge variety of sensors in today's mobile phones makes these devices a prime target for human activity recognition. The smartphone is no longer solely seen as actuator in smart environments, enabling the user to control auxiliary devices and sensors, but can now play a vital part in the network of sensing information itself. Especially in the area of human activity recognition, camera-based or body-worn systems are predominant. While they achieve high accuracy, these methods often suffer from privacy issues or obtrusiveness and consequently social stigma. In this thesis, I present an unobtrusive approach to perceive the vicinity surrounding the phone by leveraging the properties of ultrasound sensing. The device emits ultrasonic waves via its speaker and records the echo via the microphone. By analyzing the received signal, I can deduct certain movements, e.g. gestures performed above the phone, but also more complex motions involving the whole body of the user. I outline various experiments to estimate the feasibility of ultrasound sensing in different scenarios as well as propose an algorithm and mobile application that can classify given gestures and activities performed by the user. The system is able to recognize predefined gestures with an overall accuracy of 81% over six different users and can detect human activities up to 2m away
Best practices to visualize activity data in mobile apps
No full textPhysical activity and exercise are essential factors to live a healthy life. Fitness trackers have great potential to assist individuals in making healthy changes to their lifestyle. A variety of fitness trackers are available in the market such as fitness apps based on mobile platform, wearable sensors (e.g. smartwatch, armband, wristband), balancing boards (e.g. Wii fit) etc. In this thesis, the focus is on fitness apps based on mobile platform. These apps provide different information and features to the user such as a summary of the physical activity performed, feedback of the activity (e.g. through virtual trainer), exercise plans according to the user's workout routine, user's achievements and many more. Also, fitness apps aim to present a lot of statistical data to the users regarding their current or previous physical activity which may range from days to years. To visualize this data, visual designs such as maps, graphs, images are used. However, very little is known about such visualization schemes and design strategies for fitness data w.r.t engaging users. Furthermore, it is important to know if the provided features in the app are useful. The main objective of this study is to evaluate different visualization schemes used in visualizing fitness data and to explore usability requirements, motivating factors for using mobile fitness apps. For this purpose, a profound research is done in three phases. The first phase focuses on finding expectations of a user from fitness app through a short primary survey in University Gym, the second phase includes designing an extensive user survey and fitness app mock-ups based on the survey findings in the first phase. In the third phase, the designed mock-ups are evaluated by means of the user survey designed in second phase and the survey results are analyzed using statistical test. The study reveals that users find some visualization schemes very useful whereas they do not prefer some visualization schemes at all. Same is the case observed for motivational features e.g. ranking, rewards and other functionalities of the app e.g. workout summary, nutrition information. This thesis concludes with best practices for designing visualization schemes and analysis of user requirements for mobile fitness applications such as integrated feedback, home screen design of the app and some features like data sharing, data export etc. These findings show the way to develop highly usable fitness applications with user-centric design
Anomalie Detektion für Pfadeplannung bei Einzel- und Multipersonbetrieb in Smart Home Anwendungen
No full textThe most popular outdoor positioning system, global positioning system (GPS), does not perform well in indoor environment. Because this system primarily depends on the signal propagation in the air and complex architecture of buildings will interfere with signal propagation, i.e., its indoor positioning performance will be limited by the line-of-sight nature. While the drawbacks of GPS, other indoor positioning techniques (such as Wi-Fi based, RFID based) can provide Location-based-service (LBS) for various applications, which make our life comfortable and smart. As one kind of these sensing and positioning techniques, the passive Electric Field Sensing has numerous advantages compared to the others, e.g., lower power consumption and no personal information and specific positioning tokens required. So it is applied in our Smart Floor system to position and track movement for users, which mainly aims at the elderly care. On the other hand, a passive EFS-based positioning system might be susceptible to disturbance, due to the aliasing effect and noises from environment. To address this problem, I studied and investigated the Anomaly Detection issue in the Machine Learning domain, which aims at discovering proper ML algorithms to improve the positioning and movement prediction performance of our Smart Floor system. In this thesis, I proposed a novel ML algorithm for this goal, namely the Dictionary-based Anomaly Detection Algorithm. Compared with other existing algorithms, this dictionary algorithm exploits not only the normal data but also coupling outliers to obtain our desired results, i.e. indoor positions of users. Furthermore, combining with a customized positioning scheme relying on anchor points, the Dictionary-based indoor Positioning and Movement Prediction approach preformed well in our living laboratory. Moreover as discussion and expansion, the Dictionary-based Anomaly Detection Algorithm is especially practicable in application scenarios where a large amount of outliers and normal data are always at the same time observed
Activity Recognition On Unmodified Consumer Smartphones Via Active Ultrasonic Sensing
No full textSensor miniaturisation and streaming classification techniques can be used to recognize human behaviours and contexts. This is extremely valuable to realize smart environments, e.g. to support healthy and independent living. The most important parameters to sense include indoor location, gestures, or emergencies like falls. Up to now, activity recognition systems face a number of sensitive drawbacks. For example, camera-based systems induce privacy issues and are costly to deploy. Body-worn systems are inconvenient to wear over long periods of time. Highly visible systems may introduce social stigma and modify the well-known living environment. In this project, we explore the possibility for the use of a new, unobtrusive, physical principle to sense and recognize human activities using off-the-shelf smart-phone. A person's smart-phone is a cornucopia of information. The huge variety of sensors in today's mobile phones makes these devices a prime target for human activity recognition. Our novel approach is to develop a novel activity recognizing system using an unmodified smart-phone. We profit from integrated microphones and loudspeakers without additional hardware components needed. The advantage of this system is therefore that it can be easily installed on a smart-phone and put into action. An android application has already been developed which is able to send a high frequency sound in the near ultrasound range, e.g. 20 kHz. Using the received echo from the microphone, the information caused by movement in midair around the device will be extracted. In this thesis we intend to improve the performance of the existing system with respect to noise cancellation and other classification schemes. In this thesis, we present an android application called Trainer for complex activity recognition. It is built on ultrasense [8], a mobile application that capitalizes the characters of ultrasound to inspect the surrounding environment. The application is able to send a high frequency signal in the near ultrasound range, e.g. 20 kHz. Using the received echo from the microphone, the information caused by movement in midair around the device will be extracted. Complex activities tagged under home exercises are evaluated using micro-Doppler signatures [mD-signatures]. We propose an algorithm to classify a set of exercises carried out by the user and show that using the Support vector machine classifier we are able to obtain an accuracy of 85% using Principal component analysis and a signature feature introduced in this thesis as a feature
Indoor localization based on electric potential sensing
No full textIndoor localization is needed in applications ranging from health care to entertainment. Although approaches based on video cameras have the upper hand in terms of accuracy and maturity, they raise privacy concerns and require heavy computation. Passive electric field sensing represents a low-cost, low-power, non-intrusive alternative for localization, which is investigated in this thesis. A human being naturally generates an electric field when walking. This field carries an ambiguous and nonlinear information about the person's position. The present thesis proposes to combine measurements from several electric field sensors, thus resolving the ambiguity and obtaining a problem similar to trilateration. The method is presented as a detailed analytical model and implemented in a scalable system, the Platypus. The Platypus operates with a commercial sensor, the PS25451 EPIC (electric potential integrated circuit) manufactured by Plessey Semiconductors, which costs less than 10 Euro and consumes about 6mW. In this work, six sensors are fixed on the ceiling of a room, covering an area of 5m2, and the localization method is evaluated with 30 subjects. Results show that individuals walking at a comfortable speed are localized approximately twice each time they make a step, with an average error of 19.1 cm. The thesis contributes an original localization method that can be used in fusion with other systems, such as infrared sensors, to combine their respective strengths. The described analytical models have a large scope and can be adapted to other applications in human movement sensing
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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