Context Adaptivity as Enabler for Meaningful Pervasive Advertising

Socio-demographic user profiles are currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer’s context are largely unanswered. Research in the field is scattered and frequently prototype-driven. What the community lacks is a thorough methodology to provide the basis for any context-adaptive system: conceptualizing context. This position paper describes our current research of conceptualizing context for pervasive advertising. It summarizes findings from literature analysis and proposes a methodology for context conceptualization, which is currently work-in-progress

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Kompensation positionsbezogener Artefakte in Aktivitätserkennung

This thesis investigates, how placement variations of electronic devices influence the possibility of using sensors integrated in those devices for context recognition. The vast majority of context recognition research assumes well defined, fixed sen- sor locations. Although this might be acceptable for some application domains (e.g. in an industrial setting), users, in general, will have a hard time coping with these limitations. If one needs to remember to carry dedicated sensors and to adjust their orientation from time to time, the activity recognition system is more distracting than helpful. How can we deal with device location and orientation changes to make context sensing mainstream? This thesis presents a systematic evaluation of device placement effects in context recognition. We first deal with detecting if a device is carried on the body or placed somewhere in the environ- ment. If the device is placed on the body, it is useful to know on which body part. We also address how to deal with sensors changing their position and their orientation during use. For each of these topics some highlights are given in the following. Regarding environmental placement, we introduce an active sampling ap- proach to infer symbolic object location. This approach requires only simple sensors (acceleration, sound) and no infrastructure setup. The method works for specific placements such as "on the couch", "in the desk drawer" as well as for general location classes, such as "closed wood compartment" or "open iron sur- face". In the experimental evaluation we reach a recognition accuracy of 90% and above over a total of over 1200 measurements from 35 specific locations (taken from 3 different rooms) and 12 abstract location classes. To derive the coarse device placement on the body, we present a method solely based on rotation and acceleration signals from the device. It works independent of the device orientation. The on-body placement recognition rate is around 80% over 4 min. of unconstrained motion data for the worst scenario and up to 90% over a 2 min. interval for the best scenario. We use over 30 hours of motion data for the analysis. Two special issues of device placement are orientation and displacement. This thesis proposes a set of heuristics that significantly increase the robustness of motion sensor-based activity recognition with respect to sen- sor displacement. We show how, within certain limits and with modest quality degradation, motion sensor-based activity recognition can be implemented in a displacement tolerant way. We evaluate our heuristics first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the dis- placed recognition rate from 24% for a displaced accelerometer, which had 96% recognition when not displaced, to 82%

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware

Analysis of failure mechanisms of machine embroidered electrical contacts and solutions for improved reliability

In recent years, a number of research projects and patents have proposed to apply embroidery of conductive yarn to build electric circuits on textile substrates. To contact electronic modules or components to these circuits, embroidery itself was applied as a contacting method. Thereby, the embroidery needle is stitching through a conductive pad on an electronic substrate and is laying the conductive thread over this pad. The yarn and the pad establish an electrical contact. However, until today this contacting technology based on embroidery has not been adopted by the industry since reliability issues during stress were reported by different researchers. Yet, neither these failure phenomena were investigated comprehensively, nor was it attempted to understand their cause. This inhibited potential improvements to make these embroidered contacts reliable. Furthermore, the lack of alternative technologies for a reliable and volume producible contacting of embroidered circuits with electronic components or modules kept embroidered circuits from evolving to actual products. Therefore, this thesis primarily develops an understanding of the contact mechanism underlying embroidered contacts, and develops a theory that explains the failure phenomena. Secondarily the thesis overcomes these reliability issues by improving these contacts and by finding alternatives. The ultimate goal beyond this thesis is a volume producible contacting process. Therefore, this thesis looks mainly at machine embroidered contacts

Exploring the Use of Wearables to Enable Indoor Navigation for Blind Users

One of the challenges that people with visual impairments (VI) have to have to confront daily, is navigating independently through foreign or unfamiliar spaces.Navigating through unfamiliar spaces without assistance is very time consuming and leads to lower mobility. Especially in the case of indoor environments where the use of GPS is impossible, this task becomes even harder.However, advancements in mobile and wearable computing pave the path to new cheap assistive technologies that can make the lives of people with VI easier.Wearable devices have great potential for assistive applications for users who are blind as they typically feature a camera and support hands and eye free interaction. Smart watches and heads up displays (HUDs), in combination with smartphones, can provide a basis for development of advanced algorithms, capable of providing inexpensive solutions for navigation in indoor spaces. New interfaces are also introduced making the interaction between users who are blind and mo-bile devices more intuitive.This work presents a set of new systems and technologies created to help users with VI navigate indoor environments. The first system presented is an indoor navigation system for people with VI that operates by using sensors found in mo-bile devices and virtual maps of the environment. The second system presented helps users navigate large open spaces with minimum veering. Next a study is conducted to determine the accuracy of pedometry based on different body placements of the accelerometer sensors. Finally, a gesture detection system is introduced that helps communication between the user and mobile devices by using sensors in wearable devices