An automatic parameter extraction method for the 7x50m Stroke Efficiency Test

We developed an automatic method to extract the parameters of the 7 x 50m Stroke Eficiency Test for swimming based on a wrist worn acceleration sensor device. In the wrist acceleration signal we detect characteristic swim events such as wall push-offs, wall-strikes and strokes. Based on this information we compute the distance per stroke and the swimming velocity. The upper error bounds of our automatic method are 1.67% for the velocity and 1.33% for the time per stroke. The velocity measurement accuracy is of comparable order to the manual accuracy. The automatic method clearly outperforms the manual measurement for the time per stroke extraction

Distributed modular toolbox for multi-modal context recognition

We present a GUI-based C++ toolbox that allows for building distributed, multi-modal context recognition systems by plugging together reusable, parameterizable components. The goals of the toolbox are to simplify the steps from prototypes to online implementations on low-power mobile devices, facilitate portability between platforms and foster easy adaptation and extensibility. The main features of the toolbox we focus on here are a set of parameterizable algorithms including different filters, feature computations and classifiers, a runtime environment that supports complex synchronous and asynchronous data flows, encapsulation of hardware-specific aspects including sensors and data types (e.g., int vs. float), and the ability to outsource parts of the computation to remote devices. In addition, components are provided for group-wise, event-based sensor synchronization and data labeling. We describe the architecture of the toolbox and illustrate its functionality on two case studies that are part of the downloadable distribution. © Springer-Verlag Berlin Heidelberg 2006

Distributed modular toolbox for multi-modal context recognition

We present a GUI-based C++ toolbox that allows for building distributed, multi-modal context recognition systems by plugging together reusable, parameterizable components. The goals of the toolbox are to simplify the steps from prototypes to online implementations on low-power mobile devices, facilitate portability between platforms and foster easy adaptation and extensibility. The main features of the toolbox we focus on here are a set of parameterizable algorithms including different filters, feature computations and classifiers, a runtime environment that supports complex synchronous and asynchronous data flows, encapsulation of hardware-specific aspects including sensors and data types (e.g., int vs. float), and the ability to outsource parts of the computation to remote devices. In addition, components are provided for group-wise, event-based sensor synchronization and data labeling. We describe the architecture of the toolbox and illustrate its functionality on two case studies that are part of the downloadable distribution. © Springer-Verlag Berlin Heidelberg 2006

Distributed Modular Toolbox for Multi-modal Context Recognition

Abstract. We present a GUI-based C++ toolbox that allows for building distributed, multi-modal context recognition systems by plugging together reusable, parameterizable components. The goals of the toolbox are to simplify the steps from prototypes to online implementations on low-power mobile devices, facilitate portability between platforms and foster easy adaptation and extensibility. The main features of the toolbox we focus on here are a set of parameterizable algorithms including different filters, feature computations and classifiers, a runtime environment that supports complex synchronous and asynchronous data flows, encapsulation of hardware-specific aspects including sensors and data types (e.g., int vs. float), and the ability to outsource parts of the computation to remote devices. In addition, components are provided for group-wise, event-based sensor synchronization and data labeling. We describe the architecture of the toolbox and illustrate its functionality on two case studies that are part of the downloadable distribution.

Yhteisöllinen energiatehokkuus mobiililaitteilla

We have created a mobile energy measurement application and gathered energy measurement data from over 725,000 devices, running over 300,000 applications, in heterogeneous environments, and constructed models of what is normal in each context for each application. We have used this data to find energy abnormalities in the wild, and provide users of our application advice on how to deal with them. These abnormalities cannot be discovered in laboratory conditions due to the rich interaction of the smartphone and its operating environment. Employing a collaborative mobile energy awareness application with thousands of users allows us to gather a large amount of data in a short time. Such a large and diverse dataset has helped us answer many research questions. Our work is the first collaborative approach in the area of mobile energy debugging. Information received from each device running our application improves the advice given to other users running the same applications. The author has developed a context data gathering hub for smartphones, discovered the need for a common API that unifies network connectivity, energy awareness, and user experience, and investigated the impact of mobile collaborative energy awareness applications, to find previously unknown energy bugs on smartphones, and to improve users' knowledge of smartphone energy behavior.Viime vuosien aikana älypuhelinten laitteistot ovat kehittyneet entistä tehokkaammiksi, mutta akkuteknologia ei ole kehittynyt yhtä nopeasti. Tämä on synnyttänyt tarpeen tehostaa sekä laitteiston että ohjelmiston energiatehokkuutta. Älypuhelimen energiatehokkuuden optimointi on haastavaa, koska toimintaympäristö on moninainen ja käsittää paitsi laitteiston ja sen asetukset, niin myös sovellukset, jotka käyttävät laitteiston toimintoja. Tässä väitöstyössä on keskitytty mobiililaitteiden energiaongelmien ja poikkeamien löytämiseen ja niiden korjaamiseen. Väitöskirja käsittelee yhteisöllisen metodin käyttöä energiankulutukseen liittyvien epätehokkuuksien löytämisessä ja korjaamisessa mobiililaitteilla. Tätä metodia on ensimmäistä kertaa sovellettu mobiililaitteille väitöstyöhön liittyvässä Carat-projektissa. Projektissa on luotu energianmittaussovellus mobiililaitteille ja kerätty energiamittauksia yli 725 000 laitteelta ja 300 000 sovelluksesta monipuolisissa ympäristöissä. Näiden pohjalta on tehty malleja sovellusten normaalista energiankulutuksesta eri konteksteissa. Tietojen ja mallien avulla on löydetty energiapoikkeavuuksia tavallisessa käytössä olevilta laitteilta ja annettu sovelluksen käyttäjille neuvoja poikkeavuuksien korjaamiseen. Väitöstyön aikana kerätty suurikokoinen ja monipuolinen aineisto on auttanut vastaamaan moniin kysymyksiin koskien älypuhelinten energiankulutusta arkikäytössä. Kaikkia poikkeavuuksia ei voida löytää laboratorio-olosuhteissa, sillä mobiililaitteen ympäristö vaikuttaa vahvasti sen toimintaan. Esitetty menetelmä on ensimmäinen, joka soveltaa yhteisöllistä lähestymistapaa mobiililaitteiden energiaongelmien löytämiseen. Kirjoittaja on kehittänyt kontekstitietojen keräysratkaisun älypuhelimille. Hän on huomannut tarpeen järjestelmälle, joka yhdistää mobiililaitteen tilanteen, käytön, energiatehokkuuden ja käyttäjäkokemuksen. Työssä on kehitetty uusi menetelmä energiapoikkeamien analyysiin yhteisöllisesti kerättyjen mittausten perusteella sekä tutkittu energiatehokkuussovellusten vaikutusta eri mobiililaitteilla. Näiden avulla on löydetty ennen tuntemattomia energiaongelmia älypuhelimista ja parannettu käyttäjien ymmärrystä älypuhelinten energiakäyttäytymisestä

A Framework for Generic and Energy Efficient Context Recognition for Personal Mobile Devices

The advancements in the field of mobile computing over the last decade have enabled the scientific community to expedite the theoretical and experimental work to achieve the vision of ubiquitous computing. As ubiquitous computing aims to provide seamless and distraction free task support to its users, one of the essential pieces of information required by the ubiquitous computing systems to do so is the context of its users. Context of a user can be defined as the information that describes the task the user is performing and the environment in which the user is currently present. Among various platforms that are commonly used to determine user's context, the personal mobile devices like smart phones stand out as one of the most widely used and widely evaluated ones. However, despite numerous advantages that are provided by modern day personal mobile devices, such as high computational and communication capabilities, variety of on-board sensors to capture raw data related to user's motion and environment, high resolution displays to enable interaction with other services and systems, these devices suffer from limited battery resources. In contrast to the advancements in other domains, the advancements in the battery domain have not been up to the mark. Consequently, the context recognition applications developed for these devices suffer from the trade-off between achieving accuracy and longevity of other device's basic operations. As a result, most of the existing context recognition applications for these devices are fine tuned for specific context types and thereby lacks generality. The situation gets worse when a number of context recognition applications are executed simultaneously, thus competing for limited resources and consuming the device's battery additively. To address the aforementioned issues, this thesis provides a generic and energy efficient context recognition framework for personal mobile devices. The main contribution consists of a generic framework to support development of context recognition applications supported by algorithms to achieve their energy efficient execution. The proposed framework consists of two systems namely the component system and the activation system. The component system allows developers to create context recognition applications using a component abstraction. This enables runtime analysis of applications' structures to adopt our novel energy efficiency mechanism. The activation system uses a state machine abstraction to allow context dependent activation of context recognition configurations pertaining to relevant user's tasks such that only needed configurations are executed to determine only the relevant context characteristics, thereby enabling energy efficiency. The activation system also provides generic applicability of four different energy efficiency techniques, already used in different existing systems but mostly for specific context characteristics. To aid rapid prototyping, both systems are equipped with off-line development tools. The tools include graphical editors and a component tool-kit. The graphical editors allow developers to create component configurations used by the component system and state machines used by the activation system. These editors enable developers to create component configurations and state machines by simply dragging, dropping and connecting different models used in our component and state machine abstractions. These tools also provide validation and code generation utilities. In addition to the graphical editor, the framework provides a component tool-kit which consists of a number of already implemented sensing, preprocessing and classification components which can be re-used in new applications. In order to provide the energy efficient execution of context recognition applications, the thesis introduces a novel energy efficiency technique called configuration folding. Configuration folding analyses structures of simultaneously executing context recognition applications to identify redundant functionalities between them and as an output produces a single redundancy free context recognition configuration which holds the structural integrity of all applications. Consequently, the overall energy expenditure is reduced compared to the original expenditure when redundant functionalities are not removed. The experimental evaluation of configuration folding on test applications shows energy savings between 13 and 48 %. The thesis also investigates optimization possibilities in configuration folding in case the redundant functionalities between the applications differ in parametrization. Towards this end, the thesis identifies commonly used parameters in context recognition applications and defines relations between them. Finally, an extended version of configuration folding is introduced to handle the differences in parametrization. The evaluation of the extended version of configuration folding on test scenarios shows energy saving of up to 45%. The contributions in this thesis have been evaluated extensively. The framework has been used in number of European Commission (EC) projects and in student projects and theses at the University of Duisburg-Essen, Germany. Using the component system and the activation system, a number of applications have been developed in those projects. Some of these applications include crowd density estimation in buses, bus ride detections, navigation application for buses in Madrid, user movement detection, user localization, fall detection application etc. Moreover, the component system, the activation system and the configuration folding technique have been published in different prestigious conferences and workshops

Multi-modal on-body sensing of human activities

Increased usage and integration of state-of-the-art information technology in our everyday work life aims at increasing the working efficiency. Due to unhandy human-computer-interaction methods this progress does not always result in increased efficiency, for mobile workers in particular. Activity recognition based contextual computing attempts to balance this interaction deficiency. This work investigates wearable, on-body sensing techniques on their applicability in the field of human activity recognition. More precisely we are interested in the spotting and recognition of so-called manipulative hand gestures. In particular the thesis focuses on the question whether the widely used motion sensing based approach can be enhanced through additional information sources. The set of gestures a person usually performs on a specific place is limited -- in the contemplated production and maintenance scenarios in particular. As a consequence this thesis investigates whether the knowledge about the user's hand location provides essential hints for the activity recognition process. In addition, manipulative hand gestures -- due to their object manipulating character -- typically start in the moment the user's hand reaches a specific place, e.g. a specific part of a machinery. And the gestures most likely stop in the moment the hand leaves the position again. Hence this thesis investigates whether hand location can help solving the spotting problem. Moreover, as user-independence is still a major challenge in activity recognition, this thesis investigates location context as a possible key component in a user-independent recognition system. We test a Kalman filter based method to blend absolute position readings with orientation readings based on inertial measurements. A filter structure is suggested which allows up-sampling of slow absolute position readings, and thus introduces higher dynamics to the position estimations. In such a way the position measurement series is made aware of wrist motions in addition to the wrist position. We suggest location based gesture spotting and recognition approaches. Various methods to model the location classes used in the spotting and recognition stages as well as different location distance measures are suggested and evaluated. In addition a rather novel sensing approach in the field of human activity recognition is studied. This aims at compensating drawbacks of the mere motion sensing based approach. To this end we develop a wearable hardware architecture for lower arm muscular activity measurements. The sensing hardware based on force sensing resistors is designed to have a high dynamic range. In contrast to preliminary attempts the proposed new design makes hardware calibration unnecessary. Finally we suggest a modular and multi-modal recognition system; modular with respect to sensors, algorithms, and gesture classes. This means that adding or removing a sensor modality or an additional algorithm has little impact on the rest of the recognition system. Sensors and algorithms used for spotting and recognition can be selected and fine-tuned separately for each single activity. New activities can be added without impact on the recognition rates of the other activities