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.

Distributed modular toolbox for multi-modal context recognition

A graphical program development system for developing a graphical program, wherein the development system utilizes a configuration matrix for representing the graphical program. The matrix may graphically illustrate or specify the operation of the computer program. The matrix may comprise columns (or rows) displayed on the display device, including one or more columns which comprise symbols for functional elements and one or more interconnection columns that include interconnections between two or more of the symbols for functional elements. Once the matrix (the computer program) has been created, the computer program may be executed, wherein the computer program executes according to the interconnected symbols for functional elements displayed in the matrix.

Method for configuration and parameterization of a graphical computer program for the operation of a data processing system

Today’s pervasive availability of computing devices enabled with wireless communication and location- or inertial sensing capabilities is unprecedented. The number of smartphones sold worldwide are still growing and increasing numbers of sensor enabled accessories are available which a user can wear in the shoe or at the wrist for fitness tracking, or just temporarily puts on to measure vital signs. Despite this availability of computing and sensing hardware the merit of application seems rather limited regarding the full potential of information inherent to such senor deployments. Most applications build upon a vertical design which encloses a narrowly defined sensor setup and algorithms specifically tailored to suit the application’s purpose. Successful technologies, however, such as the OSI model, which serves as base for internet communication, have used a horizontal design that allows high level communication protocols to be run independently from the actual lower-level protocols and physical medium access. This thesis contributes to a more horizontal design of human activity recognition systems at two stages. First, it introduces an integrated toolchain to facilitate the entire process of building activity recognition systems and to foster sharing and reusing of individual components. At a second stage, a novel method for automatic integration of new sensors to increase a system’s performance is presented and discussed in detail.
The integrated toolchain is built around an efficient toolbox of parametrizable components for interfacing sensor hardware, synchronization and arrangement of data streams, filtering and extraction of features, classification of feature vectors, and interfacing output devices and applications. The toolbox emerged as open-source project through several research projects and is actively used by research groups. Furthermore, the toolchain supports recording, monitoring, annotation, and sharing of large multi-modal data sets for activity recognition through a set of integrated software tools and a web-enabled database.
The method for automatically integrating a new sensor into an existing system is, at its core, a variation of well-established principles of semi-supervised learning: (1) unsupervised clustering to discover structure in data, (2) assumption that cluster membership is correlated with class membership, and (3) obtaining at a small number of labeled data points for each cluster, from which the cluster labels are inferred. In most semi-supervised approaches, however, the labels are the ground truth provided by the user. By contrast, the approach presented in this thesis uses a classifier trained on an N-dimensional feature space (old classifier) to provide labels for a few points in an (N+1)-dimensional feature space which are used to generate a new, (N+1)-dimensional classifier. The different factors that make a distribution difficult to handle are discussed, a detailed description of heuristics designed to mitigate the influences of such factors is provided, and a detailed evaluation on a set of over 3000 sensor combinations from 3 multi-user experiments that have been used by a variety of previous studies of different activity recognition methods is presented.

https://kluedo.ub.uni-kl.de/files/4192/diss.pdf

Tools and Methods to Support Opportunistic Human Activity Recognition

This article deals with one of the major problems in component-based software development: the derivation of properties of a component system from given properties of components and rules for their interaction. In the area of measurement, signal processing, and control in embedded systems, components encapsulating signal processing algorithms or signal adaptation algorithms can be modeled by means of a hierarchy of dataflow languages: synchronous (SDF), Boolean controlled (BDF), and dynamic (DDF) dataflow. If the application engineer responsible for component assembly restricts to the use of SDF components, the component system will be computationally analyzable. That is, it can be decided if it is deadlock-free, the required amount of memory can be determined, and a cyclic schedule of component instances can be computed. If the application engineer uses SDF and BDF components only, the component system will still be deterministic. The objective of this paper is to describe a novel concept of a component framework for the aforementioned application area which can automatically determine certain global properties of a component system during component assembly, whenever possible.

Towards a specification technique for component-based measurement and control software for embedded systems

Component-based Measurement and Control Software for Embedded Systems

A framework with a tool-supported high-level specification technique is very important for the development of complex signal processing applications containing software-intensive parts (e.g. hybrid systems in automated production processes) in order to provide safe and reliable systems. In this paper we present the concept of a framework, which is an object-oriented CASE-tool offering a graphical specification ability to model and validate a given application and to control its execution. A variety of people having different programming skills is able to use this visual specification technique effectively. Especially users not being interested in implementation details can specify their application on a high abstraction level by connecting reusable and reliable components (modules representing basic algorithms). As a result, complex signal graphs representing the dataflow between the modules are created. The tool supports this software specification technique by automatic type checking for the connections between modules and by changeable module parameters. On the other hand it is easy for software engineers to integrate additional signal processing algorithms into the framework thus building suitable module libraries without considering a specific high-level application.

A framework for the graphical specification and execution of complex signal processing applications

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A framework for the graphical specification and execution of complex signal processing applications