Sebastian Bader

Tracking Assembly Processes and Providing Assistance in Smart Factories

Tracking assembly processes is a necessary prerequisite to provide assistance in smart factories. In this paper, we show how to track the construction of complex components. For this we employ formal task models as background knowledge and simple sensors like RFIDs. The background knowledge is converted into a probabilistic model that actually tracks the process. As a result, we are able to provide assistance in smart factories. We discuss the performance of the approach, as well as potential applications.

Information Assistance for Smart Assembly Stations

Information assistance helps in many application domains to structure, guide and control human work processes. However, it lacks a formalisation and automated processing of background knowledge which vice versa is required to provide ad-hoc assistance. In this paper, we describe our conceptual and technical work to include contextual background knowledge in raising awareness, guiding, and monitoring the assembly worker. We present cognitive architectures as missing link between highly sophisticated manufacturing data systems and implicitly available contextual knowledge on work procedures and concepts of the work domain. Our work is illustrated with examples in SWI-Prolog and the Soar cognitive architecture.

Situation aware interaction with multi-modal business applications in smart environments

A consistent user experience in combination with proactive assistance may improve the user performance while interacting with heterogeneous data sources as e.g., occurring in business decision making. We describe our approach which is based on inferring the user intentions from sensory inputs, providing a situation aware information assistance, and controlling the environment proactively by anticipating future goals. Our system has been realized within a smart meeting room and has in parts been evaluated. In this paper, we describe the core ideas underlying our approach and report on first findings from the evaluation.

A context-aware publish-subscribe middleware for distributed smart environments

We present and discuss a context aware publish subscribe system. This allows for efficient communication between different components within distributed environments. For this we introduce context-awareness into the subscription handling. Our middleware allows to attach context properties to event subscriptions. Only if those properties are satisfied by the current state of the world, the events are distributed within the system, which minimises the communication. Finally we discuss a larger use-case for our system.

Synthesising Generative Probabilistic Models for High-Level Activity Recognition

High-level (hierarchical) behaviour with long-term correlations is difficult to describe with first-order Markovian models like Hidden Markov models. We therefore discuss different approaches to synthesise generative probabilistic models for activity recognition based on different symbolic high-level description. Those descriptions of complex activities are compiled into robust generative models. The underlying assumptions for our work are (i) we need probabilistic models in robust activity recognition systems for the real world, (ii) those models should not necessarily rely on an extensive training phase and (iii) we should use available background knowledge to initialise them. We show how to construct such models based on different symbolic representations.

A middleware for rapid prototyping smart environments: experiences in research and teaching

While developing distributed systems, like for example a smart environment, a powerful middleware is required - not only for the communication between different devices, but also to support the developers. In this paper, we discuss our system, which has been developed with a special focus on the needs in research and teaching in ubiquitous computing. It is based on a tuple space as underlying storage and a simple network protocol. The system turns out to be very well suited for both application areas.

Evaluating the robustness of activity recognition using computational causal behavior models

Activity recognition is a challenging research problem in ubiquitous computing domain and has to tackle omnipresent uncertainties, e.g., resulting from ambiguous or intermittent sensor readings. In this paper, we introduce an activity recognition approach based on causal modeling and probabilistic plan recognition. To evaluate the performance of our approach systematically, we generated sensor data with different error rates using a simulation. This data served as input for the activity recognition in a series of experiments. In these experiments we stepwise introduced and combined additional sources of uncertainty, i.e., different duration models and ignoring certain sensors, to demonstrate the robustness of our approach. Our evaluation shows that Computational Causal Behavior Models provide a basis for a robust activity recognition system.

On the applicability of clinical observation tools for human activity annotation

The annotation of human activity is a crucial prerequisite for applying methods of supervised machine learning. It is typically either obtained by live annotation by the participant or by video log analysis afterwards. Both methods, however, suffer from disadvantages when applied in dementia related nursing homes. On the one hand, people suffering from dementia are not able to produce such annotation and on the other hand, video observation requires high technical effort. The research domain of quality of care addresses these issues by providing observation tools that allow the simultaneous live observation of up to eight participants - dementia care mapping (DCM). We developed an annotation scheme based on the popular clinical observation tool DCM to obtain annotation about challenging behaviours. In this paper, we report our experiences with this approach and discuss the applicability of clinical observation tools in the domain of automatic human activity assessment.

Challenges of collecting empirical sensor data from people with dementia in a field study

Collecting annotated sensor data in real life field studies is a challenging task, especially when observing people with dementia. In this paper we outline our attempt on conducting a large scale experimental study while focusing on the technical aspects. We conclude by giving a brief summary of the obtained data set and reporting our lessons learned.

Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes—The insideDEM framework

Assessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long‐term multidimensional behavior assessment of people with dementia living in nursing homes.