Ben J. A. Kröse

Accurate activity recognition in a home setting

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.

Efficient greedy learning of Gaussian mixture models

This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points and quadratic in the (final) number of mixture components. Due to its greedy nature, the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation are provided.

Assessing acceptance of assistive social agent technology by older adults: the Almere model

This paper proposes a model of technology acceptance that is specifically developed to test the acceptance of assistive social agents by elderly users. The research in this paper develops and tests an adaptation and theoretical extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) by explaining intent to use not only in terms of variables related to functional evaluation like perceived usefulness and perceived ease of use, but also variables that relate to social interaction. The new model was tested using controlled experiment and longitudinal data collected regarding three different social agents at elderly care facilities and at the homes of older adults. The model was strongly supported accounting for 59–79% of the variance in usage intentions and 49–59% of the variance in actual use. These findings contribute to our understanding of how elderly users accept assistive social agents.

A probabilistic model for appearance-based robot localization

Abstract In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel estimation method. We show how the parameters of the model influence the localization performance. We also study how many features and which features are needed for good localization.

An activity monitoring system for elderly care using generative and discriminative models

An activity monitoring system allows many applications to assist in care giving for elderly in their homes. In this paper we present a wireless sensor network for unintrusive observations in the home and show the potential of generative and discriminative models for recognizing activities from such observations. Through a large number of experiments using four real world datasets we show the effectiveness of the generative hidden Markov model and the discriminative conditional random fields in activity recognition.

Distributed adaptive control: The self-organization of structured behavior

Abstract Starting with a neural model for classical conditioning we have developed a system for robot control that is completely self-organizing. Instead of relying on predefined control rules the system will develop adapted control by interacting with its environment. We have tested this model in navigation tasks. Our results demonstrate that the level at which control models are normally defined seems to emerge out of the neural level which implements our control architecture.

Jijo-2: an office robot that communicates and learns

Describes how the authors have combined speech recognition, dialogue management, and statistical learning procedures to develop Jijo-2; an office robot that can communicate with humans and learn about its environment.

Transferring knowledge of activity recognition across sensor networks

A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper we introduce a transfer learning method for activity recognition which allows the use of existing labelled data sets of various homes to learn the parameters of a model applied in a new home. We evaluate our method using three large real world data sets and show our approach achieves good classification performance in a home for which little or no labelled data is available.

Enjoyment intention to use and actual use of a conversational robot by elderly people

In this paper we explore the concept of enjoyment as a possible factor influencing acceptance of robotic technology by elderly people. We describe an experiment with a conversational robot and elderly users (n=30) that incorporates both a test session and a long term user observation. The experiment did confirm the hypothesis that perceived enjoyment has an effect on the intention to use a robotic system. Furthermore, findings show that the general assumption in technology acceptance models that intention to use predicts actual use is also applicable to this specific technology used by elderly people.

From images to rooms

In this paper we start from a set of images obtained by the robot that is moving around in an environment. We present a method to automatically group the images into groups that correspond to convex subspaces in the environment which are related to the human concept of rooms. Pairwise similarities between the images are computed using local features extracted from the images and geometric constraints. The images with the proposed similarity measure can be seen as a graph or in a way as a base level dense topological map. From this low level representation the images are grouped using a graph-clustering technique which effectively finds convex spaces in the environment. The method is tested and evaluated on challenging data sets acquired in real home environments. The resulting higher level maps are compared with the maps humans made based on the same data.