Albert Hein

Computational state space models for activity and intention recognition. A feasibility study.

Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance.

Utilizing an Accelerometric Bracelet for Ubiquitous Gesture-Based Interaction

In this paper we present an approach for recognizing free-handed gestures using an embedded wireless accelerometric bracelet. We developed a very low complexity algorithm which can be directly implemented on the device and operate in real-time. New gestures can be easily added through supervised learning. An evaluation shows the feasibility of our approach. Simple gestures are detected and recognized at a very high rate (> 97%) while more complex ones were misclassified more often (48% --- 95%).

Marginal filtering in large state spaces

We describe the marginal filter for activity recognition using symbolic models.The marginal filter allows fine-grained activity recognition using wearable sensors.We identify and discuss advantages over particle filters for symbolic models. Recognising everyday activities including information about the context requires to handle large state spaces. The usage of wearable sensors like six degree of freedom accelerometers increases complexity even more. Common approaches are unable to maintain an accurate belief state within such complex domains. We show how marginal filtering can overcome limitations of standard particle filtering and efficiently infer the context of actions. Symbolic models of human behaviour are used to recognise activities in two different settings with different state space sizes. Based on these scenarios we compare the marginal filter to the standard particle filter. An evaluation shows that the marginal filter performs comparably in small state spaces but outperforms the particle filter in large state spaces.

A Hybrid Approach for Recognizing ADLs and Care Activities Using Inertial Sensors and RFID

In this paper we present a feasibility study regarding the recognition of high level daily living and care activities. We examine a hybrid discriminative and model based generative approach based on RFID and inertial sensor data. We show that the presented sensor configuration is able to deliver sensor readings and object sightings at a sufficient rate without forcing user compliance. We further evaluated the advantage of a model based approach over a static classifier, compared the individual contribution of each sensor type and could reach accuracy rates of 97% and 85%.

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.

Generic performance metrics for continuous activity recognition

For evaluating activity recognition results still classical error metrics like Accuracy, Precision, and Recall are being used. They are well understood and widely accepted but entail fundamental problems: They can not handle fuzzy event boundaries, or parallel activities, and they over-emphasize decision boundaries. We introduce more generic performance metrics as replacement, allowing for soft classification and annotation while being backward compatible. We argue that they can increase the expressiveness and still allow more sophisticated methods like event and segment analysis.

Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals

Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

INSIDEDEM: TECHNOLOGY FOR MANAGING CHALLENGING BEHAVIOR IN DEMENTIA

Figure 1. Aut people with ad a group of peo panel: Behavi behavioral cat cumulated ove ticipants (test MANAGINGCHALLENGINGBEHAVIOR IN DEMENTIA Stefan J. Teipel, Margareta Halek, Albert Hein, Sebastian Bader, Thomas Kirste, Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany; German Center for Neurodegenerative Diseases, Witten, Germany; University of Rostock, Rostock, Germany. Contact e-mail: stefan.teipel@med.uni-rostock.de