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

Krüger F, Nyolt M, Yordanova K, Hein A, Kirste T - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Institute, University of Rostock, Rostock, Germany.

ABSTRACT

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 10(8) 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.

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Instrumentation and trial setting.Left: Instrumentation of participants (red points indicate IMU positions). Right: Conceptual spatial layout (view from above) and domain objects of trial setting.
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pone-0109381-g001: Instrumentation and trial setting.Left: Instrumentation of participants (red points indicate IMU positions). Right: Conceptual spatial layout (view from above) and domain objects of trial setting.

Mentions: A typical meal time routine was selected as trial setting, consisting of the following major tasks: (i) Prepare meal (prepare ingredients; cook meal). (ii) Set table. (iii) Eat meal. (iv) Clean up and put away utensils. (A symbolic map of the spatial structure of the trial domain and the involved domain objects are given in Fig. 1; a more detailed task sequence is given in Table S1). Selection of this scenario is based on the following considerations:


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

Krüger F, Nyolt M, Yordanova K, Hein A, Kirste T - PLoS ONE (2014)

Instrumentation and trial setting.Left: Instrumentation of participants (red points indicate IMU positions). Right: Conceptual spatial layout (view from above) and domain objects of trial setting.
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Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4220990&req=5

pone-0109381-g001: Instrumentation and trial setting.Left: Instrumentation of participants (red points indicate IMU positions). Right: Conceptual spatial layout (view from above) and domain objects of trial setting.
Mentions: A typical meal time routine was selected as trial setting, consisting of the following major tasks: (i) Prepare meal (prepare ingredients; cook meal). (ii) Set table. (iii) Eat meal. (iv) Clean up and put away utensils. (A symbolic map of the spatial structure of the trial domain and the involved domain objects are given in Fig. 1; a more detailed task sequence is given in Table S1). Selection of this scenario is based on the following considerations:

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Institute, University of Rostock, Rostock, Germany.

ABSTRACT

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 10(8) 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.

Show MeSH