Computational state space models for activity and intention recognition. A feasibility study.
Computational state space models for activity and intention recognition. A feasibility study.
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DOI：
10.1371/journal.pone.0109381
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Kirste T
中科院分区：
文献类型：
--
作者：
Krüger F;Nyolt M;Yordanova K;Hein A;Kirste T
计算状态空间模型 (CSSM) 支持基于知识的贝叶斯过滤器构建，用于识别智能环境、辅助生活或安全等应用领域中人类主角的意图并重建活动。计算，i。即算法表示允许构建日益复杂的人类行为模型。然而，CSSM 中使用的符号模型可能会遭受组合爆炸的影响，导致在当前研究中有限的实验设置之外进行推理变得困难。本研究的目的是获得有关基于 CSSM 的推理在现实复杂性领域中的可行性的数据。日常生活中典型的器乐活动被用作试验场景。作为主要传感器方式，采用了可穿戴惯性测量单元。通过使用 Wilcoxon 符号秩检验与已建立的基于训练的方法（隐马尔可夫模型，HMM）获得的结果进行比较，评估了 CSSM 方法可实现的结果。通过重复测量方差分析来分析建模因素对 CSSM 性能的影响。人们发现符号域模型具有多个状态，超出了先前研究中考虑的模型的复杂性至少三个数量级。然而，如果适当选择控制推理过程的因素和程序，CSSM 的表现优于 HMM。具体而言，发现先前研究中使用的推理方法（粒子过滤器）的性能远远低于边缘过滤程序。我们的结果表明，丰富的 CSSM 模型引起的组合爆炸并不必然导致难以处理的推理或较差的性能。这意味着 CSSM 模型的潜在好处（基于知识的模型构建、模型可重用性、减少对训练数据的需求）是可用的，而不会造成性能损失。然而，我们的结果还表明，CSSM 的研究需要考虑足够复杂的领域，以便理解设计决策的影响，例如启发式或推理过程的选择对性能的影响。
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. 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. 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. 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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