Evaluating the robustness of activity recognition using computational causal behavior models

Steiniger, Alexander; Bader, Sebastian; Kirste, Thomas

doi:10.1145/2370216.2370443

Cited by 11 publications

(2 citation statements)

References 15 publications

(6 reference statements)

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“…A TP (TN) indicates a sample in the positive (negative) class was classified correctly, and an FP (FN) a sample in the negative (positive) class that was classified as positive (negative). The multi-class classification model of the confusion matrix can then be extrapolated as follows (Krüger, 2016), see Figure 4. Per row n ∈ C, the confusion matrix E ∈ N (N +1)×(N +1) comprises a 1 × (N + 1) vector whose n ′ -th entry is m:cm=n 1 {nm=n ′ } .…”

Section: Discussionmentioning

confidence: 99%

An Introductory Approach to Time-Series Data Preparation and Analysis

Baumann,

Hsu,

Buba

et al. 2023

PHM_CONF

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Machine learning (ML)/Artificial Intelligence (AI) has widespread applications and has revolutionized many industries due to advanced and matured sensor technology, as well as large-scale data collection efforts. One of the key tasks for effective ML/AI operations is the extraction and identification of useful and usable data to identify complex interrelationships and solve problems efficiently. The usefulness of the data is the value and meaning of the data within the desired model, while the usability of the data refers to the ease of use of data in a model. Complex supervised and unsupervised ML models, which used to be the domain of cutting-edge scientists and academics, can now be invoked as a basic function calls in public domain packages within Python, R, MATLAB, and other languages. While these functions require effective data preprocessing to overcome the unpredicted impacts of data quality in the real world (e.g. missing data, environmental noise, synchronizing at different sampling rates, etc.), their ease of use means they are often called with little to no understanding of the underlying math or ways to efficiently work through the data set. The approachability provided by the packages enables users to dive into complex problem sets with little advance preparation. However, in doing so there is a lack of understanding which will inevitably cause problems, skew results, or force the user to take a less efficient path to get to a similar answer. Each package provides relatively simple examples that deal with specific public data sets, yet not many provide the background knowledge and comprehensive methods required for building the inputs for extensive and effective time-series data modeling. Typically, the complex nature of time-series data requires an in-depth understanding of signals analysis and domain subject expertise to use in ML/AI predictive models. This paper will provide the reader an overview of the problems associated with time-series data modelling, propose a common set of preprocessing steps to follow, demonstrate a taxonomy classification for time series data, provide introductory reasoning regarding the underlying process, and discuss the models that would benefit from such a methodology. This is done here with the goal of equipping non-knowledge-domain experts with updated and approachable techniques to find which features to focus on while preprocessing for their time-series data preparation efforts.

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Section: Discussionmentioning

confidence: 99%

An Introductory Approach to Time-Series Data Preparation and Analysis

Baumann,

Hsu,

Buba

et al. 2023

PHM_CONF

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“…To overcome the problem of the multi-interpretable nature of LTL, the author convert them into deterministic finite automatons such that they can be embedded as hierarchical graph into the loss calculation of the neural network's training process and achieve learning consistent to the LTL rules and better accuracy at classification. The works by Yordanaova et al [5] and Rueda et al [6] use computational causal behavioral models (CCBM) [20] to model domain knowledge and reason about the current state of the environment. CCBM also uses PPDL-like rules to describe the domain-specific properties of the activities.…”

Section: Related Workmentioning

confidence: 99%

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

Popko¹,

Bader²,

Lüdtke³

et al. 2022

Preprint

Self Cite

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The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter-and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.

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Extracting Planning Operators from Instructional Texts for Behaviour Interpretation

Yordanova

2018

Lecture Notes in Computer Science

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Evaluating the robustness of activity recognition using computational causal behavior models

Cited by 11 publications

References 15 publications

An Introductory Approach to Time-Series Data Preparation and Analysis

An Introductory Approach to Time-Series Data Preparation and Analysis

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

Extracting Planning Operators from Instructional Texts for Behaviour Interpretation

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