R package for animal behavior classification from accelerometer data—rabc

Yu, Hui; Klaassen, Marcel

doi:10.1002/ece3.7937

Cited by 14 publications

(9 citation statements)

References 46 publications

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“…Tri-axial accelerometer, which measure acceleration in three dimensions and provide information about energy expenditure, are particularly promising ( Nathan et al, 2012 ). Here, especially supervised machine learning methods such as random forests and XGBoost show a good performance and allow for a fine distinction of behavioral categories ( Dentinger et al, 2022 ; Nathan et al, 2012 ; Sur et al, 2023 ; Yu & Klaassen, 2021 ). However, supervised learning requires labeled data, i.e.…”

Section: Discussionmentioning

confidence: 99%

How to account for behavioral states in step-selection analysis: a model comparison

Pohle,

Signer,

Eccard

et al. 2024

PeerJ

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Step-selection models are widely used to study animals’ fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, often depend on the animal’s unobserved behavioral states, such as resting or foraging. As this is ignored in standard (integrated) step-selection analyses (SSA, iSSA), different approaches have emerged to account for such states in the analysis. The performance of these approaches and the consequences of ignoring the states in step-selection analysis, however, have rarely been quantified. We evaluate the recent idea of combining iSSAs with hidden Markov models (HMMs), which allows for a joint estimation of the unobserved behavioral states and the associated state-dependent habitat selection. Besides theoretical considerations, we use an extensive simulation study and a case study on fine-scale interactions of simultaneously tracked bank voles (Myodes glareolus) to compare this HMM-iSSA empirically to both the standard and a widely used classification-based iSSA (i.e., a two-step approach based on a separate prior state classification). Moreover, to facilitate its use, we implemented the basic HMM-iSSA approach in the R package HMMiSSA available on GitHub.

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Section: Discussionmentioning

confidence: 99%

How to account for behavioral states in step-selection analysis: a model comparison

Pohle,

Signer,

Eccard

et al. 2024

PeerJ

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“…Feature selection improves the performance of machine learning algorithms and reduces the computational time (Kuhn, 2008 , 2012 ). We used the function “findCorrelation” within “caret” to create a correlation matrix and find and remove highly correlated accelerometer‐derived metrics (Yu & Klaassen, 2021 ). The function considers the absolute values of pairwise correlations and, when two variables are highly correlated, it removes the variable with the largest mean absolute correlation.…”

Section: Methodsmentioning

confidence: 99%

Supervised versus unsupervised approaches to classification of accelerometry data

Sur¹,

Hall

Brandt

et al. 2023

Ecology and Evolution

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Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation-maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

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“…The behaviour‐classification model training followed the process as outlined by Yu and Klaassen (2021) in their description of the rabc R package, in which we reduced the initial 16 behavioural categories to 8, based on their functional similarities and their similarities in accelerometer recordings (Table 1). For detailed information on model training and on‐board data compression, see Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Continuous on‐board behaviour classification using accelerometry: A case study with a new GPS‐3G‐Bluetooth system in Pacific black ducks

Deng²,

Leen³

et al. 2022

Methods Ecol Evol

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The continuous development and improvement of tracking devices has enabled researchers to study animal movement ecology, physiology and behaviours in ever-increasing detail (Williams et al., 2019;Wilson et al., 2019). In addition to positional data, various micro-sensors on-board of tracking devices enable the monitoring of different aspects of the wildlife tracked as well as their environment (Ropert-Coudert & Wilson, 2005). Accelerometer (ACC) data is one such feature measured with micro-sensors that is increasingly used to study animal behaviours and energetics (e.g. Williams et al., 2014).

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R package for animal behavior classification from accelerometer data—rabc

Cited by 14 publications

References 46 publications

How to account for behavioral states in step-selection analysis: a model comparison

How to account for behavioral states in step-selection analysis: a model comparison

Supervised versus unsupervised approaches to classification of accelerometry data

Continuous on‐board behaviour classification using accelerometry: A case study with a new GPS‐3G‐Bluetooth system in Pacific black ducks

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