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Data from: A novel biomechanical approach for animal behaviour recognition using accelerometers

Cite this dataset

Chakravarty, Pritish; Cozzi, Gabriele; Ozgul, Arpat; Aminian, Kamiar (2019). Data from: A novel biomechanical approach for animal behaviour recognition using accelerometers [Dataset]. Dryad.


Data from animal‐borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have been the tool of choice for characterising animal behaviour. Although machine learning models perform reasonably well, they may not rely on meaningful features, nor lend themselves to physical interpretation of the classification rules. This lack of interpretability and control over classification outcomes is of particular concern where different behaviours have different frequency of occurrence and duration, as in most natural systems, and calls for the development of alternative methods. Biomechanical approaches to human activity classification could overcome these shortcomings, yet their full potential remains untapped for animal studies. We propose a general framework for behaviour recognition using accelerometers, and develop a hybrid model where (a) biomechanical features characterise movement dynamics, and (b) a node‐based hierarchical classification scheme employs simple machine learning algorithms at each node to find feature‐value thresholds separating different behaviours. Using triaxial accelerometer data collected on 10 wild Kalahari meerkats, and annotated video recordings of each individual as groundtruth, this hybrid model was validated in three scenarios: (a) when each behaviour was equally represented (EQDIST), (b) when naturally imbalanced datasets were considered (STRAT) and (c) when data from new individuals were considered (LOIO). A linear‐kernel Support Vector Machine at each node of our classification scheme yielded an overall accuracy of >95% for each scenario. Our hybrid approach had a 2.7% better average overall accuracy than top‐performing classical machine learning approaches. Further, we showed that not all models with high overall accuracy returned accurate behaviour‐specific performance, and good performance during EQDIST did not always generalise to STRAT and LOIO. Our hybrid model took advantage of robust machine learning algorithms for automatically estimating decision boundaries between behavioural classes. This not only achieved high classification performance but also permitted biomechanical interpretation of classification outcomes. The framework presented here provides the flexibility to adapt models to required levels of behavioural resolution, and has the potential to facilitate meaningful model sharing between studies.

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