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

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.