A novel biomechanical approach for animal behaviour recognition using accelerometers

Abstract

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.