The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study

Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (16), p.7165
Main Authors: Smit, Michelle, Ikurior, Seer J., Corner-Thomas, Rene A., Andrews, Christopher J., Draganova, Ina, Thomas, David G.
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Accuracy
Algorithms
Analysis
Animals
behaviour classification
Cats
Data collection
Data mining
Discriminant analysis
domestic cat
Eating behavior
Kappa coefficient
Machine learning
random forest
self-organizing map
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Summary:Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64–76% and 70–86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23167165