Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull

The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy. •Motion sensors enables farmers to monitor their cattle's welfare more efficiently.•We enriched the algorithm of motion sensors with complex movements for more accuracy.•We used two machine learning classifications for complex movement recognition.•All forms of movement were successfully recognized with a rate of 90 % and above.