Cat_Accelerometry

Buiding predictive models to predict cat behaviour using accelerometers

A lot of the code used for the Self-Organizing Maps was published on GitHub by cclemente in the repository Animal_accelerometry.

Step 1: Data collection

For this study, two accelerometers were attached to domestic cats:

• Ventrally attached to a collar
• Attached to a harness, with the activity monitor resting on the left shoulder blade

While wearing the accelerometers, cats were videorecorded. Videorecordings were used to score the behaviour of cats.

ActiGraph wGT3X-BT accelerometers were used.

Step 2: Feature engineering

Acceleration data was sampled at a frequency of 30 Hz (raw data). A total of 32 predictor variabes were derived from the raw x,y,z accelerometer data and summarized into 1 second epochs.

Step 3: Data preparation

Following step 2, you now have a file for each cat containing the 32 predictor variables. The next step is to merge this data with scored behaviour (annotated) data.

Step 4: Build predictive models

Following step 4, you now have the datasets you can use to build the predictive models. In this study, two Machine Learning techniques were used to build predictive models:

• Random Forest
• Self-Organizing maps

Step 5: Use models to predict behaviour

With the models built in step 4, you can now use the model to predict the behaviour using the 32 predictor variables derived from the raw accelerometer data

Step 6: Comparing models

The models were compared for the proportions of the behaviours they predicted. This was done by comparing proportional graphs and by a Dirichlet test.

Step 7: Rater reliability

Rater reliability was tested by rewatching some videorecordings and rescoring the behaviour. This second scoring was then compared to the first scoring to determine rater reliability