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On the Development of a Wearable Animal Monitor.

Luís Fonseca1, Daniel Corujo1, William Xavier2

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Wearable sensors offer an efficient, low-cost method for animal monitoring. Removing the gyroscope from animal collars improves behavior classification accuracy, achieving around 91%.

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Area of Science:

  • Agricultural Science
  • Animal Science
  • Internet of Things (IoT)

Background:

  • Traditional animal monitoring by pastoralists is labor-intensive and time-consuming.
  • Wearable sensors offer a less invasive, cost-effective, and efficient alternative for animal monitoring.
  • The Internet of Things (IoT) enables advanced data collection for animal behavior analysis.

Purpose of the Study:

  • To analyze the impact of different sensor features on animal behavior learning.
  • To evaluate the influence of gyroscope inclusion/exclusion on cost and learning outcomes.
  • To assess the generalizability of a learned animal behavior model to younger animals.

Main Methods:

  • Implementation of wearable sensors on animals for data collection.
  • Development of a machine learning model for behavior classification.
  • Comparative analysis of model performance with and without gyroscope data.
  • Evaluation of model accuracy using sensed data from adult and younger animals.

Main Results:

  • Accurate classification of animal behaviors with a balanced accuracy of approximately 91%.
  • Exclusion of the gyroscope sensor demonstrated an advantageous impact on classification accuracy and cost.
  • The thermometer sensor showed a positive contribution to behavior identification, warranting further investigation.

Conclusions:

  • Wearable sensor technology, particularly without a gyroscope, is effective for accurate animal behavior classification.
  • Future research should explore diverse animal behaviors, seasonal variations, and longer-term data collection for enhanced model robustness.