motion2health 1.0.0

Behaviour-mediated and direct prediction workflows for livestock health monitoring using accelerometry

Motion2Health: Livestock Accelerometry & Health Outcome Prediction

Motion2Health is a SOTA Python package and research framework for livestock health outcome prediction (Gamma-Glutamyl Transferase - GGT) using high-frequency continuous accelerometry data and behavior classification.

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🚀 Key Features

• Unified Predictor Engine (GGTPredictor): Easily load pre-trained classical and PyTorch deep learning models to predict livestock health outcomes.
• 12 Benchmarking Workflows:
  • Classical Tabular Models: ElasticNet, LME, RandomForest, SVM
  • Deep Learning Models: MultiScale-CNN + LSTM, MultiScale-CNN + Causal Transformer
  • Direct Sensor & Behavior-Disentangled Architectures
• End-to-End Pipeline: From 1 Hz raw tri-axial accelerometer CSV data to behavior estimation (Grazing, Ruminating, Standing, Lying) and GGT trajectory forecasting.

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📦 Installation

Install directly via pip:

pip install motion2health

Or install from source:

git clone https://github.com/SydneyBioX/motion2health.git
cd motion2health
pip install -e .

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💡 Quick Start

from motion2health import GGTPredictor

# Initialize the predictor using the behavior-disentangled Random Forest model
predictor = GGTPredictor(workflow="behaviour_rf")

# Predict GGT outcome on Study Day 14
predicted_ggt = predictor.predict_ggt(
    csv_path="data/sample_animal.csv",
    baseline_ggt=32.0,    # Pre-trial baseline GGT (U/L)
    study_day=14          # Target day (-7 to 24)
)

print(f"Predicted GGT Value: {predicted_ggt:.2f} U/L")

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🧪 Deep Learning (PyTorch Transformer) Workflow

# Initialize deep learning Transformer workflow
predictor_dl = GGTPredictor(workflow="behaviour_transformer")

# Predict GGT with Monte Carlo Test-Time Augmentation (TTA)
predicted_ggt_dl = predictor_dl.predict_ggt(
    csv_path="data/sample_animal.csv",
    baseline_ggt=28.5,
    study_day=7
)

print(f"Transformer Predicted GGT: {predicted_ggt_dl:.2f} U/L")

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📖 Citation & Repository

Public research code, analysis scripts, and evaluation benchmarks are available in the official repository:

• Repository: SydneyBioX/motion2health

License: MIT License