Client Collaboration: We collaborated with an esteemed research team dedicated to advancing horse welfare. Our joint effort focused on developing quantitative measures to assess the well-being of horses through controlled experiments.
Technical Innovation: Our contribution involved crafting custom Python code tailored to run seamlessly on a Raspberry Pi device. This setup was pivotal in automating the control and data collection processes during behavioral experiments with horses.
Analytical Approach: The heart of our analysis lay in meticulously examining the behavioral data gathered. We employed statistical methods and graphical analysis to decode the intricate patterns of horse behavior, providing a scientific basis for evaluating the efficacy of various training methodologies.
Outcome: The insights gleaned from our analysis offered a transformative perspective on horse training techniques, shedding light on their impact on learning and performance. This project not only propelled the research team’s understanding of equine welfare but also set a new benchmark for evidence-based animal training practices.
Our project capitalised on a comprehensive and scalable suite of mature open-source components, meticulously chosen to fulfill the rigorous requirements of experimental control and contemporary data analytics.
Client Infrastructure:
- Raspberry Pi: Leveraged for its compact yet powerful capabilities, interfaced with touch sensors and motor controls for feed regulation.
Data Sources:
- Custom Log Files: Strategically structured for optimal data retrieval, fault tolerance and analysis.
Analytics Tools:
- DuckDB: A contemporary SQL-based analytics database renowned for its speed and depth in data analysis.
- Pandas: The quintessential library for data manipulation, offering high-performance structures ideal for complex data operations.
Web Application Framework:
- Streamlit: This innovative framework transforms data scripts into interactive and shareable web applications, streamlining the user experience.
This technical stack not only supported the project’s objectives but also provided a robust foundation for future scalability and innovation.