Summer School Sensor Data and AI in Animal and Human Health

About

Do you feel like technology is moving faster than you can keep up with? You are not alone. Professionals working with animals and humans are increasingly expected to work with sensors, AI, machine learning and digital twins. Yet the biological meaning and practical value are not always clear, and understanding them often seems to require advanced mathematics or programming. This Summer School helps you to understand what these technologies can and cannot do, how to interpret data meaningfully, and how to ask the right questions. No programming required.

Course leader

Ricardo da Silva Torres, WUR, Ali Youssef, University of Manitoba & Tomas Norton, KU Leuven

Target group

This Summer School is designed for domain experts working with animals or humans who want to use digital technologies more effectively, without becoming technical experts themselves. It is particularly relevant for:
- Healthcare professionals, veterinarians and other practitioners who want to use data and sensors to improve health or welfare outcomes, but do not feel comfortable working with raw datasets or technical models.
- Researchers and R&D professionals in academia and industry who see the potential of sensors and wearables in their work, but struggle to assess which technologies are suitable and which are not.
- Product developers, business developers and managers in AgTech or life sciences who work with data scientists or technology providers and want to better understand the technology in order to steer development or investment decisions.

Course aim

After completing the course you are able to:
- Make informed decisions on sensor technologies
- Understand key biological signals and how they can be measured using sensors (e.g. movement, physiology, location) - without the engineering jargon;
- Evaluate sensors based on practicalities such as wearability, battery life, data resolution and biological fit (e.g. is it comfortable to wear?);
- Understand computational methods for sensors;
- Understand the basic logic behind commonly used computational approaches, including machine learning and AI, without engaging with mathematics or programming;;
- Assess whether a computational method is appropriate for a given question or unnecessarily complex;
- Understand how tools such as large language models and chatbots can support tasks like data exploration, summarisation or interpretation in biological contexts;l;
- Work more confidently with data scientists and technology providers
- Ask targeted, critical questions about data, models and assumptions when collaborating with technical experts;
- Interpret results well enough to judge their biological relevance and limitations;
- Avoid common pitfalls and unrealistic expectations when applying digital technologies in animal or human monitoring;

Fee info