Our TinyML Show&Tell sessions continue to highlight an encouraging trend: students around the world are not only learning about embedded AI, they are actively building real systems with it. A recent session featured projects from Colombia, where a growing community of students and researchers is exploring how TinyML and EdgeAI can support applications in healthcare, rehabilitation, and privacy-preserving sensing.

Many of these projects are connected to the work of Diego Méndez, a full professor at Pontificia Universidad Javeriana in Bogotá, whose students are developing embedded AI systems through research and experimentation. Their work reflects the impact of engaging with the TinyML/EdgeAI ecosystem and collaborating within its global community.

This momentum can be traced in part to the SciTinyML 2025 Workshop held in Bogotá last year, organized by the TinyMLedu initiative and partially supported by the EdgeAI Foundation, Edge Impulse and Seeed Studio. Workshops like this often act as catalysts, bringing together students, educators, and practitioners who continue collaborating and building long after the event ends.

Today, we highlight a few of the projects emerging from this growing TinyML/EdgeAI community in Colombia.

Postural Monitoring with TinyML and Federated Learning (Ángela Torres - Universidad del Rosario)

Ángela presented her work on the validation of an IoT system with federated learning and TinyML for postural monitoring in a simulated healthcare environment. The system combines embedded sensing with on-device machine learning to detect posture patterns while allowing models to improve collaboratively through federated learning.

By keeping sensitive health data on local devices and sharing only model updates, the approach enables privacy-preserving monitoring, an important requirement for many healthcare applications.

Embedded Movement Intention Detection for Post-Stroke Rehabilitation (Andrés Gómez - Pontificia Universidad Javeriana)

Andrés presented the results of his master’s thesis: an embedded movement intention detection system based on surface electromyography (sEMG) designed to support the smart rehabilitation of post-stroke patients.

The system processes muscle activity signals directly on an embedded device to infer a patient’s intended movement in real time. This capability could enable more responsive rehabilitation devices and assistive technologies that adapt to a patient’s intent. Andrés’ research has also resulted in peer-reviewed journal publications.

Albaricoque: A Privacy-Preserving Human Detection Node (Sergio Mesa - Pontificia Universidad Javeriana)

Sergio presented the preliminary results of his master’s thesis: Albaricoque, a TinyML-based human detection node designed with privacy in mind. By using ultrasonic and passive infrared (PIR) sensors and performing inference locally on the device, the system can detect human presence avoiding the usage of cameras that transmit sensitive visual data.

The project has already gained international recognition: Albaricoque was selected as one of the winning entries in the global Edge Impulse Hackathon in December 2025.

Together, these projects show how local initiatives can help grow a global ecosystem of TinyML and EdgeAI builders. The Bogotá workshop helped spark a community that is now producing research, prototypes, and student-led innovations.

This is exactly what we hope to see: students building real systems, sharing their work, and helping expand the global TinyML community. We welcome students to register to present at an upcoming Show&Tell session!

Written by Professor Diego Méndez
Pontificia Universidad Javeriana