About TECO
Established in 1993, TECO is a pioneering research group at KIT’s Institute of Telematics advancing ubiquitous and wearable computing through intelligent embedded systems, embedded AI, and novel human-computer interaction.
In close collaboration with industry, TECO drives innovation and fundamental research in real-world domains such as HealthTech and Industry 4.0, delivering open-source platforms including OpenEarable and EdgeML.
Founded at KIT as a pioneer in ubiquitous computing.
Research projects across the TECO portfolio.
People advancing ubiquitous computing.
Featured platform
The world's first fully open-source AI platform for ear-based sensing applications.
Learn moreResearch
OpenEarable is an open-source, AI-enabled platform for ear-based sensing applications with true wireless audio. The modular and reconfigurable platform is packed with a variety of high-precision sensors, designed for both development and research applications.
Finger-to-Ear ECG explores earbud-based cardiac monitoring by combining an in-ear electrode with finger contact, enabling high-quality ECG recordings and morphology preservation in a practical wearable form factor.
This project systematically reviews affective wearable haptic interventions, examining how touch-based wearables regulate emotions and well-being across haptic modalities, body locations, intervention mechanisms, and application contexts.
EarXplore is an evolving, interactive database that organizes and visualizes research on earables. It helps the community explore existing studies, uncover trends, and shape the future of earable technology.
EarStreAM is a closed-loop earable system that detects stress through in-ear sensing and automatically delivers personalized, LLM-generated meditation, adapting the intervention until the user’s physiological state returns toward baseline.
edge-ml is an embedded-first machine learning framework designed to help developers build models for microcontrollers faster and more robustly. As a browser-based, end-to-end solution, it simplifies the entire ML pipeline into a few simple steps: recording data, labeling samples, training models, and deploying validated embedded machine learning directly on the edge.
Transfer
Validaitor is a TECO research spin-off developing a comprehensive auditing and certification system for AI. Funded by the BMBF’s StartUpSecure KASTEL program, it ensures AI systems are trustworthy and compliant.
AskUI provides AI-powered automation by controlling operating systems rather than individual applications. By bringing vision to code, it enables developers to automate any use case across all platforms seamlessly.
Liftwise is a German startup providing an AI-powered fitness system for gyms. It uses a smart sensor armband and NFC technology to offer real-time technique feedback, automated tracking, and gamification.
Get involved
