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.
Heatables investigates the use of near-infrared (NIR) and infrared (IR) optical stimulation within the human ear to modulate thermal perception and comfort. The project explores the auditory canal as a novel physiological and perceptual interface for personalized thermal regulation.
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.
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.
The WHAR Datasets library standardizes formats and preprocessing in Wearable Human Activity Recognition (WHAR) research. It offers a unified, open-source framework with configuration-driven workflows for easier dataset handling and model training. Supporting nine major datasets, library promotes reproducibility, comparability, and efficiency in WHAR research.
HARNode is an open-source, time-synchronised wearable system that enables scalable, multi-device human activity recognition (HAR) in real-world environments. Each node combines IMU and pressure sensors in a compact, Wi-Fi-connected module for high-precision motion data collection.
UltrasonicSpheres creates localized ultrasonic audio zones in space, audible only when wearing OpenEarable 2.0. As users move between zones, they automatically hear the corresponding audio — with spatial direction preserved and no sound leaking into the environment. The system enables natural, hands-free, location-based audio experiences using off-the-shelf speakers and open-source earables.
An open-source hardware extension of the OpenEarable 1.3 platform that enables multifunctional biopotential sensing (EEG, EMG, EOG) in the ear, paving the way for new applications in HCI and wearable health.
MicroNAS is a hardware-aware neural architecture search (HW-NAS) framework designed for time series classification on microcontrollers (MCUs). It automatically generates efficient neural networks that meet strict memory and latency constraints, enabling real-time machine learning directly on low-power embedded devices.
HammerHAI is a European initiative led by HLRS and partners that provides secure, scalable AI and HPC resources for industry and research. As part of the EuroHPC “AI Factories,” it makes compliant AI technologies accessible and supports innovation through consulting, training, and ready-to-use tools.
The OpenWearables project is an initiative to advance wearable computing by following open-hardware and open-software principles. It aims to create a research infrastructure and community that makes wearable devices more accessible, extensible, interoperable and impactful.
In the FFLPlus project we aim to us a multimodal generative AI framework for metadata extraction from 2D CAD drawings. It leverages large vision–language models to interpret complex engineering layouts and automatically retrieve structured information, enabling scalable, secure, and efficient data management across manufacturing and construction workflows.
A nationwide research platform providing cutting-edge Big Data infrastructure to foster collaboration between industry and academia, enabling innovative projects in Industry 4.0, smart cities, energy, and medicine.
A premier doctoral school training the next generation of researchers to apply data science and AI to pressing health challenges, from advanced diagnostics to personalized therapies, in collaboration with leading German institutions.
An interdisciplinary graduate school focused on designing adaptive IT systems that improve economic decision-making. The research explores the intersection of human behavior, technology, and institutions.
