QLila Europe's First Live-In-Lab
The Quantitative Live-In-Lab (QLiLa), established by Prof. Faisal in Kulmbach focuses on deploying artificial intelligence (AI) methods to address health-related questions while seamlessly integrating them into everyday life. The interdisciplinary and international team includes researchers from engineering, computer science, nutrition science, and neuroscience fields. Their collective aim is to analyze human behaviors and develop new technologies that support a long, healthy, and independent life.
The concept of a 'Live-In-Lab,' also known as a 'Reallabor' in German, translates the scientific and technical laboratory concept into an apartment setting. Rather than conducting scientific experiments in abstract settings such as laboratories or hospitals, the vision is to examine and treat individuals using digital methods in their everyday lives.
Our Research Focus
- UNDERSTANDING BEHAVIOUR
We record or obtain data on behavior in real-world environments, to fully capture the variance and richness of natural human actions.
DATA-DRIVEN ANALYSIS
We take a data-driven approach to analyse the high-dimensional data we record, often across complex action spaces.
BEHAVIORAL INSIGHTS
We build data-driven models from these datasets, to gain insight into human behavior in both health and disease.
Impressions from Research at the Quantitive Live-In-Lab in Kulmbach - © Prof. Dr. Aldo Faisal
Technical Equipment
Depth Cameras
Smart Clothing Modules
Eye-Tracking Glasses
High-Performance Computers
For more information also see this UBT Press Announcement from January 2023: Smart clothing and artificial intelligence: A new technology for the diagnosis and monitoring of neurological diseases
Join Our Lab
If you want to join our research initiative or partner with us for a project, check out our current Master projects or write us at faisal_lab@uni-bayreuth.de.
Media Highlights
RTL Filming in Kulmbach Live-In-Lab
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B. Kadirvelu et al.: A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia. Nature Medicine (2023), DOI: https://dx.doi.org/10.1038/s41591-022-02159-6
V. Ricotti et al.: Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy. Nature Medicine (2023), DOI: https://dx.doi.org/10.1038/s41591-022-02045-1