Demographic change, the shortage of skilled professionals, and the growing need for individualized support in healthcare, rehabilitation, and the workplace pose major challenges for societies worldwide. At the same time, the increasing use of intelligent and autonomous technologies calls for new approaches to safe, intuitive, and human-centered collaboration between humans and machines. Advances in artificial intelligence, neurotechnology, and robotics offer new opportunities to address these challenges. Against this backdrop, our research focuses on novel approaches to human-technology interaction that enable personalized, safe, and intuitive support for individuals across a wide range of applications.

Our Vision:  From Human Adaptation to Technology Adaptation

Our long-term vision is a future in which intelligent technologies continuously adapt to the individual rather than requiring individuals to adapt to technology. By combining advances in artificial intelligence, neurotechnology, and robotics, we aim to create systems that understand human intentions, capabilities, and needs, enabling truly personalized assistance, rehabilitation, and augmentation across healthcare, industry, and space applications.

Research

At the core of our work is the question of how technical systems can perceive and interpret human states, abilities, needs, and intentions in order to adapt dynamically to the individual. 

We combine methods from artificial intelligence, neurotechnology, robotics, and signal processing to develop multimodal human-machine interfaces and adaptive assistive systems. By analyzing neurophysiological and physiological signals such as brain activity, muscle activity, eye movements, body motion, speech, and facial expressions, we seek to model cognitive and physical states, including intention, workload, attention, emotion, and movement planning.

A particular focus lies on intelligent wearable robots and robotic assistance systems for rehabilitation and motor support. These systems are designed to anticipate human intentions, adapt to individual capabilities, and enable seamless collaboration between humans and machines. To achieve this, we investigate advanced machine learning methods, multimodal sensor fusion, and shared-autonomy control architectures that allow humans and intelligent systems to act as collaborative partners.

Our research bridges fundamental science and real-world applications. Alongside the development of novel AI methodologies, we study neuroscientific questions related to motor control, neuroplasticity, movement planning, cognitive workload, and human adaptation. The resulting technologies are evaluated in realistic and translational settings, particularly in neurorehabilitation, human-robot interaction, assistive robotics, and, increasingly, in challenging operational environments such as space exploration.

By integrating Human-Centered AI, Neurotechnology, and Intelligent Robotics, we aim to establish the scientific foundations for adaptive systems that enhance human capabilities while remaining trustworthy, safe, and user-centered.

Research Areas

Neurotechnology and Biosignal Processing

• Processing and analysis of neurophysiological and psychophysiological data
• Electroencephalography (EEG)
• Electromyography (EMG)
• Eye tracking and gaze analysis
• Motion and activity sensing
• Facial expression, speech, and behavioral analysis
• Multimodal sensor fusion and data integration

Artificial Intelligence for Human-Technology Interaction

• Machine learning and deep learning
• Adaptive and personalized AI systems
• Human-in-the-loop learning
• Intention, attention, and cognitive workload recognition
• Trustworthy and explainable AI
• Continual and lifelong learning in assistive systems

Human-Machine and Human-Robot Interfaces

• Brain-computer interfaces (BCIs)
• Multimodal interaction systems
• Speech-based assistance technologies
• Affective computing and emotion recognition
• Tactile and haptic interfaces
• Intuitive interaction and control concepts

Wearable Robotics and Assistive Technologies

• Exoskeletons for rehabilitation and assistance
• Intention-aware robotic systems
• Shared autonomy and cooperative control
• Safe human-robot interaction
• Adaptive control architectures
• Personalized neurorehabilitation technologies

Embedded Intelligent Systems

• Edge AI and onboard learning
• Real-time signal processing
• Distributed intelligent sensing systems
• FPGA-based computing architectures
• Embedded AI and microcontroller systems
• Energy-efficient AI for mobile assistive technologies

Application Domains

• Neurorehabilitation
• Medical robotics and assistive technologies
• Human-robot collaboration
• Ambient assisted living
• Preventive healthcare and health monitoring
• Industrial and workplace assistance
• Space applications and extreme environments