MSCA DN AERIALIST – Doctoral Network on Assistive Health Technology in Unsupervised/Home Settings

WP1: Ensuring safe and reliable Assistive Health Technology

WP2: Sensorimotor AI for assistive health technology

WP3: Symbiotic human Interaction for Assistive Health Technology

WP4: Application Case Studies to bridge the knowledge gap

DC1: Application of system thinking and system safety to head-foot steering system for your powered wheelchair

DC2: Ensuring Runtime Safety: A Digital Twin Approach for ML-Based Controllers

DC3: A EMI Risk management of Assistive Health Technology

DC4: Probabilistic, data-driven sensor fusion for reliable human-robot sensing under real-life conditions

DC5: Optimising Real-Time Multimodal Data Collection for Assistive Technology

DC6: Biomimetic model-based reinforcement learning of motor primitives

DC7: Fusion of Electrical Stimulation with the Wearable Robot

DC8: Sensing the intention to move in people with neurological movement disorders

DC9: Situation-aware controller selection for robot-assisted overground gait training

DC10: Co-Adaptive Data-Driven Modeling for Precision Motion Control in Powered Exo-aid

DC11: Experience-Driven Co-Adaptation through Multi-Agent Learning in Assistive Devices

DC12: Wearable sensors for health monitoring and assistive communication

DC13: In-Game Assessment for Patient-adaptive Intelligent Rehabilitation Robotics

DC14: Advancing Prosthetic Limb Control: Bridging the Gap Between Adaptability and Safety

DC15: User-centred model predictive control approach for robotic assistance