DC5: Optimising Real-Time Multimodal Data Collection for Assistive Technology
Project: Optimising Real-Time Multimodal Data Collection for Assistive Technology (WP2)
Host institution: KU Leuven (Belgium)
Supervisor: Prof. H. Hallez (KU Leuven, Belgium)
Co-supervisor(s): Prof. G. Rauter (UNIBAS), Dr. H. Plácido da Silva (PLUX)
Objectives:
Research and gather field-based use cases for assistive health technology, emphasizing reliable sensors, logic, and com- munication devices to collect raw data. Explore the use of edge computing and distributed machine learning on embedded devices to improve cloud services.
Expected Results:
Development of guidelines for edge-based algorithms utilizing advanced data processing, including machine learning, to translate data into events and offer actionable insights for symbiotic assistive health technology or technical support teams.
Planned secondment(s):
- UNIBAS (3 months, M21-M23): Development of Edge sensing algorithms, with G. Rauter (KPI: joint conference paper)
- PLUX (3 months, M37-M39): Validation of Edge sensing algorithms on a case-study based on assistive communication, with H. Gamboa (KPI: joint journal paper)
Enrolment in Doctoral degree: Doctoral School of KU Leuven (Belgium)
Required profile: Electrical Engineering or Computer Science
Desirable skills/interests: Sensorbased algorithm – Edge Computing – TinyML – distributed machine learning