Within the Take-off 2022 fall edition, 47 projects have been granted financing. Three of them are for research projects at UT. These include research projects on a portable monitoring system for muscle strength and movement profiles, photonic chips that work with UV light and a maintenance planning tool for industrial rollers.
Prof Dr M. Sartori, Dr H. Wang, Dr M.I. Refai
WearM.AI (wearable musculoskeletal and artificial intelligence) is a wearable system that generates a digital biomechanical twin of the user by fusing advanced neuromusculoskeletal models with machine learning. Our radical solution helps develop the next generation of fitness enthusiasts and athletes by helping them monitor advanced biomechanical parameters such as muscle strength and movement profiles. The solution offers a wearable alternative to traditional sensors that measure muscle strength and offers in‐depth biomechanical measures when compared to currently available fitness trackers. Although our current focus is on the sports industry, the product has applications in movement rehabilitation monitoring.
AluviaPhotonics: enabling UV photonic integrated circuits
Prof Dr S.M. Garcia Blanco
AluviaPhotonics B.V., an upcoming spin-off from the Integrated Optical Systems (IOS) group of the University of Twente, will offer foundry services for photonic integrated circuits (PICs) operating down to the ultraviolet wavelength range (~200 nm). Thanks to this NWO Take-off grant, a design manual will be available to allow for the design of PICs in the Al2O3 integrated platform, benefiting application fields including quantum computing, UV spectroscopy and metrology.
Maintenance Planning Tool for Industrial Rollers
Dr R. Loendersloot, Prof Dr T. Tinga, A. Alemi PhD
Software tools for maintenance planning are rare, despite the extensive research on predictive maintenance. Either over-conservative maintenance schedules or a high risk of system failure result from this. The key issues are that operational conditions can be demanding during production and direct measurement of relevant wear parameters unfeasible; only process data is available. The methodology developed in the NWO Smart Industry project Supreme is based on a partly physics-based and partly data-driven approach to predict the wear of industrial journal bearings. The feasibility to commercialise this concept in a software application will be explored in this project.
Because of these Take-off grants, academic and innovative starters can study the feasibility and commercial application of their research-based ideas. They can also explore the possibility of starting a business based on knowledge innovations from knowledge institutions.