A toolkit towards VR rehabilitation. Enhancing upper limb kinematic analysis and visualization
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
IT116, Linnanmaa Campus
Topic of the dissertation
A toolkit towards VR rehabilitation. Enhancing upper limb kinematic analysis and visualization
Doctoral candidate
Master of Engineering Ummi Khaira Latif
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Applied Computing
Subject of study
Computer Science and Engineering
Opponent
Professor Monica Bordegoni, Politecnico di Milano
Custos
Professor Georgi V. Georgiev, Japan Advanced Institute of Science and Technology (JAIST)
Turning VR rehabilitation data into information therapists can use
Virtual reality (VR) rehabilitation produces a wealth of movement data that could help therapists follow patients' progress more objectively. This doctoral research shows that making use of this data takes more than accurate measurement: the results must also be presented in a way that makes sense to therapists.
VR has become a popular tool in arm rehabilitation because its game-like exercises keep patients motivated. To let patients interact in the virtual world, VR devices continuously record every movement they make. This movement data could also show therapists how well a patient moves and whether they are improving, yet it is rarely used for that purpose.
This doctoral research set out to make the data usable for therapists, so that it can help them assess patients' movement objectively. A review of earlier studies pointed to part of the problem: researchers measure arm movements in so many different ways that their results are hard to compare.
Building on this, the research developed tools that utilize off-the-shelf VR devices, so that clinics can measure movement without special motion capture systems. The tools recognize common arm movements, analyze wrist movement and calculate the results in a standardized way, making them comparable across studies and clinics.
Consistent measurement is only half the task: the results must also be understandable to therapists. The research therefore designed a visual way of presenting the results and asked rehabilitation therapists to evaluate it. They preferred familiar measures from their daily work, such as how far the arm can move, while more abstract measures, such as movement smoothness, were harder to interpret without clinical explanation.
This reveals a gap: the measures valued in research are not always the ones therapists find useful. The key message is therefore that better measurement alone is not enough. Closing this gap requires close collaboration with clinicians and tools built around real clinical needs. This research lays a practical foundation for turning movement data recorded during VR rehabilitation into information therapists can use.
VR has become a popular tool in arm rehabilitation because its game-like exercises keep patients motivated. To let patients interact in the virtual world, VR devices continuously record every movement they make. This movement data could also show therapists how well a patient moves and whether they are improving, yet it is rarely used for that purpose.
This doctoral research set out to make the data usable for therapists, so that it can help them assess patients' movement objectively. A review of earlier studies pointed to part of the problem: researchers measure arm movements in so many different ways that their results are hard to compare.
Building on this, the research developed tools that utilize off-the-shelf VR devices, so that clinics can measure movement without special motion capture systems. The tools recognize common arm movements, analyze wrist movement and calculate the results in a standardized way, making them comparable across studies and clinics.
Consistent measurement is only half the task: the results must also be understandable to therapists. The research therefore designed a visual way of presenting the results and asked rehabilitation therapists to evaluate it. They preferred familiar measures from their daily work, such as how far the arm can move, while more abstract measures, such as movement smoothness, were harder to interpret without clinical explanation.
This reveals a gap: the measures valued in research are not always the ones therapists find useful. The key message is therefore that better measurement alone is not enough. Closing this gap requires close collaboration with clinicians and tools built around real clinical needs. This research lays a practical foundation for turning movement data recorded during VR rehabilitation into information therapists can use.
Created 1.10.2026 | Updated 2.10.2026