In a clinical setting, accurate therapeutic data recording is crucial. A prior thesis explored virtual neurological exams using VR, but faced hand motion tracking challenges. A prototype VR glove produced by AiQ Synertial could address these issues, improving hand and finger motion capture. However, it needs testing for accuracy and reliability before being deemed applicable in a clinical environment.
C# / Unity
The project aims to evaluate the VR glove's capability to enhance hand tracking beyond UltraLeap's limitations, providing precise measurements for neurological examinations. Key objectives include testing connectivity, data accuracy, and responsiveness of the VR glove in a clinical setting to determine its reliability and applicability.
The project aims to address limitations in VR hand tracking for neurological exams. Previous systems like UltraLeap struggled with overlapping objects and fine motor motions. A prototype VR glove by AiQ Synertial, equipped with 16 sensors, is proposed to enhance tracking accuracy and reliability, needing thorough testing for clinical applicability.
The VR glove showed unreliable sensor data and frequent misalignments. Despite recalibration and environmental adjustments, accuracy issues persisted. Faulty IMUs were confirmed, with overlapping objects and plane detection problems further undermining the glove's reliability. Overall, the VR glove proved inadequate for precise hand tracking in neurological applications.
Project duration: 19.Feb – 14. Jun 2024
Samuel Schenk
MachineMD AG
Weyermannsstrasse 36
3008 Bern
Switzerland
Hanan Mufti
Arzu Çöltekin, arzu.coltekin@fhnw.ch
Cloe Hüsser, cloe.huesser@fhnw.ch
