Szczegóły publikacji
Opis bibliograficzny
Applying multimodal mixed reality system for classifying Parkinson’s disease: design and evaluation of the voice module / Joanna Stępień, Miłosz DUDEK, Marek WODZIŃSKI, Mateusz DANIOŁ, Magdalena Igras-Cybulska, Magdalena Wójcik-Pędziwiatr, Daria HEMMERLING // W: VRW 2025 [Dokument elektroniczny] : 2025 IEEE conference on Virtual Reality and 3D user interfaces workshops : 8–12 March 2025, Saint-Malo, France : proceedings. — Wersja do Windows. — Adobe Reader. — Piscataway : The Institute of Electrical and Electronics Engineers, cop. 2025. — Dod. ISBN: 979-8-3315-2563-7. — e-ISBN: 979-8-3315-1484-6. — S. 953–958. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 957–958, Abstr. — Publikacja dostępna online od: 2025-04-24
Autorzy (7)
- AGHStępień Joanna
- AGHDudek Miłosz
- AGHWodziński Marek
- AGHDanioł Mateusz
- Igras-Cybulska Magdalena
- Wójcik-Pędziwiatr Magdalena
- AGHHemmerling Daria
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159687 |
|---|---|
| Data dodania do BaDAP | 2025-05-20 |
| Tekst źródłowy | URL |
| DOI | 10.1109/VRW66409.2025.00194 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE Conference on Virtual Reality and 3D User Interfaces 2025 |
Abstract
Early detection and regular monitoring are crucial for the effective treatment and improvement of the quality of life in patients with Parkinson’s disease, a neurodegenerative disorder characterized by bradykinesia, tremors, muscle stiffness, balance issues, and speech impairments. Currently, diagnosis depends on frequent specialist visits, which can be burdensome for elderly patients. This work presents a prototype of a non-invasive tool for accurate Parkinson’s disease detection through voice analysis, utilizing a mixed reality head-mounted display. MR goggles (Microsoft HoloLens 2) offer a stress-free, in-home environment, minimizing the need for frequent hospital visits. Patients perform 5 voice-related tasks, and advanced speech recognition models (wav2vec2, wavLM, HuBERT) and text models (BERT) are employed for analysis. Data collection and system evaluation were conducted using the MR system, with 57 participants contributing to the dataset (including 21 patients diagnosed with Parkinson’s disease and 36 healthy controls). This technology holds great promise as an advanced diagnostic tool for neurodegenerative diseases, facilitating interactive assessments, reducing the strain on healthcare providers, and enhancing patient comfort by limiting the necessity of regular check-up visits.
