Cited by

Gait in Parkinson's Disease (v1.0.0)

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8 publications cite any version of this project, newest first.

• Multimodal Machine Learning Approach for Timed-Up-and-Go Test Score Prediction from Wearable Gait Data
  Nabid Faiem, Tunç Aşuroğlu, Koray Açıcı et al. Communications in computer and information science, 2026.
• Explainable AI Highlights the Most Relevant Gait Features for Neurodegenerative Disease Classification
  Gianmarco Tiddia, Francesca Mainas, Alessandra Retico et al. Applied Sciences, 2025.
• A review of machine learning and deep learning for Parkinson’s disease detection
  Hajar Rabie, Moulay A. Akhloufi. Discover Artificial Intelligence, 2025.
• Evaluating Motor Symptoms in Parkinson’s Disease Through Wearable Sensors: A Systematic Review of Digital Biomarkers
  Carlos Polvorinos-Fernández, Luis Sigcha, Luigi Borzì et al. Applied Sciences, 2024.
• Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach
  Nabid Faiem, Tunç Aşuroğlu, Koray Açıcı et al. Communications in computer and information science, 2024.
• Techniques for the detection and management of freezing of gait in Parkinson's disease – A systematic review and future perspectives
  Sunil Kumar Bansal, Bijit Basumatary, Rajinder Bansal et al. MethodsX, 2023.
• Classification Using Radial Basis Function for Prediction of Parkinson's Disease
  Md. Mosharraf Umar, Sameena Naaz. IEEE Global Conference for Advancement in Technology (GCAT), 2022.
• Usage of VGRF in Biometrics: Application on Healthy and Parkinson Gaits
  Rami Alkhatib, Mohamad O. Diab, Omar Itani et al. International Conference on Computer Applications & Information Security (ICCAIS), 2018.

Citation data from OpenAlex, updated weekly. Papers that use the project without citing it may be missing.