A Multimodal Model-Fusion Approach for Improved Prediction of Freezing of Gait in Parkinson’s Disease
Author:
Affiliation:
1. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,
2. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10183770/10153508.pdf?arnumber=10153508
Reference34 articles.
1. Deep domain adaptation to predict freezing of gait in patients with Parkinson’s disease;torvi;Proc 17th IEEE Int Conf Mach Learn Appl (ICMLA),2018
2. Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson’s Disease Using Wearable Sensors
3. Effect of rhythmic auditory cueing on parkinsonian gait: A systematic review and meta-analysis
4. Utilizing Neural Networks to Predict Freezing of Gait in Parkinson's Patients
5. A new machine learning based approach to predict Freezing of Gait
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1. Knowledge mapping and research trends of accidental falls in patients with Parkinson’s disease from 2003 to 2023: a bibliometric analysis;Frontiers in Neurology;2024-08-22
2. Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models;2024 IEEE International Conference on Digital Health (ICDH);2024-07-07
3. Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems;2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE);2024-06-19
4. Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis;Sensors;2024-06-18
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