Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors

Cited by (126)

Items 101–126 of 126

1. Digital Devices for Assessing Motor Functions in Mobility Impaired and Healthy Populations: A Systematic Literature Review (Preprint)
   Christine Cong Guo, Patrizia Andrea Chiesa, Carl de Moor, Mir Sohail Fazeli, Thomas Schofield, Kimberly Hofer, Shibeshih Mitiku Belachew, Alf Scotland · 2022
2. Identification of kinematic biomarkers for self-reported Parkinson's disease symptoms
   Ayala Matzner, Yuval El-Hanani, Izhar Bar‐Gad · 2022 · Research Square
3. Parkinson's disease detection using sea lion shuffled shepherd optimization assisted deep maxout network
   Mohana Ramaiyar Sundaram, Iwin Thanakumar Joseph S, Velliangiri Sarveshwaran · 2022 · Concurrency and Computation Practice and Experience
4. Predição de Congelamento de Marcha por Meio da Detecção de Eventos de Pré-Congelamento
   João Pedro A. Amoedo, Paulo H. N. Gonçalves, Rafael Albuquerque, Eulanda Miranda Dos Santos, Rafael Giusti, Carlos Renato · 2023
5. Real Time Detection of Freezing of Gait of Parkinson Patients based on Machine Learning Running on a Body Worn Device
   Ali Haddadi Esfahani, Oliver Maye, Max Frohberg, Maria Speh, M. Jöbges, Peter Langendörfer · 2024 · Procedia Computer Science
6. Vision-Based Abnormal Action Dataset for Recognising Body Motion Disorders
   Jiaying Ying, Xin Shen, Xin Yu · 2024 · Lecture notes in computer science
7. Multimodal Sensor Fusion Deep Learning Model for Early Prediction of Freezing of Gait in Parkinson's Disease
   Rohit Gupta, Amit Bhongade, Tapan Kumar Gandhi · 2026 · Computational Intelligence
8. A Parkinson’s Disease Gait Identification Model Based on Attention Mechanism and Long Short-Term Memory Network
   Jianxian Zhang, Xianghan Zheng, Huan Wang, Jing Cai, Ying Liu · 2025 · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
9. A feature fusion approach for detection and counting of freezing-of-gait episodes in Parkinson’s patients using shank acceleration sensor
   Preeti Khera, Neelesh Kumar, Ashima Khosla, Garima Maithani · 2025 · Multimedia Tools and Applications
10. Prediction of Freezing of Gait in Parkinson’s Disease Patients Using Machine Learning
    Sruthy Ajith · 2025 · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
11. Freezing of gait detection in Parkinsons Disease using relative power from wearable sensors: a Random Forest approach
    João Paulo Ferreira Soares · 2025
12. Transfer Learning Approach for Human Activity Recognition using EfficientNet
    R. R. Rajalaxmi, R. Thamilselvan, P P Sudharsana, S Ponharishkumar, S Ragavi, Sanjeev Rathan R · 2025 · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON)
13. Application of 3D Convolutional Model Based on Artificial Intelligence in Parkinson’s Assessment
    Jiaqi Guo, Qing Li, Shanshan Cao · 2025 · International Journal of High Speed Electronics and Systems
14. A WaveNet Deep Learning Framework for Real-Time FoG Prediction in Patients with Parkinson’s Disease
    Mosleh Hmoud Al-Adhaileh, Arshad Iqbal, Theyazn H. H. Aldhyani, Muhammad Irfan Uddin, Abdullah Hamoud Abdulrahman Al-Nefaie · 2025 · Journal of Disability Research
15. A Hybrid Algorithm to Predict Parkinson’s Disease Using Freezing of Gait
    G. Gopichand, Guggulla Jaya Ganesh Reddy, Metta Manikanta Karthikeya Reddy, Batchu Akash, Shaik Fardeen · 2025 · Lecture notes in electrical engineering
16. Caracterización de Relojes Inteligentes para el Análisis del Movimiento Humano
    Ignacio Pavón, Carlos Polvorinos-Fernández, María Centeno-Cerrato, Guillermo de Arcas, Luis Sigcha · 2025 · Anales de Ingeniería Mecánica
17. Additional file 1 of Assessing inertial measurement unit locations for freezing of gait detection and patient preference
    Johanna J. O’Day, Marissa R. Lee, Kirsten A. Seagers, Shannon L. Hoffman, Ava Jih-Schiff, Łukasz Kidziński, Scott L. Delp, Helen M Bronte-Stewart · 2022 · Figshare
18. Freezing of Gait Recognition via Deterministic Learning and Electromyography
    Zhuo Fang, Zonghai Huang, Jingting Zhang, Rui Huang, Hong Cheng · 2026 · Lecture notes in electrical engineering
19. Detecção de congelamento da marcha na Doença de Parkinson usando potência relativa de sensores vestíveis: uma abordagem com Random Forest
    João Paulo Ferreira Soares · 2025 · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
20. The evolution of trends and technology in wearable sensors used to detect falls in people with neurodegenerative diseases: a systematic review
    Yuanzheng Chen, Tinghuai Huang, Zijie Lin, Quan Zhou · 2026 · Frontiers in Neurorobotics
21. A customised 1D-CNN for recognition of freezing of gait in Parkinson’s disease using multivariate decomposition techniques
    NANCY RAJENDRAN, Selvaraj Thomas George, Kalkeseetharaman Pennagaram Krushnakumar, Ravi T. Ashwala, Subathra Muthu Sinnasamy Pandian · 2026 · Open Computer Science
22. Skin Conductance Dynamics Across Freezing of Gait, Voluntary Stopping, and Walking in Parkinson's Disease: Effects of Medication State in Semi‐Free‐Living Environments
    Juan Daniel Delgado-Terán, Richard J. A. Van Wezel, Tjitske Heida · 2026 · European Journal of Neuroscience
23. Unraveling Freezing of Gait in Parkinson’s Disease: Datasets, Algorithms, and Open-Source Solutions
    Luigi Borzì, Luis Sigcha, Florenc Demrozi · 2025 · Springer eBooks
24. A Leakage-Audited, Real-Time Streaming Evaluation of Compact Causal Sequence Models for Freezing-of-Gait Detection
    Chao Zhang · 2026 · SSRN Electronic Journal
25. Optimizing Multi-Sensor Fusion for Activity Recognition in Parkinson's Disease Using Deep Learning Networks
    Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul · 2026 · International Conference on Big Data Analytics and Practices (IBDAP)
26. Wearable Sensors and AI in Parkinson's Disease
    Shivam Kumar, Mandeep Kumar Jangra, Akanksha Saxena · 2026 · Advances in computational intelligence and robotics book series