Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments
Albert Samà, Daniel Rodríguez-Martín, Carlos Pérez‐López, et al.
Pattern Recognition Letters (2017) Vol. 105, pp. 135-143
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
Julià Camps, Albert Samà, Mario Martín, et al.
Knowledge-Based Systems (2017) Vol. 139, pp. 119-131
Open Access | Times Cited: 195
Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review
Scott Pardoel, Jonathan Kofman, Julie Nantel, et al.
Sensors (2019) Vol. 19, Iss. 23, pp. 5141-5141
Open Access | Times Cited: 158
Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
Martina Mancini, Vrutangkumar V. Shah, Samuel Stuart, et al.
Journal of NeuroEngineering and Rehabilitation (2021) Vol. 18, Iss. 1
Open Access | Times Cited: 122
A recurrence plot-based approach for Parkinson’s disease identification
Luis C. S. Afonso, Gustavo Henrique de Rosa, Clayton R. Pereira, et al.
Future Generation Computer Systems (2018) Vol. 94, pp. 282-292
Closed Access | Times Cited: 110
Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning
Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, et al.
Sensors (2021) Vol. 21, Iss. 2, pp. 614-614
Open Access | Times Cited: 94
Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors
Luis Sigcha, Nélson Costa, Ignacio Pavón García, et al.
Sensors (2020) Vol. 20, Iss. 7, pp. 1895-1895
Open Access | Times Cited: 93
Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
Gaurav Shalin, Scott Pardoel, Edward D. Lemaire, et al.
Journal of NeuroEngineering and Rehabilitation (2021) Vol. 18, Iss. 1
Open Access | Times Cited: 66
A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application
Zeeshan Habib, Muhammad Ali Mughal, Muhammad Attique Khan, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 33, pp. 80179-80200
Closed Access | Times Cited: 13
Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life
Alessandro Zampogna, Luigi Borzì, Domiziana Rinaldi, et al.
Bioengineering (2024) Vol. 11, Iss. 5, pp. 440-440
Open Access | Times Cited: 8
A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects
Amit Salomon, Eran Gazit, Pieter Ginis, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 8
Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease
Florenc Demrozi, Ruggero Bacchin, Stefano Tamburin, et al.
IEEE Journal of Biomedical and Health Informatics (2019) Vol. 24, Iss. 9, pp. 2444-2451
Closed Access | Times Cited: 64
Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors
Bochen Li, Zhiming Yao, Jianguo Wang, et al.
Electronics (2020) Vol. 9, Iss. 11, pp. 1919-1919
Open Access | Times Cited: 58
Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor
Luigi Borzì, Luis Sigcha, Daniel Rodríguez-Martín, et al.
Artificial Intelligence in Medicine (2022) Vol. 135, pp. 102459-102459
Closed Access | Times Cited: 37
Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson’s disease: A systematic review
Tinghuai Huang, Meng Li, Jian‐Wei Huang
Frontiers in Aging Neuroscience (2023) Vol. 15
Open Access | Times Cited: 20
Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson’s disease
Núria Caballol, Àngels Bayés, Anna Prats, et al.
PLoS ONE (2023) Vol. 18, Iss. 2, pp. e0279910-e0279910
Open Access | Times Cited: 17
The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson’s disease
Peng Wu, Biwei Cao, Zhendong Liang, et al.
Frontiers in Aging Neuroscience (2023) Vol. 15
Open Access | Times Cited: 17
Flexible Gel-Free Multi-Modal Wireless Sensors With Edge Deep Learning for Detecting and Alerting Freezing of Gait Symptom
Yuhan Hou, Jack Ji, Yi Zhu, et al.
IEEE Transactions on Biomedical Circuits and Systems (2023) Vol. 17, Iss. 5, pp. 1010-1021
Closed Access | Times Cited: 16
Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions
Nizam Uddin Ahamed, Dylan Kobsar, Lauren C. Benson, et al.
PLoS ONE (2018) Vol. 13, Iss. 9, pp. e0203839-e0203839
Open Access | Times Cited: 55
Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test
Tal Reches, Moria Dagan, Talia Herman, et al.
Sensors (2020) Vol. 20, Iss. 16, pp. 4474-4474
Open Access | Times Cited: 49
Classification, Prediction, and Monitoring of Parkinson’s disease using Computer Assisted Technologies: A Comparative Analysis
Jinee Goyal, Padmavati Khandnor, Trilok Chand Aseri
Engineering Applications of Artificial Intelligence (2020) Vol. 96, pp. 103955-103955
Closed Access | Times Cited: 42
A new machine learning based approach to predict Freezing of Gait
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Pattern Recognition Letters (2020) Vol. 140, pp. 119-126
Open Access | Times Cited: 41
Detecting motor symptom fluctuations in Parkinson’s disease with generative adversarial networks
Vishwajith Ramesh, Erhan Bilal
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 23
Detection and prediction of freezing of gait with wearable sensors in Parkinson’s disease
Wei Zhang, Hong Sun, Debin Huang, et al.
Neurological Sciences (2023) Vol. 45, Iss. 2, pp. 431-453
Closed Access | Times Cited: 14
Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis
Hagar Elbatanouny, Natasa Kleanthous, Hayssam Dahrouj, et al.
Sensors (2024) Vol. 24, Iss. 12, pp. 3959-3959
Open Access | Times Cited: 5
Age-related gait patterns classification using deep learning based on time-series data from one accelerometer
Xiaoping Zheng, Elisabeth Wilhelm, Egbert Otten, et al.
Biomedical Signal Processing and Control (2025) Vol. 104, pp. 107406-107406
Open Access
Page 1 - Next Page

Scroll to top