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:

An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease
Luis R. Peraza, Kirsi M. Kinnunen, Róisín McNaney, et al.
Sensors (2021) Vol. 21, Iss. 24, pp. 8286-8286
Open Access | Times Cited: 19

Showing 19 citing articles:

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review
Luis Sigcha, Luigi Borzì, Federica Amato, et al.
Expert Systems with Applications (2023) Vol. 229, pp. 120541-120541
Open Access | Times Cited: 52
Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors
Christian Werner, Natalie Hezel, Fabienne Dongus, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 21
Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model
Xiaolong Wu, Lin Ma, Penghu Wei, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 5
Validity and Reliability of a Smartphone App for Gait and Balance Assessment
Usman Rashid, David Barbado, Sharon Olsen, et al.
Sensors (2021) Vol. 22, Iss. 1, pp. 124-124
Open Access | Times Cited: 35
Single-View, Video-Based Diagnosis of Parkinson’s Disease via Margin of Stability Gait Analysis
Jun-Seok Seo, Yiyu Chen, Do‐Young Kwon, et al.
Lecture notes in computer science (2025), pp. 354-368
Closed Access
Advanced sensor technology for smart patient monitoring in healthcare
Karthikeyan P. Iyengar, Rajesh Botchu, Sahana Giliyaru, et al.
Elsevier eBooks (2025), pp. 77-88
Closed Access
The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review
Alessandra Franco, Michela Russo, Marianna Amboni, et al.
Sensors (2024) Vol. 24, Iss. 18, pp. 5957-5957
Open Access | Times Cited: 3
An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson’s Disease
Min Chen, Zhanfang Sun, Xin Tao, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 3937-3946
Open Access | Times Cited: 7
Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review
Ahnjili Zhuparris, Annika A. de Goede, Iris E. Yocarini, et al.
Sensors (2023) Vol. 23, Iss. 11, pp. 5243-5243
Open Access | Times Cited: 6
Gait Monitoring and Analysis: A Mathematical Approach
Massimo Canonico, Francesco Desimoni, Alberto Ferrero, et al.
Sensors (2023) Vol. 23, Iss. 18, pp. 7743-7743
Open Access | Times Cited: 3
A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study
Paola Pierleoni, Sara Raggiunto, Alberto Belli, et al.
Applied Sciences (2022) Vol. 12, Iss. 11, pp. 5486-5486
Open Access | Times Cited: 4
Designing for Participatory Data Governance: Insights from People with Parkinson's
Pranav Kulkarni, Reuben Kirkham, Ling Wu, et al.
Designing Interactive Systems Conference (2024) Vol. 102, pp. 541-555
Closed Access
An interactive virtual reality system based on leap motion controller for hand motor rehabilitation
Amal Bouatrous, Abdelkrim Meziane, Nadia Zenati, et al.
(2024), pp. 1-5
Closed Access
Towards Sustainable Management of Parkinson’s Disease Healthcare: Evaluating the Role and Impact of Mobile Health Devices (Preprint)
Mevludin Memedi, Blerim Emruli, Yang Cao, et al.
(2024)
Closed Access
In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer
Miles Welbourn, Paul Sheriff, Pirinka Georgiev Tuttle, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
An Interpretable Deep Learning Optimized Wearable Daily Monitoring System for Parkinson's Disease Patients
Fei Su, Min Chen, Zhanfang Sun, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents
Amanrai Singh Kahlon, Khushboo Verma, Alexander Sage, et al.
Sensors (2023) Vol. 23, Iss. 19, pp. 8275-8275
Open Access
In-Clinic and Natural Gait Observations (I-CAN-GO): A Master Protocol to Validate Gait using a Lumbar Accelerometer
Miles Welbourn, Paul Sheriff, Pirinka G. Tuttle, et al.
Research Square (Research Square) (2023)
Open Access
A Tool for Home Monitoring in Parkinson's Disease
Marco Mercuri, Francesco Pio Cecca, Sara Mattioli, et al.
(2022), pp. 1-5
Closed Access
Page 1

Scroll to top