Kang Ren

672 total citations
31 papers, 388 citations indexed

About

Kang Ren is a scholar working on Neurology, Physical Therapy, Sports Therapy and Rehabilitation and Biomedical Engineering. According to data from OpenAlex, Kang Ren has authored 31 papers receiving a total of 388 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Neurology, 9 papers in Physical Therapy, Sports Therapy and Rehabilitation and 9 papers in Biomedical Engineering. Recurrent topics in Kang Ren's work include Parkinson's Disease Mechanisms and Treatments (20 papers), Neurological disorders and treatments (11 papers) and Balance, Gait, and Falls Prevention (9 papers). Kang Ren is often cited by papers focused on Parkinson's Disease Mechanisms and Treatments (20 papers), Neurological disorders and treatments (11 papers) and Balance, Gait, and Falls Prevention (9 papers). Kang Ren collaborates with scholars based in China, Japan and United Kingdom. Kang Ren's co-authors include Zhiwei Luo, Changqin Quan, Yun Ling, Chao Gao, Pei Huang, Jin Zhao, Shengdi Chen, Wenwu Chen, Yan Xu and Xuebing Cao and has published in prestigious journals such as IEEE Access, Sensors and The Journals of Gerontology Series A.

In The Last Decade

Kang Ren

26 papers receiving 376 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kang Ren China 11 185 162 62 62 57 31 388
Federica Amato Italy 8 84 0.5× 100 0.6× 36 0.6× 40 0.6× 45 0.8× 15 227
Luigi Borzì Italy 13 245 1.3× 105 0.6× 206 3.3× 24 0.4× 31 0.5× 27 469
Tomás Arias‐Vergara Germany 15 162 0.9× 358 2.2× 42 0.7× 194 3.1× 262 4.6× 55 681
Radim Krupička Czechia 11 164 0.9× 65 0.4× 149 2.4× 11 0.2× 21 0.4× 46 421
Panagiota Bougia Greece 6 252 1.4× 58 0.4× 193 3.1× 15 0.2× 24 0.4× 6 437
R. Prashanth India 8 290 1.6× 180 1.1× 31 0.5× 17 0.3× 50 0.9× 15 474
Nicholas Kostikis Greece 7 260 1.4× 112 0.7× 74 1.2× 10 0.2× 18 0.3× 12 369
Luis Sigcha Spain 9 188 1.0× 45 0.3× 175 2.8× 8 0.1× 16 0.3× 24 361
Dina Baga Greece 5 156 0.8× 38 0.2× 102 1.6× 10 0.2× 19 0.3× 6 287
Philipp Klumpp Germany 9 22 0.1× 90 0.6× 26 0.4× 83 1.3× 129 2.3× 24 280

Countries citing papers authored by Kang Ren

Since Specialization
Citations

This map shows the geographic impact of Kang Ren's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kang Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kang Ren more than expected).

Fields of papers citing papers by Kang Ren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kang Ren. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kang Ren. The network helps show where Kang Ren may publish in the future.

Co-authorship network of co-authors of Kang Ren

This figure shows the co-authorship network connecting the top 25 collaborators of Kang Ren. A scholar is included among the top collaborators of Kang Ren based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kang Ren. Kang Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Fei Hu, et al.. (2025). Shallow multi-view network for sensor-based recognition of Parkinson’s freezing of gait. Biomedical Signal Processing and Control. 112. 108473–108473.
2.
Quan, Changqin, et al.. (2025). FedOcw: optimized federated learning for cross-lingual speech-based Parkinson’s disease detection. npj Digital Medicine. 8(1). 357–357. 2 indexed citations
3.
Wang, Xuemei, Zhan Wang, Yaqin Yang, et al.. (2024). Machine‐learning model for the prediction of acute orthostatic hypotension after levodopa administration. CNS Neuroscience & Therapeutics. 30(3). e14575–e14575. 3 indexed citations
4.
Qin, He, Lingyu Wu, Wei Du, et al.. (2024). Instrumented timed up and go test and machine learning-based levodopa response evaluation: a pilot study. Journal of NeuroEngineering and Rehabilitation. 21(1). 163–163. 1 indexed citations
5.
Wang, Miao, Xingli Zhao, Fengzhu Li, et al.. (2024). Using sustained vowels to identify patients with mild Parkinson’s disease in a Chinese dataset. Frontiers in Aging Neuroscience. 16. 1377442–1377442. 3 indexed citations
6.
Hao, Shuai, et al.. (2024). Transmission Line Defect Target-Detection Method Based on GR-YOLOv8. Sensors. 24(21). 6838–6838. 7 indexed citations
7.
8.
Ling, Yun, et al.. (2024). Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes. npj Digital Medicine. 7(1). 169–169. 11 indexed citations
9.
Ma, Lingyan, et al.. (2023). A progression analysis of motor features in Parkinson's disease based on the mapper algorithm. Frontiers in Aging Neuroscience. 15. 1047017–1047017. 3 indexed citations
10.
Shen, Dingding, Yun Ling, Dianyou Li, et al.. (2023). Bilateral globus pallidus interna deep brain stimulation in Parkinson’s disease: Therapeutic effects and motor outcomes prediction in a short-term follow up. Frontiers in Human Neuroscience. 16. 1023917–1023917.
11.
Gao, Chao, et al.. (2023). Wearable sensor-based gait analysis to discriminate early Parkinson’s disease from essential tremor. Journal of Neurology. 270(4). 2283–2301. 37 indexed citations
12.
Cai, Guoen, Ying-Qing Wang, Huidan Weng, et al.. (2023). Specific Distribution of Digital Gait Biomarkers in Parkinson’s Disease Using Body-Worn Sensors and Machine Learning. The Journals of Gerontology Series A. 78(8). 1348–1354. 11 indexed citations
13.
Ma, Lingyan, Cheng Chen, Zhan Wang, et al.. (2023). Remote scoring models of rigidity and postural stability of Parkinson’s disease based on indirect motions and a low-cost RGB algorithm. Frontiers in Aging Neuroscience. 15. 1034376–1034376. 3 indexed citations
14.
Cui, Shishuang, Gen Li, Juanjuan Du, et al.. (2022). MAO-B Polymorphism Associated with Progression in a Chinese Parkinson’s Disease Cohort but Not in the PPMI Cohort. Parkinson s Disease. 2022(1). 3481102–3481102. 3 indexed citations
15.
Wang, Qiyue, et al.. (2022). Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset. Frontiers in Aging Neuroscience. 14. 1036588–1036588. 13 indexed citations
16.
Wang, Linbin, Lu Xu, Zhengyu Lin, et al.. (2022). Effects of Unilateral Stimulation in Parkinson's Disease: A Randomized Double-Blind Crossover Trial. Frontiers in Neurology. 12. 812455–812455. 4 indexed citations
17.
Li, Wendan, Jintao Zhang, Jianjun Lu, et al.. (2022). Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision. Frontiers in Aging Neuroscience. 14. 921081–921081. 11 indexed citations
18.
Li, Gen, Chao Gao, Yuyan Tan, et al.. (2021). Prediction of Freezing of Gait in Parkinson’s Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study. Frontiers in Human Neuroscience. 15. 636414–636414. 10 indexed citations
19.
Ma, Lingyan, Yu Tian, Yun Ling, et al.. (2021). Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers. Frontiers in Aging Neuroscience. 12. 627199–627199. 10 indexed citations
20.
Jiang, Zhenglong, et al.. (2007). Simulation model of lightning stroke to a transmission line considering the leader propagation randomicity. International Power Engineering Conference. 1282–1286. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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