Chun‐Ta Chen

29 papers receiving 290 citations

Peers

Chun‐Ta Chen
Comparison fields: 5 of 88
  • Rehabilitation 37
  • Genetics 30
  • Control and Systems Engineering 58
  • Developmental Neuroscience 10
  • Computer Vision and Pattern Recognition 41
Replace Young-Dae Hong with:
Young-Dae Hong South Korea
Silvio Traversaro Italy
João P. Ferreira Portugal
Soha Pouya Switzerland
Nahla Khraief Tunisia
Emmanouil Spyrakos-Papastavridis United Kingdom
Sylvain Bertrand United States
Houman Dallali Italy
Ran Jiao China
Bernd Henze Germany
Chun‐Ta Chen relative to Young-Dae Hong South Korea Young-Dae Hong's profile →
Citations per field
00.5×1.5×2.1×
Young-Dae Hong · 1×
Citations per year

Countries citing papers authored by Chun‐Ta Chen

Since Specialization
Citations

This map shows the geographic impact of Chun‐Ta Chen'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 Chun‐Ta Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chun‐Ta Chen more than expected).

Fields of papers citing papers by Chun‐Ta Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chun‐Ta Chen. 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 Chun‐Ta Chen. The network helps show where Chun‐Ta Chen may publish in the future.

Co-authors

The 25 scholars most cited alongside Chun‐Ta Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chun‐Ta Chen Line = papers co-authored together Chun‐Ta Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202039
2 201039
3 200935
4 200834
5 200724
6 200916
7 202314
8 200513
9 200213
10 201411
11
Positive blood cultures in pediatric emergency department patients: epidemiological and clinical characteristics.
20068
12
Optimal Path Programming of the SPM Using the Boltzmann-Hamel-d’Alembert Dynamics Formulation Model
20087
13 20117
14 20085
15 20145
16 20225
17 20164
18 20113
19 20083
20 20223

About Chun‐Ta Chen

Chun‐Ta Chen is a scholar working on Biomedical Engineering, Control and Systems Engineering, Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 30 papers that have together received 304 indexed citations. Recurring topics across this work include Prosthetics and Rehabilitation Robotics (6 papers), Muscle activation and electromyography studies (4 papers), Streptococcal Infections and Treatments (3 papers), Robotic Mechanisms and Dynamics (2 papers), Music Technology and Sound Studies (2 papers), Robotic Path Planning Algorithms (2 papers), Music and Audio Processing (2 papers) and Neonatal and Maternal Infections (2 papers). The work is most often cited by research in Rehabilitation (37 citations), Genetics (30 citations), Control and Systems Engineering (58 citations), Developmental Neuroscience (10 citations) and Computer Vision and Pattern Recognition (41 citations). Chun‐Ta Chen has collaborated with scholars based in Taiwan and United States. Frequent co-authors include Yucheng Wu, Lin‐Yu Wang, Jhi‐Joung Wang, Wen‐Tzeng Huang, Sheng-Hsien Chen, Hsuan‐Rong Huang, Chin‐Hsing Chen, Roy E. Ritzmann, Zhihong Huang and Roger D. Quinn. Their work appears in journals such as Sensors, Journal of CO2 Utilization, Advanced Robotics, Industrial Robot the international journal of robotics research and application and Mobile Networks and Applications.

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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