M. Castañeda

39 papers receiving 395 citations

Peers

M. Castañeda
Comparison fields: 5 of 113
  • Computer Vision and Pattern Recognition 131
  • Human-Computer Interaction 35
  • Rehabilitation 22
  • Cognitive Neuroscience 41
  • Aquatic Science 16
Replace Zhiqin Qian with:
Zhiqin Qian China
Suman Tewary India
Kazuo Nakazawa Japan
Farong Gao China
Abolfazl Mohebbi Canada
Guangyu Jia United Kingdom
Tao Lu China
Ajat Shatru Arora India
Hyun Seung Yang South Korea
Stefano Michieletto Italy
M. Castañeda relative to Zhiqin Qian China Zhiqin Qian's profile →
Citations per field
00.5×6.5×
Zhiqin Qian · 1×
Citations per year

Countries citing papers authored by M. Castañeda

Since Specialization
Citations

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

Fields of papers citing papers by M. Castañeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Castañeda. 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 M. Castañeda. The network helps show where M. Castañeda may publish in the future.

Co-authors

The 25 scholars most cited alongside M. Castañeda, 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 M. Castañeda Line = papers co-authored together M. Castañeda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200486
2 196839
3 202137
4 201826
5 201823
6 202021
7 201617
8 200414
9 201413
10 196913
11 201310
12 20239
13 20049
14 20219
15 20238
16 20108
17 20057
18 20127
19 20095
20 20175

About M. Castañeda

M. Castañeda is a scholar working on Surgery, Computer Vision and Pattern Recognition, Biomedical Engineering, Pharmacology and Cognitive Neuroscience, having authored 42 papers that have together received 411 indexed citations. Recurring topics across this work include Surgical Simulation and Training (7 papers), Musculoskeletal pain and rehabilitation (4 papers), 3D Shape Modeling and Analysis (4 papers), Robotic Path Planning Algorithms (3 papers), Stroke Rehabilitation and Recovery (3 papers), Augmented Reality Applications (3 papers), Optical Imaging and Spectroscopy Techniques (2 papers) and Digital Imaging for Blood Diseases (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (131 citations), Human-Computer Interaction (35 citations), Rehabilitation (22 citations), Cognitive Neuroscience (41 citations) and Aquatic Science (16 citations). M. Castañeda has collaborated with scholars based in Mexico, Italy and United States. Frequent co-authors include Fernando Arámbula Cosı́o, Albert Tyler, Antonio Frisoli, Massimo Bergamasco, Alejandro Valencia-Arías, Edoardo Sotgiu, Mamoru Mitsuishi, Sandra Solano, Kanako Harada and Patricia Tato. Their work appears in journals such as Sensors, Gels, Simulation in Healthcare The Journal of the Society for Simulation in Healthcare, Journal of Fluorescence and Nutrients.

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.

Explore authors with similar magnitude of impact