Marko Munih

4.5k total citations
183 papers, 3.3k citations indexed

About

Marko Munih is a scholar working on Biomedical Engineering, Rehabilitation and Cognitive Neuroscience. According to data from OpenAlex, Marko Munih has authored 183 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Biomedical Engineering, 54 papers in Rehabilitation and 52 papers in Cognitive Neuroscience. Recurrent topics in Marko Munih's work include Muscle activation and electromyography studies (81 papers), Stroke Rehabilitation and Recovery (54 papers) and Prosthetics and Rehabilitation Robotics (30 papers). Marko Munih is often cited by papers focused on Muscle activation and electromyography studies (81 papers), Stroke Rehabilitation and Recovery (54 papers) and Prosthetics and Rehabilitation Robotics (30 papers). Marko Munih collaborates with scholars based in Slovenia, Italy and United Kingdom. Marko Munih's co-authors include Matjaž Mihelj, Domen Novak, Janez Podobnik, A. Kralj, Roman Kamnik, Nicola Vitiello, Nick Donaldson, R.J. Jaeger, Tadej Bajd and Kenneth J. Hunt and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Expert Systems with Applications.

In The Last Decade

Marko Munih

172 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marko Munih Slovenia 31 1.8k 908 649 508 288 183 3.3k
Cristina P. Santos Portugal 25 1.5k 0.9× 365 0.4× 420 0.6× 411 0.8× 234 0.8× 247 2.5k
Thomas Schauer Germany 25 1.7k 0.9× 603 0.7× 505 0.8× 365 0.7× 146 0.5× 153 2.8k
Xuguang Wang France 23 1.4k 0.8× 466 0.5× 485 0.7× 314 0.6× 272 0.9× 154 5.4k
Vittorio Sanguineti Italy 29 1.2k 0.6× 730 0.8× 1.3k 2.0× 621 1.2× 372 1.3× 103 2.9k
Philippe S. Archambault Canada 34 1.2k 0.6× 1.4k 1.6× 1.1k 1.7× 228 0.4× 418 1.5× 168 3.9k
Eran Guendelman United States 9 2.5k 1.4× 298 0.3× 530 0.8× 395 0.8× 386 1.3× 11 4.1k
Heike Vallery Netherlands 28 2.7k 1.5× 1.1k 1.2× 469 0.7× 583 1.1× 342 1.2× 106 3.5k
Juan C. Moreno Spain 31 2.9k 1.6× 1.8k 1.9× 714 1.1× 445 0.9× 466 1.6× 154 4.0k
Yoshiyuki Sankai Japan 37 4.3k 2.4× 2.7k 2.9× 496 0.8× 501 1.0× 666 2.3× 306 5.9k
Olivier Lambercy Switzerland 33 2.5k 1.4× 2.2k 2.4× 1.0k 1.6× 210 0.4× 497 1.7× 165 4.3k

Countries citing papers authored by Marko Munih

Since Specialization
Citations

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

Fields of papers citing papers by Marko Munih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marko Munih

This figure shows the co-authorship network connecting the top 25 collaborators of Marko Munih. A scholar is included among the top collaborators of Marko Munih 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 Marko Munih. Marko Munih 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.
Zorman, Milan, et al.. (2025). From perception to precision: Vision-based mobile robotic manipulation for assembly screwdriving. Robotics and Computer-Integrated Manufacturing. 98. 103148–103148.
2.
Podobnik, Janez, et al.. (2024). Teaching approach for deep reinforcement learning of robotic strategies. Computer Applications in Engineering Education. 32(6).
3.
Munih, Marko, et al.. (2023). Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living. Sensors. 23(3). 1289–1289. 6 indexed citations
4.
Munih, Marko, et al.. (2023). Mobile Robot System for Selective Asparagus Harvesting. Agronomy. 13(7). 1766–1766. 6 indexed citations
5.
Munih, Marko, et al.. (2023). Modular Lidar System for Multiple Field-of-View Ranging. Sensors. 24(1). 84–84.
6.
Chen, Baojun, Marko Munih, Angelo Davalli, et al.. (2021). A Classification Approach Based on Directed Acyclic Graph to Predict Locomotion Activities With One Inertial Sensor on the Thigh. IEEE Transactions on Medical Robotics and Bionics. 3(2). 436–445. 10 indexed citations
7.
Podlešek, Anja, et al.. (2021). Effect of Speed, Speed Differences, and Motion Type on Perceived Safety of Collaborative Robots. 1036–1041. 6 indexed citations
8.
Kamnik, Roman, et al.. (2017). Compensation for Magnetic Disturbances in Motion Estimation to Provide Feedback to Wearable Robotic Systems. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25(12). 2398–2406. 15 indexed citations
9.
Garate, Virginia Ruiz, Andrea Parri, Tingfang Yan, et al.. (2016). Walking assistance using artificial primitives. IEEE Robotics & Automation Magazine. 3 indexed citations
10.
Kamnik, Roman, et al.. (2012). DIFFERENCES BETWEEN ELITE AND NOVICE ROWERS ON ERGOMETER. ISBS - Conference Proceedings Archive. 1(1). 1 indexed citations
11.
Mazzoleni, Stefano, Marko Munih, András József Tóth, et al.. (2012). Whole-body isometric force/torque measurements for functional assessment in neuro-rehabilitation: User interface and data pre-processing techniques. Computer Methods and Programs in Biomedicine. 110(1). 27–37.
12.
Novak, Dalma, et al.. (2011). Task difficulty adjustment in biocooperative rehabilitation using psychophysiological responses. PubMed. 17. 1–6. 2 indexed citations
13.
Koenig, Alexander, et al.. (2010). Comparison of visual and haptic feedback during training of lower extremities. Gait & Posture. 32(4). 540–546. 34 indexed citations
14.
Kamnik, Roman, et al.. (2008). Design and Evaluation of a Functional Electrical Stimulation System for Hand Sensorimotor Augmentation. Neuromodulation Technology at the Neural Interface. 11(3). 208–215. 4 indexed citations
15.
Musić, Josip, et al.. (2008). Human body model based inertial measurement of sit-to-stand motion kinematics. WSEAS TRANSACTIONS on SYSTEMS archive. 7(3). 156–164. 7 indexed citations
16.
17.
Mihelj, Matjaž, et al.. (2005). Assessment of stroke patients by whole-body isometric force-torque measurements II: software design of the ALLADIN Diagnostic Device. CINECA IRIS Institutial research information system (University of Pisa). 1 indexed citations
18.
Toth, Alison P., et al.. (2005). Assessment of recovery at stroke patients by whole-body isometric force-torque measurements of functional tasks I: mechanical design of the device. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 2 indexed citations
19.
Munih, Marko, et al.. (2004). UPPER LIMB FUNCTIONAL ASSESSMENT USING HAPTIC INTERFACE. SHILAP Revista de lepidopterología.
20.
Hunt, Kenneth J., Marko Munih, & Nick Donaldson. (1998). Investigation of a nonlinear strategy for moment control in electrically-stimulated muscle. UCL Discovery (University College London).

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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