Jaime E. Duarte

675 total citations
23 papers, 429 citations indexed

About

Jaime E. Duarte is a scholar working on Biomedical Engineering, Rehabilitation and Cognitive Neuroscience. According to data from OpenAlex, Jaime E. Duarte has authored 23 papers receiving a total of 429 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Biomedical Engineering, 11 papers in Rehabilitation and 10 papers in Cognitive Neuroscience. Recurrent topics in Jaime E. Duarte's work include Stroke Rehabilitation and Recovery (11 papers), Muscle activation and electromyography studies (11 papers) and Motor Control and Adaptation (7 papers). Jaime E. Duarte is often cited by papers focused on Stroke Rehabilitation and Recovery (11 papers), Muscle activation and electromyography studies (11 papers) and Motor Control and Adaptation (7 papers). Jaime E. Duarte collaborates with scholars based in Switzerland, United States and Germany. Jaime E. Duarte's co-authors include Robert Riener, Kai Schmidt, Martin Grimmer, Peter Wolf, Chris Awai Easthope, David J. Reinkensmeyer, Florian L. Haufe, Alexandra Schättin, Eling D. de Bruin and Michele Xiloyannis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Neurophysiology and Experimental Brain Research.

In The Last Decade

Jaime E. Duarte

22 papers receiving 427 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jaime E. Duarte Switzerland 9 292 190 67 65 53 23 429
C.D. Bakker Netherlands 5 154 0.5× 127 0.7× 92 1.4× 45 0.7× 27 0.5× 6 332
Amy R. Wu Canada 13 327 1.1× 142 0.7× 39 0.6× 100 1.5× 68 1.3× 20 442
Dario Wyss Switzerland 8 200 0.7× 185 1.0× 62 0.9× 63 1.0× 42 0.8× 10 320
Andrej Olenšek Slovenia 13 265 0.9× 257 1.4× 125 1.9× 153 2.4× 38 0.7× 45 545
Joan Lobo-Prat Netherlands 13 555 1.9× 391 2.1× 116 1.7× 33 0.5× 75 1.4× 40 740
Camila Shirota Australia 11 195 0.7× 158 0.8× 54 0.8× 130 2.0× 31 0.6× 26 416
Giovanni Cannaviello Italy 7 253 0.9× 247 1.3× 87 1.3× 34 0.5× 39 0.7× 10 420
Jérémy Olivier Switzerland 7 757 2.6× 284 1.5× 144 2.1× 104 1.6× 63 1.2× 16 885
Fangshi Zhu United States 10 470 1.6× 303 1.6× 183 2.7× 62 1.0× 103 1.9× 18 602
Anna Pagel Switzerland 4 767 2.6× 254 1.3× 129 1.9× 108 1.7× 56 1.1× 7 878

Countries citing papers authored by Jaime E. Duarte

Since Specialization
Citations

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

Fields of papers citing papers by Jaime E. Duarte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jaime E. Duarte. 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 Jaime E. Duarte. The network helps show where Jaime E. Duarte may publish in the future.

Co-authorship network of co-authors of Jaime E. Duarte

This figure shows the co-authorship network connecting the top 25 collaborators of Jaime E. Duarte. A scholar is included among the top collaborators of Jaime E. Duarte 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 Jaime E. Duarte. Jaime E. Duarte 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.
Scheermesser, Mandy, et al.. (2024). Barriers and Facilitators to the Use of Wearable Robots as Assistive Devices: Qualitative Study With Older Adults and Physiotherapists. JMIR Rehabilitation and Assistive Technologies. 11. e52676–e52676. 3 indexed citations
2.
Xiloyannis, Michele, et al.. (2023). Human-in-the-Loop Personalization of a Bi-Articular Wearable Robot’s Assistance for Downhill Walking. IEEE Transactions on Medical Robotics and Bionics. 6(1). 328–339. 7 indexed citations
3.
Wolf, Peter, et al.. (2022). An algorithm to reduce human–robot interface compliance errors in posture estimation in wearable robots. SHILAP Revista de lepidopterología. 3. e30–e30. 3 indexed citations
4.
Sonar, Harshal, Werner Popp, Jaime E. Duarte, et al.. (2020). Age-Dependent Asymmetry of Wrist Position Sense Is Not Influenced by Stochastic Tactile Stimulation. Frontiers in Human Neuroscience. 14. 65–65. 2 indexed citations
5.
Haufe, Florian L., Kai Schmidt, Jaime E. Duarte, et al.. (2020). Activity-based training with the Myosuit: a safety and feasibility study across diverse gait disorders. Journal of NeuroEngineering and Rehabilitation. 17(1). 135–135. 31 indexed citations
6.
Haufe, Florian L., Peter Wolf, Jaime E. Duarte, Robert Riener, & Michele Xiloyannis. (2020). Increasing exercise intensity during outside walking training with a wearable robot. Repository for Publications and Research Data (ETH Zurich). 390–395. 8 indexed citations
7.
Grimmer, Martin, et al.. (2019). Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking. Frontiers in Neurorobotics. 13. 57–57. 51 indexed citations
8.
Duarte, Jaime E., Heike Vallery, Verena Klamroth-Marganska, et al.. (2019). Do we need complex rehabilitation robots for training complex tasks?. PubMed. 11. 1085–1090. 4 indexed citations
9.
Schättin, Alexandra, et al.. (2018). Trends in robot-assisted and virtual reality-assisted neuromuscular therapy: a systematic review of health-related multiplayer games. Journal of NeuroEngineering and Rehabilitation. 15(1). 107–107. 53 indexed citations
10.
Wolf, Peter, et al.. (2018). Robot-Supported Multiplayer Rehabilitation: Feasibility Study of Haptically Linked Patient-Spouse Training. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4679–4684. 7 indexed citations
11.
Stämpfli, Rolf, et al.. (2018). A Method for Quantifying Interaction Forces in Wearable Robots. DORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa)). 789–794. 6 indexed citations
12.
Schmidt, Kai, et al.. (2017). The Myosuit: Bi-articular Anti-gravity Exosuit That Reduces Hip Extensor Activity in Sitting Transfers. Frontiers in Neurorobotics. 11. 57–57. 147 indexed citations
13.
Sonar, Harshal, Olivier Lambercy, Bernard J. Martin, et al.. (2017). A novel pneumatic stimulator for the investigation of noise-enhanced proprioception. PubMed. 79. 25–30. 3 indexed citations
14.
Wolf, Peter, et al.. (2017). Making neurorehabilitation fun: Multiplayer training via damping forces balancing differences in skill levels. PubMed. 2017. 876–881. 18 indexed citations
15.
Marchal–Crespo, Laura, et al.. (2016). Evaluation of a mixed controller that amplifies spatial errors while reducing timing errors. PubMed. 2016. 5136–5139. 3 indexed citations
16.
17.
Zondervan, Daniel K., Jaime E. Duarte, Justin B. Rowe, & David J. Reinkensmeyer. (2014). Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement’s timing. Experimental Brain Research. 232(3). 1057–1070. 4 indexed citations
18.
Duarte, Jaime E.. (2014). Effects of Robotic Challenge Level on Motor Learning, Rehabilitation, and Motivation: The Real-World Challenge Point Framework. eScholarship (California Digital Library).
19.
Duarte, Jaime E., et al.. (2014). Robot-assisted motor training: Assistance decreases exploration during reinforcement learning. PubMed. 2014. 3516–20. 6 indexed citations
20.

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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