Syn Schmitt

1.6k total citations
85 papers, 1.1k citations indexed

About

Syn Schmitt is a scholar working on Biomedical Engineering, Cognitive Neuroscience and Orthopedics and Sports Medicine. According to data from OpenAlex, Syn Schmitt has authored 85 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Biomedical Engineering, 14 papers in Cognitive Neuroscience and 14 papers in Orthopedics and Sports Medicine. Recurrent topics in Syn Schmitt's work include Muscle activation and electromyography studies (50 papers), Prosthetics and Rehabilitation Robotics (23 papers) and Motor Control and Adaptation (14 papers). Syn Schmitt is often cited by papers focused on Muscle activation and electromyography studies (50 papers), Prosthetics and Rehabilitation Robotics (23 papers) and Motor Control and Adaptation (14 papers). Syn Schmitt collaborates with scholars based in Germany, United States and Australia. Syn Schmitt's co-authors include Michael Günther, Daniel F. B. Haeufle, Oliver Röhrle, Hartmut Geyer, Veit Wank, Michael D. Taylor, Michael Sprenger, Reinhard Blickhan, Nils Karajan and Wolfgang Ehlers and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Syn Schmitt

76 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Syn Schmitt Germany 19 850 249 172 141 125 85 1.1k
Matthew Millard Germany 14 1.3k 1.5× 314 1.3× 329 1.9× 42 0.3× 118 0.9× 38 1.9k
Michael Sherman United States 12 904 1.1× 229 0.9× 216 1.3× 48 0.3× 82 0.7× 22 1.4k
Antonio Fratini Italy 19 496 0.6× 294 1.2× 190 1.1× 247 1.8× 63 0.5× 52 1.1k
Daniel F. B. Haeufle Germany 17 642 0.8× 198 0.8× 77 0.4× 68 0.5× 31 0.2× 49 801
Rositsa Raikova Bulgaria 15 546 0.6× 309 1.2× 126 0.7× 64 0.5× 48 0.4× 49 675
E. Pavan Italy 15 577 0.7× 145 0.6× 67 0.4× 89 0.6× 41 0.3× 37 928
Christopher L. Dembia United States 12 1.7k 2.0× 260 1.0× 426 2.5× 23 0.2× 96 0.8× 14 2.1k
Samuel R. Hamner United States 12 1.5k 1.8× 269 1.1× 654 3.8× 31 0.2× 107 0.9× 12 2.1k
Carmichael Ong United States 7 728 0.9× 140 0.6× 156 0.9× 16 0.1× 65 0.5× 12 995
Brian Garner United States 11 589 0.7× 239 1.0× 127 0.7× 33 0.2× 66 0.5× 34 893

Countries citing papers authored by Syn Schmitt

Since Specialization
Citations

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

Fields of papers citing papers by Syn Schmitt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Syn Schmitt

This figure shows the co-authorship network connecting the top 25 collaborators of Syn Schmitt. A scholar is included among the top collaborators of Syn Schmitt 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 Syn Schmitt. Syn Schmitt 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.
Geijtenbeek, Thomas, Vittorio Caggiano, Vikas Kumar, et al.. (2025). Emergence of natural and robust bipedal walking by learning from biologically plausible objectives. iScience. 28(4). 112203–112203. 1 indexed citations
2.
Shao, Yitian, Andrew Benson, Isabel Wurster, et al.. (2025). A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression. Device. 3(6). 100719–100719.
3.
Hu, Zhiming, et al.. (2024). Pose2Gaze: Eye-Body Coordination During Daily Activities for Gaze Prediction From Full-Body Poses. IEEE Transactions on Visualization and Computer Graphics. 31(9). 4655–4666. 3 indexed citations
4.
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Hammer, Maria, et al.. (2023). A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers. Computer Methods in Biomechanics & Biomedical Engineering. 28(4). 430–449. 1 indexed citations
7.
Schmitt, Syn, et al.. (2023). An investigation of tendon strains in jersey finger injury load cases using a finite element neuromuscular human body model. Frontiers in Bioengineering and Biotechnology. 11. 1293705–1293705. 2 indexed citations
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9.
Holm, Christian, et al.. (2021). The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric L -shaped particles. Royal Society Open Science. 8(9). 201839–201839. 1 indexed citations
10.
Haeufle, Daniel F. B., et al.. (2020). Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy. Frontiers in Robotics and AI. 7. 511265–511265. 3 indexed citations
11.
Schmitt, Syn, et al.. (2019). Development of a hybrid muscle controller for an active finite element human body model in LS-DYNA capable of occupant kinematics prediction in frontal and lateral maneuvers. 4 indexed citations
12.
Günther, Michael, Daniel F. B. Haeufle, & Syn Schmitt. (2018). The basic mechanical structure of the skeletal muscle machinery: One model for linking microscopic and macroscopic scales. Journal of Theoretical Biology. 456. 137–167. 16 indexed citations
13.
Schmitt, Syn, et al.. (2017). A movement generation algorithm for FE Human Body Models. PAMM. 17(1). 201–202. 1 indexed citations
14.
Haeufle, Daniel F. B., et al.. (2017). Weekly Time Course of Neuro-Muscular Adaptation to Intensive Strength Training. Frontiers in Physiology. 8. 329–329. 10 indexed citations
15.
Schmitt, Syn, et al.. (2017). The influence of biophysical muscle properties on simulating fast human arm movements. Computer Methods in Biomechanics & Biomedical Engineering. 20(8). 803–821. 31 indexed citations
16.
Günther, Michael, et al.. (2017). Strain in shock-loaded skeletal muscle and the time scale of muscular wobbling mass dynamics. Scientific Reports. 7(1). 13266–13266. 8 indexed citations
17.
Haeufle, Daniel F. B., et al.. (2016). Evaluating morphological computation in muscle and dc-motor driven models of hopping movements. eScholarship (California Digital Library). 14 indexed citations
18.
Lindemann, Ulrich, Michael Schwenk, Syn Schmitt, et al.. (2016). Effect of uphill and downhill walking on walking performance in geriatric patients using a wheeled walker. Zeitschrift für Gerontologie und Geriatrie. 50(6). 483–487. 11 indexed citations
19.
Röhrle, Oliver, Michael Sprenger, & Syn Schmitt. (2016). A two-muscle, continuum-mechanical forward simulation of the upper limb. Biomechanics and Modeling in Mechanobiology. 16(3). 743–762. 60 indexed citations
20.
Haeufle, Daniel F. B., Michael Günther, Reinhard Blickhan, & Syn Schmitt. (2012). Proof of Concept: Model Based Bionic Muscle with Hyperbolic Force-Velocity Relation. SHILAP Revista de lepidopterología. 7 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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