John Nassour

441 total citations
21 papers, 279 citations indexed

About

John Nassour is a scholar working on Biomedical Engineering, Control and Systems Engineering and Cognitive Neuroscience. According to data from OpenAlex, John Nassour has authored 21 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Biomedical Engineering, 6 papers in Control and Systems Engineering and 5 papers in Cognitive Neuroscience. Recurrent topics in John Nassour's work include Soft Robotics and Applications (7 papers), Prosthetics and Rehabilitation Robotics (6 papers) and Muscle activation and electromyography studies (6 papers). John Nassour is often cited by papers focused on Soft Robotics and Applications (7 papers), Prosthetics and Rehabilitation Robotics (6 papers) and Muscle activation and electromyography studies (6 papers). John Nassour collaborates with scholars based in Germany and France. John Nassour's co-authors include Gordon Cheng, Fred H. Hamker, Guoping Zhao, Martin Grimmer, Patrick Hénaff, Heinrich Lang, Vincent Hugel, Shoubhik Debnath, Stefan K. Ehrlich and Kathrin Koch and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Neural Networks and Learning Systems and Sensors and Actuators A Physical.

In The Last Decade

John Nassour

20 papers receiving 273 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Nassour Germany 8 217 77 59 34 32 21 279
Masashi Hamaya Japan 12 268 1.2× 207 2.7× 53 0.9× 73 2.1× 18 0.6× 43 432
Vijaykumar Rajasekaran United Kingdom 7 189 0.9× 81 1.1× 90 1.5× 79 2.3× 23 0.7× 12 299
Eric Kubica Canada 12 227 1.0× 86 1.1× 55 0.9× 9 0.3× 29 0.9× 22 394
Roberto Meattini Italy 9 267 1.2× 73 0.9× 124 2.1× 67 2.0× 47 1.5× 31 335
Omar A. Domínguez-Ramírez Mexico 6 52 0.2× 77 1.0× 69 1.2× 22 0.6× 47 1.5× 50 224
Alejandro Hernández Arieta Switzerland 9 202 0.9× 98 1.3× 181 3.1× 15 0.4× 38 1.2× 17 353
Chiharu Ishii Japan 11 244 1.1× 155 2.0× 78 1.3× 17 0.5× 47 1.5× 77 393
Woosung Yang South Korea 12 243 1.1× 185 2.4× 16 0.3× 34 1.0× 9 0.3× 51 427
Janis Wojtusch Germany 10 202 0.9× 105 1.4× 35 0.6× 36 1.1× 46 1.4× 24 295
João Ramos United States 11 262 1.2× 183 2.4× 52 0.9× 22 0.6× 52 1.6× 26 392

Countries citing papers authored by John Nassour

Since Specialization
Citations

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

Fields of papers citing papers by John Nassour

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Nassour

This figure shows the co-authorship network connecting the top 25 collaborators of John Nassour. A scholar is included among the top collaborators of John Nassour 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 John Nassour. John Nassour 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.
Peng, Yifei, et al.. (2025). Material-Driven Mechanical Programming of Soft Robotic Tentacles. mediaTUM (Technical University of Munich). 684–689. 1 indexed citations
2.
Nassour, John, et al.. (2024). MRI Compatible Valve Enables Fast Actuation of Soft Hand Exoskeleton in Medical Imaging. mediaTUM (Technical University of Munich). 1682–1687.
3.
Nassour, John, et al.. (2023). Multi-sensory fusion of wearable sensors for automatic grasping and releasing with soft-hand exoskeleton. mediaTUM (Technical University of Munich). 1–6. 2 indexed citations
4.
Nassour, John, et al.. (2023). Model predictive control of a soft elbow exosuit reduces interaction torque. mediaTUM (Technical University of Munich). 1–4. 1 indexed citations
5.
Nassour, John, et al.. (2023). Dynamic model of an online programmable textile soft actuator. mediaTUM (Technical University of Munich). 1–6. 1 indexed citations
6.
Nassour, John, et al.. (2022). Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials. Journal of NeuroEngineering and Rehabilitation. 19(1). 124–124. 7 indexed citations
7.
Nassour, John, et al.. (2021). Development of a wearable modular IMU sensor network suit with a distributed vibrotactile feedback for on-line movement guidance. mediaTUM (Technical University of Munich). 3 indexed citations
8.
Nassour, John, Guoping Zhao, & Martin Grimmer. (2021). Soft pneumatic elbow exoskeleton reduces the muscle activity, metabolic cost and fatigue during holding and carrying of loads. Scientific Reports. 11(1). 12556–12556. 60 indexed citations
9.
Ehrlich, Stefan K., et al.. (2021). Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. 6102–6105. 3 indexed citations
10.
Nassour, John, Fred H. Hamker, & Gordon Cheng. (2020). High-Performance Perpendicularly-Enfolded-Textile Actuators for Soft Wearable Robots: Design and Realization. IEEE Transactions on Medical Robotics and Bionics. 2(3). 309–319. 28 indexed citations
11.
Nassour, John, et al.. (2020). A Robust Data-Driven Soft Sensory Glove for Human Hand Motions Identification and Replication. IEEE Sensors Journal. 20(21). 12972–12979. 33 indexed citations
12.
Nassour, John & Fred H. Hamker. (2019). Enfolded Textile Actuator for Soft Wearable Robots. 60–65. 15 indexed citations
13.
Nassour, John. (2019). Marionette-based programming of a soft textile inflatable actuator. Sensors and Actuators A Physical. 291. 93–98. 10 indexed citations
14.
Nassour, John, et al.. (2019). Concrete Action Representation Model: From Neuroscience to Robotics. IEEE Transactions on Cognitive and Developmental Systems. 12(2). 272–284. 7 indexed citations
15.
Hamker, Fred H., et al.. (2018). Learning of Central Pattern Generator Coordination in Robot Drawing. Frontiers in Neurorobotics. 12. 44–44. 4 indexed citations
16.
Hamker, Fred H., et al.. (2018). A Humanoid Robot Learns to Recover Perturbation During Swinging Motion. IEEE Transactions on Systems Man and Cybernetics Systems. 50(10). 3701–3712. 15 indexed citations
17.
Nassour, John, et al.. (2014). Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots. Biological Cybernetics. 108(3). 291–303. 57 indexed citations
18.
Debnath, Shoubhik & John Nassour. (2014). Extending cortical-basal inspired reinforcement learning model with success-failure experience. 173. 293–298. 3 indexed citations
19.
Debnath, Shoubhik, John Nassour, & Gordon Cheng. (2014). Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot. 1016–1021. 5 indexed citations
20.
Nassour, John, et al.. (2012). Qualitative Adaptive Reward Learning With Success Failure Maps: Applied to Humanoid Robot Walking. IEEE Transactions on Neural Networks and Learning Systems. 24(1). 81–93. 23 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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