Michael D. Naish

1.7k total citations
101 papers, 1.3k citations indexed

About

Michael D. Naish is a scholar working on Biomedical Engineering, Surgery and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michael D. Naish has authored 101 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Biomedical Engineering, 37 papers in Surgery and 33 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michael D. Naish's work include Soft Robotics and Applications (37 papers), Surgical Simulation and Training (31 papers) and Neurological disorders and treatments (22 papers). Michael D. Naish is often cited by papers focused on Soft Robotics and Applications (37 papers), Surgical Simulation and Training (31 papers) and Neurological disorders and treatments (22 papers). Michael D. Naish collaborates with scholars based in Canada, Italy and United States. Michael D. Naish's co-authors include Rajni V. Patel, Ana Luisa Trejos, Yue Zhou, Mary E. Jenkins, Richard Malthaner, Christopher M. Schlachta, Elizabeth A. Croft, B. Benhabib, Jagadeesan Jayender and Marie‐Eve LeBel and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Sensors and The International Journal of Robotics Research.

In The Last Decade

Michael D. Naish

96 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael D. Naish Canada 21 864 494 266 208 193 101 1.3k
Ana Luisa Trejos Canada 23 1.2k 1.4× 651 1.3× 207 0.8× 224 1.1× 239 1.2× 127 1.7k
Yo Kobayashi Japan 20 1.1k 1.3× 495 1.0× 171 0.6× 122 0.6× 195 1.0× 229 1.7k
Masakatsu G. Fujie Japan 22 1.7k 2.0× 571 1.2× 280 1.1× 171 0.8× 307 1.6× 345 2.4k
Hideyuki Hirata Japan 27 1.2k 1.4× 481 1.0× 180 0.7× 125 0.6× 384 2.0× 47 1.6k
Hidenori Ishihara Japan 23 1.1k 1.2× 394 0.8× 159 0.6× 106 0.5× 353 1.8× 60 1.4k
Jumpei Arata Japan 19 889 1.0× 349 0.7× 151 0.6× 84 0.4× 168 0.9× 78 1.3k
Kourosh Zareinia Canada 21 724 0.8× 401 0.8× 127 0.5× 57 0.3× 377 2.0× 69 1.2k
Antônio Padilha Lanari Bó Brazil 19 545 0.6× 123 0.2× 133 0.5× 142 0.7× 49 0.3× 71 950
S. Farokh Atashzar United States 26 1.0k 1.2× 216 0.4× 102 0.4× 176 0.8× 610 3.2× 143 1.9k
Eftychios G. Christoforou Cyprus 18 495 0.6× 245 0.5× 128 0.5× 132 0.6× 179 0.9× 57 1.1k

Countries citing papers authored by Michael D. Naish

Since Specialization
Citations

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

Fields of papers citing papers by Michael D. Naish

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael D. Naish. 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 Michael D. Naish. The network helps show where Michael D. Naish may publish in the future.

Co-authorship network of co-authors of Michael D. Naish

This figure shows the co-authorship network connecting the top 25 collaborators of Michael D. Naish. A scholar is included among the top collaborators of Michael D. Naish 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 Michael D. Naish. Michael D. Naish 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.
Zhou, Yue, Mary E. Jenkins, S. Jayne Garland, et al.. (2024). Variability of Parkinsonian Tremor During Different Tasks and Under External Interference. IEEE Sensors Journal. 24(22). 37492–37502.
2.
Zhou, Yue, Mary E. Jenkins, S. Jayne Garland, et al.. (2024). Tremor Suppression Using Functional Electrical Stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 3289–3298. 6 indexed citations
3.
Zhou, Yue, et al.. (2023). Multimodal Tremor Suppression of the Wrist Using FES and Electric Motors–A Simulation Study. IEEE Robotics and Automation Letters. 8(11). 7543–7550. 6 indexed citations
4.
Zhou, Yue, et al.. (2023). Comprehensive Kinematic Model of a Tendon-Driven Wearable Tremor Suppression Device. IEEE Transactions on Robotics. 40. 421–437. 3 indexed citations
5.
Zhou, Yue, et al.. (2022). Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 30. 2856–2865. 4 indexed citations
6.
Naish, Michael D., et al.. (2022). Multi-modal Prosthesis Control using sEMG, FMG and IMU Sensors. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 2983–2987. 5 indexed citations
7.
Naish, Michael D., et al.. (2017). A Breakthrough in Tumor Localization: Combining Tactile Sensing and Ultrasound to Improve Tumor Localization in Robotics-Assisted Minimally Invasive Surgery. IEEE Robotics & Automation Magazine. 24(2). 54–62. 17 indexed citations
8.
LeBel, Marie‐Eve, et al.. (2017). Energy-Based Metrics for Arthroscopic Skills Assessment. Sensors. 17(8). 1808–1808. 11 indexed citations
9.
Trejos, Ana Luisa, et al.. (2017). Development of a physical shoulder simulator for the training of basic arthroscopic skills. International Journal of Medical Robotics and Computer Assisted Surgery. 14(1). 9 indexed citations
10.
11.
Naish, Michael D., et al.. (2014). A Chance-Constrained Programming Approach to Preoperative Planning of Robotic Cardiac Surgery Under Task-Level Uncertainty. IEEE Journal of Biomedical and Health Informatics. 19(2). 612–622. 4 indexed citations
12.
Atashzar, S. Farokh, et al.. (2013). Robot-assisted lung motion compensation during needle insertion. 1682–1687. 11 indexed citations
13.
Patel, Rajni V., et al.. (2011). A chance-constrained approach to preoperative planning of robotics-assisted interventions. PubMed. 3. 2127–2130. 2 indexed citations
14.
Trejos, Ana Luisa, Shiva Jayaraman, Rajni V. Patel, Michael D. Naish, & Christopher M. Schlachta. (2010). Force sensing in natural orifice transluminal endoscopic surgery. Surgical Endoscopy. 25(1). 186–192. 22 indexed citations
15.
Trejos, Ana Luisa, et al.. (2010). New tactile sensing system for minimally invasive surgical tumour localization. International Journal of Medical Robotics and Computer Assisted Surgery. 6(2). 211–220. 27 indexed citations
16.
Jayaraman, Shiva, et al.. (2010). Toward construct validity for a novel sensorized instrument-based minimally invasive surgery simulation system. Surgical Endoscopy. 25(5). 1439–1445. 7 indexed citations
17.
Naish, Michael D., et al.. (2009). Design of a novel 3 degree of freedom robotic joint. 6 indexed citations
18.
Trejos, Ana Luisa, Rajni V. Patel, Michael D. Naish, & Christopher M. Schlachta. (2008). Design of a sensorized instrument for skills assessment and training in minimally invasive surgery. 965–970. 23 indexed citations
19.
Trejos, Ana Luisa, et al.. (2008). Feasibility of locating tumours in lung via kinaesthetic feedback. International Journal of Medical Robotics and Computer Assisted Surgery. 4(1). 58–68. 45 indexed citations
20.
Naish, Michael D., Elizabeth A. Croft, & B. Benhabib. (2003). Coordinated dispatching of proximity sensors for the surveillance of manoeuvring targets. Robotics and Computer-Integrated Manufacturing. 19(3). 283–299. 14 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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