Perla Maiolino

1.9k citations
81 papers · 1.4k · h-index 18

Impact in

Papers in

Perla Maiolino

76 papers receiving 1.4k citations

Peers

Perla Maiolino
Comparison fields: 5 of 76
  • Cognitive Neuroscience 584
  • Biomedical Engineering 1.0k
  • Human-Computer Interaction 119
  • Control and Systems Engineering 411
  • Mechanical Engineering 348
Replace Vincent Duchaine with:
Vincent Duchaine Canada
Uikyum Kim South Korea
Sungchul Kang South Korea
Alexander Schmitz Japan
Chaoyang Song China
Van Anh Ho Japan
Makoto Shimojo Japan
Josie Hughes Switzerland
Yingzhong Tian China
Zhiwei Luo Japan
Perla Maiolino relative to Vincent Duchaine Canada Vincent Duchaine's profile →
Citations per field
00.5×9.5×
Vincent Duchaine · 1×
Citations per year

Countries citing papers authored by Perla Maiolino

Since Specialization
Citations

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

Fields of papers citing papers by Perla Maiolino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Perla Maiolino, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Perla Maiolino Line = papers co-authored together Perla Maiolino links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011338
2 2013175
3 201881
4 202265
5 201561
6 201745
7 201943
8 201840
9 201928
10
Embedded Distributed Capacitive Tactile Sensor
200827
11 202325
12 202022
13 201322
14 202121
15 202120
16 202319
17 202019
18 201818
19 202217
20 202116

About Perla Maiolino

Perla Maiolino is a scholar working on Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering, Mechanical Engineering and Electrical and Electronic Engineering, having authored 81 papers that have together received 1.4k indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (38 papers), Tactile and Sensory Interactions (34 papers), Soft Robotics and Applications (30 papers), Robot Manipulation and Learning (28 papers), Modular Robots and Swarm Intelligence (11 papers), Advanced Materials and Mechanics (6 papers), Muscle activation and electromyography studies (4 papers) and Robotics and Sensor-Based Localization (4 papers). The work is most often cited by research in Cognitive Neuroscience (584 citations), Biomedical Engineering (1.0k citations), Human-Computer Interaction (119 citations), Control and Systems Engineering (411 citations) and Mechanical Engineering (348 citations). Perla Maiolino has collaborated with scholars based in United Kingdom, Italy and United States. Frequent co-authors include Giorgio Cannata, Giorgio Metta, Marco Maggiali, Lorenzo Natale, Fumiya Iida, Alexander Schmitz, Liang He, Josie Hughes, Luca Scimeca and Thrishantha Nanayakkara. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Sensors Journal, IEEE Transactions on Robotics, Soft Robotics and IEEE Robotics & Automation Magazine.

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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