Demo Data from EDBT 2026 / reviewers in the wild / expert
Benjamin Treussart
dblp:274/9286
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot manipulation · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › wearable robotics
exoskeleton |
0.4 | 1 | 2020 | Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load · ICRA 2020 |
Robotics › Robot manipulation › wearable robotics › exoskeleton
upper-limb exoskeleton |
0.4 | 1 | 2020 | Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load · ICRA 2020 |
Human-robot interaction › human-in-the-loop control
electromyography-based control |
0.4 | 1 | 2020 | Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load · ICRA 2020 |
Human-robot interaction › assistive robotics
physical assistance |
0.4 | 1 | 2020 | Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load · ICRA 2020 |
Methods — techniques the papers use, named apart from their topics
intention estimation · 0.9gravity compensation · 0.9electromyography · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Controlling an upper-limb exoskeleton by EMG signal while carrying unknown loadabstractImplementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to assist carrying an unknown load without using force sensors or specific handle. The method is based on user's intentions estimated through a wireless EMG armband allowing movement direction and intensity estimation along 1 Degree of Freedom. This control law aimed to behave like a gravity compensation except that the mass of the load does not need to be known. The proposed approach was tested on 10 participants during a lifting task with a single Degree of Freedom upper-limb exoskeleton. Participants performed it in three different conditions : without assistance, with an exact gravity compensation and with the proposed method based on EMG armband. The evaluation of the efficiency of the assistance was based on EMG signals captured on seven muscles (objective indicator) and a questionnaire (subjective indicator). Results showed a statically significant reduction of mean activity of the biceps, erector spinae and deltoid by 20%±14%, 18%±12% and 25% ± 16% respectively while comparing the proposed method with no assistance. In addition, similar muscle activities were found both in the proposed method and the traditional gravity compensation. Subjective evaluation showed better precision, efficiency and responsiveness of the proposed method compared to the traditional one. Benjamin Treussart, Franck Geffard, Nicolas Vignais, Frédéric Marin |
ICRA | 1 |