Demo Data from EDBT 2026 / reviewers in the wild / expert
Mark M. Iskarous
dblp:232/8187
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-4208-3943ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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.
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 50% Haptics and multimodal interaction · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Artificial intelligence
1 paper |
Robot manipulation · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Haptics and multimodal interaction › haptics
biomimetic tactile sensor |
0.4 | 1 | 2019 | E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses · Proc. IEEE 2019 |
Wearable and physiological sensing › flexible electronics
electronic skin |
0.4 | 1 | 2019 | E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses · Proc. IEEE 2019 |
Robotics › Robot manipulation
dexterous manipulation |
0.1 | 1 | 2019 | E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses · Proc. IEEE 2019 |
Robotics › Robot manipulation › robotic hand
prosthetic hand control |
0.1 | 1 | 2019 | E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses · Proc. IEEE 2019 |
Methods — techniques the papers use, named apart from their topics
pattern recognition of electromyogram signals · 1.1neuromorphic encoding · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | E-Skins: Biomimetic Sensing and Encoding for Upper Limb ProsthesesabstractProsthetic hands, today, have anthropomorphic, multifinger design. A common control method uses pattern recognition of electromyogram signals. However, these prostheses do not capture the human hand's sensory perception, which is critical for prosthesis embodiment and dexterous object manipulation. This problem can be solved by sensorized electronic skin (e-skin) composed of various sensors that transduce sensory percepts such as touch, pressure, temperature, and pain, just as human skin does. This review will present the physiology of the receptors that encode tactile, thermal, nociceptive, and proprioceptive information. The e-skin is designed to mimic these receptors and their responses. We review each sensor subtype, and its design and performance when embedded in the e-skin. Next, we review the spiking response of the receptors, which are then relayed to sensory nerves and encoded by the brain as sensory percepts. The e-skin system is designed to produce neuromorphic or receptorlike spiking activity. Computational models to mimic these sensory nerve signals are presented and then various methods to interface with the nervous system are explored and compared. We conclude the review with the state of the art in e-skin design and deployment in closed-loop applications that demonstrate the benefits of sensory feedback for amputees. Mark M. Iskarous, Nitish V. Thakor |
Proc. IEEE | 1 |