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Emily G. Keller

dblp:353/6091 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-5732-6138ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

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%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › medical robotics
powered prosthesis
0.712023
Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes · ICRA 2023
Robotics › Robot manipulation › wearable robotics
prosthetic device
0.712023
Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes · ICRA 2023

Methods — techniques the papers use, named apart from their topics

floating body dynamics · 0.7constraint force estimation · 0.7
YearPublicationVenuePosition
2023 Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes
abstract
Many powered prosthetic devices use load cells to detect ground interaction forces and gait events. These sensors introduce additional weight and cost in the device. Recent proprioceptive actuators enable an algebraic relationship between actuator torques and ground contact forces. This paper presents a proprioceptive force sensing paradigm which estimates ground reaction forces as a solution to detect gait events without a load cell. A floating body dynamic model is obtained with constraints at the center of pressure representing foot-ground interaction. Constraint forces are derived to estimate ground reaction forces and subsequently timing of gait events. A treadmill experiment is conducted with a powered knee-ankle prosthesis used by an able-bodied subject walking at various speeds and slopes. Results show accurate gait event timing, with pooled data showing heel strike detection lagging by only 6.7 ± 7.2 ms and toe off detection leading by 30.4 ± 11.0 ms compared to values obtained from the load cell. These results establish proof of concept for predicting gait events without a load cell in powered prostheses with proprioceptive actuators.
Emily G. Keller, Curt A. Laubscher, Robert D. Gregg IV
ICRA1