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Laura A. Hallock

dblp:202/1873 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-0595-815XORCID · reported

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.

Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 33% Human-robot interaction · 33% Wearable and physiological sensing · 33%
Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Accessibility and assistive technology
assistive technology
0.312018
Empirical Quantification and Modeling of Muscle Deformation: Toward Ultrasound-Driven Assistive Device Control * This work was supported by the NSF National Robotics Initiative (award no. 81774), Siemens Healthcare (85993), and the NSF Graduate Research Fellowship Program · ICRA 2018
Human-robot interaction › physical human-robot interaction
prosthetic control
0.312018
Empirical Quantification and Modeling of Muscle Deformation: Toward Ultrasound-Driven Assistive Device Control * This work was supported by the NSF National Robotics Initiative (award no. 81774), Siemens Healthcare (85993), and the NSF Graduate Research Fellowship Program · ICRA 2018
Wearable and physiological sensing › acoustic sensing
ultrasonic sensing
0.312018
Empirical Quantification and Modeling of Muscle Deformation: Toward Ultrasound-Driven Assistive Device Control * This work was supported by the NSF National Robotics Initiative (award no. 81774), Siemens Healthcare (85993), and the NSF Graduate Research Fellowship Program · ICRA 2018
Robotics › Motion planning and robot control › robot control
exoskeleton control
0.112018
Empirical Quantification and Modeling of Muscle Deformation: Toward Ultrasound-Driven Assistive Device Control * This work was supported by the NSF National Robotics Initiative (award no. 81774), Siemens Healthcare (85993), and the NSF Graduate Research Fellowship Program · ICRA 2018

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

volumetric scanning · 0.7ultrasound imaging · 0.7motion capture · 0.7
YearPublicationVenuePosition
2018 Empirical Quantification and Modeling of Muscle Deformation: Toward Ultrasound-Driven Assistive Device Control * This work was supported by the NSF National Robotics Initiative (award no. 81774), Siemens Healthcare (85993), and the NSF Graduate Research Fellowship Program
abstract
Surface electromyography is currently the sensing modality of choice for control of biosignal-driven prostheses and exoskeletons; however, the sensor's noisy and aggregate nature inhibits collection of distinguishable signal streams to robustly manipulate multiple device degrees of freedom (DoF). We here explore 2D B-mode ultrasound as an alternative source of muscle activation data (namely, muscle deformation) that can be more precisely localized, allowing for the theoretical collection of multiple naturally-varying signals that could be used to control high-DoF assistive devices. We here present a proof-of-concept study showing a) the observability of muscle deformation via ultrasound, and b) novel descriptions of the spatially-varying nature of the signal. These analyses are accomplished through the study of nine volumetric scans of the biceps brachii under varied elbow angle and loading conditions, collected and spatially localized using an ultrasound scanner and motion capture. We here establish the feasibility of measuring several force-associated deformation signals (including muscle cross-sectional area and thickness) via real-time ultrasound scanning and quantify the spatial variation of these signals. Additionally, we propose future applications for both our signal characterizations and the generated muscle volume data set, including better design of assistive device sensor locations and validation of existing muscle deformation models.
Laura A. Hallock, Akira Kato, Ruzena Bajcsy
ICRA1