Publications by authors named "Laura A Hallock"
Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation.
IEEE Trans Neural Syst Rehabil Eng
January 2022
Article Synopsis
- Current methods for measuring muscle output force in real time are noninvasive and limit our understanding of movement and assistive devices.
- This study introduces muscle deformation as a promising signal to infer muscle force and create control systems for devices, showing that changes in muscle thickness correlate well with elbow force and can be measured with ultrasound.
- With real-time feedback, participants found this muscle deformation method easier and equally accurate for tracking tasks compared to traditional surface electromyography (sEMG) methods, with resources made available for further research on the Open-Arm project.*
OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics.
Annu Int Conf IEEE Eng Med Biol Soc
July 2019
We present a novel neural-network-based pipeline for segmentation of 3D muscle and bone structures from localized 2D ultrasound data of the human arm. Building from the U-Net [1] neural network framework, we examine various data augmentation techniques and training data sets to both optimize the network's performance on our data set and hypothesize strategies to better select training data, minimizing manual annotation time while maximizing performance. We then employ this pipeline to generate the OpenArm 2.
View Article and Find Full Text PDFBackground: Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in primary care and rural settings. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram interpretation, including (1) view identification, (2) image segmentation, (3) quantification of structure and function, and (4) disease detection.
Methods: Using 14 035 echocardiograms spanning a 10-year period, we trained and evaluated convolutional neural network models for multiple tasks, including automated identification of 23 viewpoints and segmentation of cardiac chambers across 5 common views.