Primary Publication
Laura Hallock, Akira Kato, and Ruzena Bajcsy. "Empirical quantification and modeling of muscle deformation: Toward ultrasound-driven assistive device control." In IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. (2018)  View

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.

Related Publications
Yonatan Nozik*, Laura A. Hallock*, Daniel Ho, Sai Mandava, Chris Mitchell, Thomas Hui Li, and Ruzena Bajcsy, "OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics," in International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. *equal contribution (2019)  View

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 2.0 data set, the first factorial set of multi-subject, multi-angle, multi-force scans of the arm with full volumetric annotation of the biceps and humerus. This data set has been made available on SimTK ( to enable future exploration of muscle force modeling, improved musculoskeletal graphics, and assistive device control.