Interactive & Emergent Autonomy Lab

Projects

Active Learning and Data-Driven Control Active Perception in Human-Swarm Collaboration Algorithmic Matter and Emergent Computation Control for Nonlinear and Hybrid Systems Cyber Physical Systems in Uncertain Environments Haptic Languages Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices Vibrissal Responsive Neurons

Information Maximizing Clinical Diagnostics

OpenSim
MFI
Muscle Activation

Kinematic and muscular redundancy in the human musculoskeletal system makes it difficult to evaluate the function of specific muscle groups. The neck is particularly challenging, with seven cervical vertebrae and more than 20 muscle groups. Muscle atrophy of the deep neck extensor muscles has been identified using quantitative Magnetic Resonance imaging in individuals with chronic whiplash disorders. However, the biomechanical consequences of weakness to his muscle group is unknown. In this work, we develop utilize musculoskeletal models, Bayesian inference, and information theory to develop a testing algorithm to efficiently estimate the strength of individual muscle groups of the neck through physical strength testing.

People

Rebecca Abbott (Ph.D. Student)

Collaborators

James Elliott, University of Sydney

Publications

The qualitative grading of muscle fat infiltration in whiplash using fat and water magnetic resonance imaging
R. Abbott, A Peolsson, J. West, J. Elliott, U Aslund, A Karlsson, O Dahlqvist Leinhard
The Spine Journal, vol. 18, pp. 717-725, 2018. Paper

Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference
R. Crawford, J Cornwall, R. Abbott, J. Elliott
BMC Musculoskeletal Disorders, vol. 18, no. 25, 2017. Paper

Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: part II - cervical spine musculature
J. Elliott, J Cornwall, E Kennedy, R. Abbott, R. Crawford
BMC Musculoskeletal Disorders, vol. 19, no. 1, 2017. Paper

The geography of fatty infiltrates within the cervical multifidus and semispinalis cervicis in individuals with chronic whiplash-associated disorders
R. Abbott, A. Pedler, M. Sterling, J. Hides, T. D. Murphey, M. Hoggarth, and J. Elliott
Journal of Orthopaedic and Sports Physical Therapy, vol. 45, no. 4, pp. 281–288, 2015.

Muscle-Fat MRI: 1.5 Tesla and 3.0 Tesla versus histology
A. Smith, T Parrish, R. Abbott, M. Hoggarth, K. Mendoza, Y. Chen, J. Elliott
Muscle & Nerve, vol. 50, pp. 170-176, 2014. Paper


Funding

This project is funded by National Institute of Health grant T32 EB009406.