Christopher Shallal

Christopher Shallal

I'm a PhD candidate in the Harvard–MIT Program in Health Sciences and Technology and a researcher in the Biomechatronics Group at the MIT Media Lab, advised by Prof. Hugh Herr.

Prior to my doctoral research, I received my B.S. in Biomedical Engineering from Johns Hopkins University. During my doctoral work, I have been supported as an NSF Graduate Research Fellow, an MIT UCEM Scholar supported by the Alfred P. Sloan Foundation, an MIT HEALS Graduate Fellow, and a K. Lisa Yang Bionics Graduate Fellow.

Email / Google Scholar / LinkedIn / MIT Profile / Lab

Background

My work is shaped by engineering practice and by my lived experience as a person with bilateral lower limb amputations. I approach bionic reconstruction as an integrated systems engineering problem in which anatomy, neural signaling, embedded electronics, control algorithms, and robotic hardware must be designed together. The coupling among these components produces emergent behavior that cannot be understood by studying any one subsystem in isolation. Engineering and interrogating the complete system in practice therefore offers a way to uncover principles of human motor control. These scientific insights, in turn, reveal how each anatomical, sensing, computational, and mechanical component can be improved and advanced.

Doctoral Research Highlights

My doctoral research focuses on advancing intuitive human-machine interfacing. In practice, intuitive control remains elusive because many interfaces rely on weak, indirect, or unstable signals, or require extensive processing that obscures the user's underlying motor intent. I am therefore developing tools that maximize the information we can extract from the peripheral nervous system. To further this goal, I design novel interfaces that can efficiently read from and write to the human nervous system. These tools are now being evaluated in an FDA IDE clinical trial (NCT06391697) to assess their feasibility for restoring intuitive control of bionic prostheses.

Tracking two 3 mm magnetic beads	A wireless magnetic implant system for continuous neuromuscular sensing Christopher C. Shallal^†, Cameron R. Taylor^†, Seong Ho Yeon, Richard J. Casler, Dara Oseyemi, Guillermo Herrera-Arcos, Junqing Qiao, Tony Shu, Jay Xu, Daniel Levine, Ana Rajcevic, Sean Boerhout, Aimee Liu, Ellen G. Clarrissimeaux, Joseph A. Paradiso, Matthew J. Carty, Hugh M. Herr ^† Equal contribution. medRxiv, 2025 preprint / code / algorithm PDF / overview videos We present MuSE (Muscle State Estimator), a skin-mounted magnetometer array that wirelessly tracks 3 mm diameter magnetic beads implanted in muscle, enabling continuous neuromuscular sensing across multiple degrees of freedom for prosthetic control. MuSE uses INFO, an extended information filter, to estimate magnet position and velocity in real time.
Learn more about MuSE sensing and tracking MuSE at a glance From muscle dynamics to neuroprosthetic control The platform pairs implanted 3 mm magnetic beads with a skin mounted array of 640 sensors and real time state estimation, translating muscle dynamics into continuous signals for neuroprosthetic control. Because the beads move with the tissue, MuSE directly measures deep muscle mechanics, including muscle length and velocity, at the implant depth. Inside the system MuSE sensing hardware and INFO tracking Sensor array and electronics stack The FPGA enables parallel processing of the high density magnetometer array. Together, the 640 sensors and high throughput electronics extend tracking depth, reduce estimated magnet position error and variance, and enable submillimeter tracking. Implanted magnetic beads Pairs of passive 3 mm beads encode muscle length through their changing separation; their small, unpowered form keeps the implant minimally invasive. INFO tracking pipeline Sensor measurements, dipole physics, noise, magnet dynamics, and environmental disturbances are fused for real time state estimation. Using the information form lets measurements from the dense array contribute additively, while a second order motion model provides smooth position and velocity estimates without injecting noise into the state estimate. algorithm PDF / GitHub repository
	Continuous neural control of a 2-DOF ankle-foot prosthesis enables dynamic obstacle maneuvers after transtibial amputation Tsung-Han Hsieh^†, Hyungeun Song^†, Christopher Shallal^†, Daniel V. Levine^†, Seong Ho Yeon, Junqing Qiao, Tony Shu, Matthew J. Carty, John McCullough, Hugh M. Herr ^† Equal contribution. medRxiv, 2025 preprint / level ground clip / cross slope clip A prosthetic ankle and foot with multiple degrees of freedom enables continuous neural control, agile locomotion, and adaptation across complex terrain.
	Tissue-integrated bionic knee restores versatile legged movement after amputation Tony Shu, Daniel Levine, Seong Ho Yeon, Ethan Chun, Christopher C. Shallal, John McCullough, Rickard Brånemark, Matthew J. Carty, Marco Ferrone, Sean Boerhout, Alexander Ko, Corey L. Sullivan, Gloria Zhu, Michael Nawrot, Matthew Carney, Ged Wieschhoff, Gabriel Friedman, Hugh Herr Science, 2025 paper / MIT News / code / full video We combine muscle reconstruction, an implant anchored to bone, and continuous neural control in a neuroembodied prosthetic system that supports versatile movement and greater prosthetic embodiment.
	Modulation of Prosthetic Ankle Plantarflexion Through Direct Myoelectric Control of a Subject-Optimized Neuromuscular Model Tony Shu, Christopher Shallal, Ethan Chun, Aashini Shah, Angel Bu, Daniel Levine, Seong Ho Yeon, Matthew Carney, Hyungeun Song, Tsung-Han Hsieh, Hugh M. Herr IEEE Robotics and Automation Letters, 2022 paper A neuromuscular model optimized for the participant maps muscle activation to desired ankle dynamics, allowing direct modulation of powered prosthetic torque.
	Restoration of bilateral motor coordination from preserved agonist-antagonist coupling in amputation musculature Tony Shu, Shan Shan Huang, Christopher Shallal, Hugh M. Herr Journal of NeuroEngineering and Rehabilitation, 2021 paper A neuromuscular modeling and optimization framework estimates phantom limb movement and quantifies the motor benefits of preserved coupling between agonist and antagonist muscles.

Learn more about magnetomicrometry

Magnetomicrometry uses small magnetic beads implanted in muscle and external magnetic field sensors to track muscle length in real time. The videos below introduce the measurement approach and its use in prosthetic control.

MIT News / original research

Magnetic Implants for Muscle Tracking
MIT Media Lab

Magnetomicrometry-Based Control
MIT Media Lab

Selected News

May 2026 Featured by the MIT Yang Tan Collective for work on intuitive neural interfaces for prosthetics.
July 2025 Our tissue integrated bionic knee research was published in Science.
March 2021 Awarded a National Science Foundation Graduate Research Fellowship.