page updated in June 2021
Seungmoon Song

Postdoctoral Fellow
Mechanical Engineering
Stanford University

Email: ssm0445 at gmail
CV



I will be joining Northeastern University in Jan 2022 as an assistant professor in Mechanical & Industrial Engineering. My lab will focus on modeling the neuromechanics of human movement and applying it to assistive devices and rehabilitation treatment.

I am looking for graduate students and postdocs to join my lab, so please email me if interested in the following directions.

1. Simulation framework to develop ankle exoskeleton gait assistance for older adults [project link]
keywords: neuromechanical simulation, leg exoskeleton, gait experiment, older adult with knee osteoarthritis

2. Deep reinforcement learning for modeling human locomotion control [project link]
keywords: neuromechanical simulation, deep reinforcement learning, NeurIPS: Learn to move competition

For the postdoc positions, I am looking for researchers with one or more of the following expertise:
- Neuromechanical simulation for human motor control
- Human gait experiment/analysis for assistive device
- Deep reinforcement learning for continuous control
- Physics simulation for legged characters



I am a postdoctoral fellow at the Mechanical Engineering Department of Stanford University. I work with Drs. Steve Collins on gait assistive exoskeletons. I am developing human-in-the-loop optimization frameworks for exoskeleton assistance that can improve human locomotion performance in all dimensions of comfort, safety, efficiency, and speed. I am also interested in better understanding motor learning and across-subject variability we observe from subjects walking in exoskeletons.

I am a recipient of the NIH Pathway to Independence Award (K99/R00, NIA) for the project Simulation framework to develop ankle exoskeleton gait assistance for older adults. In this project, I will adapt the neuromechanical models from my previous studies to predict outcomes of exoskeleton assistance.

I am also the lead organizer of the NeurIPS 2019: Learn to Move competition. Through this competition, we aim to bridge researchers from neuroscience, biomechanics, robotics, and machine learning to model neuro-musculo-skeletal dynamics of human movement.
My broader goal is to better understand and model the motor control and biomechanics of human locomotion and to apply the obtained knowledge to rehabilitation engineering. I develop computational models and conduct human experiments to propose and evaluate control hypotheses. For my Ph.D. study at the Robotics Institute of Carnegie Mellon University with Dr. Hartmut Geyer, I developed and evaluated neuromechanical control models to understand the circuitry-to-behavior layer of human locomotion. I envision my research to bring about simulation testbeds (which I call the digital locomotor clones) for training protocols and assistive devices as well as intelligent motion controllers for assistive and humanoid robots.


Journal papers


Optimized hip-knee-ankle exoskeleton assistance reduces the metabolic cost of walking with worn loads
GM Bryan, PW Franks, S Song, R Reyes, MP O'Donovan, KN Gregorczyk & SH Collins
submitted
[preprint]

Optimized hip-knee-ankle exoskeleton assistance at a range of walking speeds
GM Bryan, PW Franks, S Song, AS Voloshina, R Reyes, MP O'Donovan, KN Gregorczyk & SH Collins
submitted
[preprint]

Deep reinforcement learning for modeling human locomotion in neuromechanical simulation
S Song, Ł Kidziński, XB Peng, C Ong, J Hicks, S Levine, CG Atkeson & SL Delp
submitted
[preprint] [video 2017] [video 2018] [video 2019]

Optimizing exoskeleton assistance for faster self-selected walking
S Song & SH Collins
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021
[early access] [video 1] [video 2] [video Stanford] [press]

Using force data to self-pace an instrumented treadmill and measure self-selected walking speed
S Song, H Choi & SH Collins
Journal of NeuroEngineering and Rehabilitation, 2020
[paper] [code] [video]

Predictive neuromechanical simulations indicate why walking performance declines with aging
S Song & H Geyer
The Journal of Physiology, 2018
[paper] [press]

Evaluation of a Neuromechanical Walking Control Model Using Disturbance Experiments
S Song & H Geyer
Frontiers in Computational Neuroscience, 2017
[paper] [video 1] [video 2]

A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion
S Song & H Geyer
The Journal of Physiology, 2015
[paper] [video] [model] [journal cover]



Conference papers


Bayesian optimization using domain knowledge on the ATRIAS biped
A Rai, R Antonova, S Song, W Martin, H Geyer & CG Atkeson
IEEE ICRA, 2018
[payper] [video]

Towards a hierarchical neuromuscular control model with reflex-based spinal control - A study with a simple running model
S Song
International Symposium on Advanced Intelligent Systems, 2015
[paper] [video] [best presentation award]

Regulating speed in a neuromuscular human running model
S Song & Hartmut Geyer
IEEE Humanoids, 2015
[paper] [video]

Toward a virtual neuromuscular control for robust walking in bipedal robots
Z Batts, S Song & Hartmut Geyer
IEEE IROS, 2015
[paper] [video]

Development of a bipedal robot that walks like an animation character
S Song, J Kim & K Yamane
IEEE ICRA, 2015
[paper] [video] [talk] [press]

Integration of an adaptive swing control into a neuromuscular human walking model
S Song, R Desai & H Geyer
IEEE EMBC, 2013
[paper] [video]

Generalization of a muscle-reflex control model to 3D walking
S Song & H Geyer
IEEE EMBC, 2013
[paper]

The effect of foot compliance encoded in the windlass mechanism on the energetics of human walking
S Song, C LaMontagna, SH Collins & H Geyer
IEEE EMBC, 2013
[paper] [video] [project page]

Regulating speed and generating large speed transitions in a neuromuscular human walking model
S Song & H Geyer
IEEE ICRA, 2012
[paper] [video] [slides]

The energetic cost of adaptive feet in walking
S Song & H Geyer
IEEE ROBIO, 2011
[paper] [slides] [project page]

Development of an omnidirectional walking engine for full-sized lightweight humanoid robots
S Song, YJ Ryoo & DW Hong
ASME IDETC, 2011
[paper] [video1] [video2] [slides] [project page]

Linear-time encodable rate-compatible punctured LDPC codes with low error floors
S Song, D Hwang, S Seo & J Ha
IEEE VTC, 2008
[paper]



Conference abstracts

S Song, H Choi, K Poggensee, CG Atkeson & SH Collins, Human-in-the-loop optimization of ankle-exoskeleton assistance for faster preferred walking speed: a preliminary study, Dynamic Walking, 2019. [5 min talk]

S Song, Ł Kidziński, R Khidorka, C Ong, S Mohanty, J Hicks, J Ku, S Carroll, S Levine, M Salathé, CG Atkeson, SH Collins & S Delp, Learn to Move: a competition to bridge biomechanics, neuroscience, robotics, and machine learning to model human motor control, Dynamic Walking, 2019. [10 min talk + Q&A]

S Song, H Geyer, SH Collins & CG Atkeson, Towards predictive neuromechanical simulations for pathological gait and assistive devices, World Congress of Biomechanics, 2018. [poster]

A Falisse, G Serrancoli, C Dembia, S Song, I Jonkers & F De Groote, "Computationally efficient pre-dictive muscle-driven simulations of 3D walking," World Congress of Biomechanics, 2018.

S Song, Y Aucie & G Torres-Oviedo, Can split-belt treadmill walking be explained with a reflex-based model?, Neuroscience, 2017. [poster]

S Song & H Geyer, Modeling and exploring elderly walking with neuromechanical simulations, Dynamic Walking, 2017. [12 min talk + Q&A]

S Song & H Geyer, A spinal reflex based neuromuscular model of human locomotion investigated against unexpected disturbances, Neuroscience, 2016. [poster]

S Song & H Geyer, Testing a neuromuscular locomotion control model against human experiments, Dynamic Walking, 2016. [10 min talk + Q&A]

S Song & H Geyer, Using a neuromuscular model of human locomotion to control bipedal robots, Dynamic Walking, 2015. [poster]

S Song & H Geyer, Robust 3D locomotion models using primarily reflex control, Dynamic Walking, 2013. [10 min talk + 10 min Q&A]




Patents

J Kim, K Yamane & S Song, Method for developing and controlling a robot to have movements match-ing an animation character, United States Patent 9427868, 2016. [link]

J Nam, J An, D Hwang, J Ha & S Song, Apparatus and method for encoding low density parity check code, Korean patent 10-0999272-00-00, 2010. [link]




Invited talks

BioRob 2020 Workshop on Community-Based Rehabilitation Research using Wearable Devices, November 2020.
“Toward predictive simulation for rehabilitation treatment”
[15 min talk + 5 min Q&A]

University of Delaware, September 2020.
“Towards predictive simulation of human movement for assistive devices and rehabilitation treatment”
[40 min talk + 20 min Q&A] abstract

WearRAcon, March 2020.
“Towards optimal gait assistance”
[20 min talk + 5 min Q&A] [link]

NeurIPS Deep RL workshop, December 2019.
“Learn to Move competition”
[15 min talk] [link]

Universities in Europe, July 2018
“Modeling human locomotion control and its applications”
[40 min talk + 20 min Q&A]

Universities and research institutes in Korea, July 2017
“Neuromechanical control models of human locomotion and their applications to rehabilitation engineering”
[40 min talk + 20 min Q&A]

Universities and companies in Korea, November 2015
“A neuromuscular model of human locomotion and its applicaiton to robotic assistive devices”
[40 min talk + 20 min Q&A]

The 10th workshop on humanoid soccer robots at IEEE Humanoids, November 2015.
“Neuromuscular model of human locomotion that can generate diverse locomotion behaviors”
[30 min talk] [link]




Technical writings in Korean

“Understanding the control of human locomotion through simulation and its application to robotic assistive devices,” MATERIC (mechanical and construction engineering research information center), February, 2016. [article]

“Robotic lower-limb prosthetics related technical issues - 2. Control,” ROBOT (monthly magazine), May 2013. [article]

“Robotic lower-limb prosthetics related technical issues - 1. Hardware,” ROBOT (monthly magazine), April 2013. [article]