Prospective Projects

While we have a wide variety of projects in the lab spanning diverse disciplines and scientific techniques, here are some general areas of research. These descriptions are intended to give you an idea of the types of question we ask and the skills you would learn in each type of project.

Biomechanics and muscle activity for normal and impaired gait and balance

Falls due to a loss of balance are a primary cause of injury and death in older adults, and are a debilitating manifestation of a wide range of neurological, musculoskeletal, and cognitive deficits. However, little is known about how specific deficits alter the control of balance. Muscle activation measured with electromyography (EMG) provides a direct window into neural activity during balance, but is complex and is thus far difficult to relate to functional or clinical measures of balance ability. We focus on the study of automatic postural responses (APR), which are rapid brainstem-mediated responses that activate muscles in response to an impending fall or disturbance to standing a balance control. In healthy humans, these responses are coordinated according to the direction of the fall. In order to do this, brainstem sensorimotor feedback circuits must correctly interpret multiple sensory inputs to interpret the direction of falling and then activate the correct muscles.

Balance control is evaluated using a custom perturbation platform in the Neuromechanics Lab. The platform is flush with the floor, can move in all directions in the horizontal plane, and is instrumented with two six-axis force plates. Measured responses from the perturbations include kinetics, kinematics (10 camera Vicon motion capture system, Oxford Metrics, Inc) and electromyographic (EMG) signals (48 channel telemetered system, Konigsburg, Inc), and brain activity measured through electroencephalography (EEG). There are a number of ongoing projects examining kinetic, kinematic, muscle, ad brain measures of balance control in healthy individuals as well as in individuals with Parkinson’s Disease, stroke, or highly skilled ballet dancers.

Related references: Torres-Oviedo and Ting 2007, Welch and Ting 2008, Safavynia et al 2012. Responsibilities: Assist in data collection from human subjects, Analysis of body motion and muscle activity during balance tasks using MATLAB and Vicon motion capture software

Mentors: J. Lucas McKay, Ph.D. (contact j.lucas.mckay at Jessica Allen, Ph.D. (contact jessica.allen at

Neuromechanical computer simulations

Controlling balance is a complex process that involves various neural mechanisms. Deficits in any of these mechanisms can lead to impaired balance control, increasing the chance of fall related injury or death. Computational techniques, such as musculoskeletal modeling, can allow us to directly probe the effect of specific neural mechanisms on the neuromuscular control of balance.

In this project we will examine neuromuscular aspects of balance control using detailed musculoskeletal modeling techniques combined with experimental biomechanical data. You will gain experience working with both musculoskeletal models and human subjects. The focus of the first semester will be learning about musculoskeletal models, which will involve working on a project examining the sensitivity of the model to different modeling assumptions. Future research project(s) will involve applying this model to study the neuromuscular control of balance.

Knowledge of computer programming (c++, matlab, python, etc) is a plus.

Recommended readings: Allen and Neptune 2012, OpenSim 3.0 Documentation and Tutorials Mentor: Jessica Allen, Ph.D. (contact jessica.allen at

Human robotics

Possible projects in the areas of human robotics and feedback control theory
1) Delayed feedback neuromechanical model and robust control analysis of human balance (application to impairments in Parkinson’s disease)
2) Principles of human-human gait assistance for design of autonomous assistive robots
3) Design and simulation of personalized muscle stimulation for gait rehabilitation post-stroke
4) Robotic assessment of ankle rigidity in Parkinson’s disease
5) Unsupervised learning analysis and modeling of gait and balance