The evolutionary development of bipedal stance, which freed the hands from locomotion, is considered a fundamental distinction between humans and our closest relatives. But two-legged locomotion is far less stable. Engineered devices frequently solve the stability problem by having a wide base of support, or concentrating the bulk of its weight lower down. In contrast, the human body has evolved with most of its mass concentrated higher up in the trunk, making it inherently unstable and prone to falls. This "mechanically unstable" design is complemented by a sophisticated neural control system that actively stabilizes the body. However, the details of how this neural controller solves the complex control problem of maintaining upright stance, while simultaneously avoiding obstacles and navigating varied terrain, are not well understood. This current lack of knowledge is a limiting factor in a range of related fields, from the development of intelligent prosthetic devices, to humanoid robots that can navigate complex environments, as well as rehabilitative methods for those with poor mobility due to neurological disease or injury. This project will use a combined experimental and computational approach to determine how humans control upright balance during walking, while navigating around obstacles or adjusting speed.
While research in human motor control has established basic principles about how the nervous system generates goal-related movements, it is unknown how different goals are integrated into a coherent pattern that addresses all tasks simultaneously. This project will study how humans combine the control of balance during walking with functional tasks such as avoiding obstacles and modifying speed and walking direction. The experimental component of this project will use electric stimulation of the vestibular system to probe how the neural controller reacts to sensed threats to upright posture and how these responses change at different points of the gait cycle. Virtual reality will be used to impose constraints from obstacles and study how humans integrate such functional tasks with balance control. The theoretical component of this project will develop a model of the field dynamics of neural populations regulating balance and establish principles for how the high-dimensional configuration space of the motor apparatus can be used to integrate multiple low-dimensional tasks into a coherent movement plan. This dynamic field model of neural control will be combined with an existing model of the biomechanics and spinal neurophysiology of human locomotion to generate predictive simulations of human walking patterns. Achieving a neural account of locomotion, balance, stepping, and obstacle avoidance using mutually informative theory and experiment will provide a functional framework for the neurophysiological basis of locomotion. This functional framework will take into account the varied demands of daily life activities, foster development of intelligent robots and devices, and illustrate how stability of locomotion deteriorates with aging and neurological disease.
Companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
