Welcome to the Mobility and Fall Prevention Research Laboratory (MFPRL) in the Department of Kinesiology & Community Health within the College of Applied Health Sciences at the University of Illinois at Urbana-Champaign. The goal of the lab is to further our understanding of the volitional control of compensatory postural responses and contribute to the existing body of fall prevention research in older adults. We use state-of-the-art motion capture, equipment for biomechanical measures, and non-invasive brain imaging to simultaneously record movements and brain activity during functional whole body movements. Using interdisciplinary approaches, our lab examines the underlying behavioral and neural mechanisms underlying postural dysfunction in older adults with neurological disorders, such as Parkinson’s disease and multiple sclerosis to identify prospective behavioral and neural biomarkers of neurological disorders and motor impairment. Given the interdisciplinary nature of our work, our lab collaborates with colleagues in Medicine, Neuroscience, Physics, Engineering, and Kinesiology across the University of Illinois campus and other national universities and institutions.

I encourage you to contact me if you have any questions or an interest in our program.

Dr. Manuel Hernandez

Recent Work

View the videos below for a brief introduction to some of our work:

Presentation of machine learning work at virtual 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics Workshop.

Virtual paddleboarding: An immersive paddleboarding game that motivates those going through physical rehabilitation by testing their balance in a fun way. The project integrates the HTC Vive together with motion capture and a moving platform.

Recent Presentations From Team Members


Wearable sensors for Parkinson’s can improve with machine learning, data from healthy adults

Low-cost, wearable sensors could increase access to care for patients with Parkinson’s disease. New machine-learning approaches and a baseline of data from healthy older adults improve the accuracy of the results from such sensors, University of Illinois Urbana-Champaign researchers and clinical collaborators found in a new study.

Study examines brain activity and balance changes as we age

When an older adult sways while standing or carefully adjusts their footing on uneven ground, it’s probably not all about weakened muscles. New interdisciplinary research suggests these effects may be outward signs of changes in brain activity that accompany aging, even in healthy people…

Team uses digital cameras, machine learning, to predict neurological diseases

In an effort to streamline the process of diagnosing patients with multiple sclerosis and Parkinson’s disease, researchers used digital cameras to capture changes in gait – a symptom of these diseases – and developed a machine-learning algorithm that can differentiate those with MS and PD from people without those neurological conditions….