Being able to balance is something most of us take for granted. However, approximately 35% of U.S. citizens 40 years and older are affected by vestibular-related balance issues. The vestibular system, located in the inner ear, is one of several sensory systems that provides our central nervous system with balance and spatial orientation information. When a person's vestibular system is impaired by disease or injury, he/she can experience balance and gait deficits in addition to dizziness and vertigo. There are physical, emotional, and monetary costs associated with sensory-based balance disabilities, such as vestibular disabilities, and the falls that typically follow bouts of balance instability. Most fall-related injuries occur during walking (gait), but treating imbalance during gait is challenging. Current clinical tools for assessing gait pathologies (gait abnormalities due to injury or disease) in people with vestibular disabilities do not fully capture body motion, neglecting potentially critical features of sensory-related disabilities during gait-based activities. The goal of this project is to develop and test data-driven algorithms (problem solving instructions) for characterizing pathological motion. This work will lead to new methods for assessing sensory-related gait disorders and support the development of novel rehabilitation strategies. As part of this research, large motion sensing networks will be combined with machine learning algorithms to identify and measure gait abnormalities in people with vestibular disabilities. Though the focus of this project is on vestibular disabilities, the methods developed can be generalized to a wide range of balance impairments stemming from sensory disabilities, injuries, neural disabilities, motor disabilities, and aging. This research will also contribute to the training of both undergraduate and graduate students through capstone design projects, clinical immersion experiences to identify unmet rehabilitation needs, and the development and implementation of an open access, online educational module focused on applications of machine learning for societal impact.

This project's primary purpose is to develop and assess data-driven machine learning (ML) algorithms that identify and quantify pathological gait in people with vestibular disabilities for the purposes of informing the creation of new assessment techniques and supporting the development of novel rehabilitation strategies. The Research Plan is organized under three objectives. The first objective is to create a shareable database of gait measurements from subjects with vestibular disabilities. Activities include: a) recruiting participants with vestibular deficits and age-matched healthy controls, b) collecting kinematic data during an experimental session in which subjects are instrumented with a full set of passive markers and up to 17 IMUs (Inertial Measurement Units), c) collecting clinical vestibular testing diagnostic data, e.g., electronystagmography test battery, and d) collecting Physical Therapist (PT) labels based on videotaped gait rehabilitation exercises that are viewed and rated on a 1-5 visual analog scale by a small cohort of PTs and d) sharing data by organizing data into tables that can be downloaded in a local database format. The second objective is to develop robust data-driven ML algorithms for automatically evaluating and characterizing pathological gait patterns in people with vestibular disabilities. Activities are organized under sub-objectives designed to learn data-driven models to a) automatically differentiate subjects with vestibular disabilities from healthy controls, b) characterize subpopulations by developing a notion of prototypical gait pathologies for each clinical subgroup and c) quantify the extent of the disability and generate hypotheses regarding the root sensorimotor or biomechanical problem. The third objective is to develop and prospectively evaluate a portable system for real-time assessment. Activities include: a) developing a portable smartphone gait assessment tool that will generate real-time ratings during gait-based rehabilitation exercises using data obtained from no more than 7 IMUs and b) prospectively testing the system in a proof-of-concept study involving 10 adults.

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.

Project Start
Project End
Budget Start
2018-09-01
Budget End
2020-08-31
Support Year
Fiscal Year
2018
Total Cost
$223,962
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109