Jaden Levinson
Pronouns: She/Her
Research Mentor(s): Safa Jabri, Graduate Student
Research Mentor School/College/Department: Department of Mechanical Engineering, College of Engineering
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 12
Presenter: 4
Abstract
The vestibular system is a sensory system found in the inner ear that provides the brain with information about motion and spatial orientation. It helps maintain balance, stabilize the head/body during movement, and maintain good posture. If the vestibular system is impaired, a patient may experience dizziness, imbalance, and difficulty walking. Vestibular disorders are difficult to diagnose as they can stem from a variety of underlying conditions and require specialized equipment to ascertain a diagnosis (e.g., dynamic posturography, rotational chair). The long-term goal of this study is to use kinematic data to develop gait assessment technology that can automatically identify individuals with vestibular disorders. In this project, we used motion capture technology (MOCAP) to collect and analyze full-body kinematic data from subjects with vestibular issues and healthy controls to better understand kinematic differences between healthy and vestibular gait. Twenty-one subjects (11 with vestibular deficits and 10 healthy) were recruited to participate in the walking study. The subjects performed a series of tasks that required them to walk with varying speed and constraints (eyes closed, walking backwards, etc.) while wearing a set of full-body MOCAP markers. The data collected was then used to compute descriptive gait metrics, including step length, step time, and head/trunk rotations, using a motion-analysis software called Visual3D. Inferential statistic tests (t-tests) were then performed to compare the various metrics of healthy individuals with subjects with vestibular deficits. Conclusions have not yet been drawn as the study is still in the data processing phase. However, we plan to leverage gait metrics that show statistically significant differences between the two groups in order to develop automatic vestibular gait detection algorithms.
Authors: Jaden Levinson, Safa Jabri, Kathleen Sienko, Jenna Wiens
Research Method: Data Collection and Analysis
