Presentation Title

Using Machine Learning and Wearables to Classify Multiple Sclerosis Fall Risk

Presenter's Name(s)

Brett MeyerFollow

Project Collaborators

Reed Gurchiek (Graduate Student Mentor)

Time

9:00 AM

Location

Jost Foundation Room

Abstract

Introduction: In the United States, there are nearly 1 million people living with Multiple Sclerosis. Multiple Sclerosis (MS) is a neurological disorder commonly characterized by fatigue, walking difficulties, numbness or tingling, and several other symptoms. These symptoms increase the risk of the MS population falling; which can prove detrimental for disease progression. Therefore, it is vital to provide patients with an accurate assessment of their fall risk and alert them when a fall is likely. Recent developments in wearable technologies has shown promise in using inertial mass units (IMUs) to quantify gait over extended periods of time, thus, allowing data to be gathered at home and in daily life routines. The purpose of this project is to develop an algorithm that determines when patients with MS are walking and determine whether the patient is classified as a faller or non-faller. This is an important step for ultimately creating a model that can predict fall risk in real time.

Materials and Methods: Data were collected from 29 subjects; each performing several daily life activities in the laboratory environment and from a thigh and chest mounted IMU at home for 48 hours. This data is analyzed using a support vector machine (SVM) classifier and bouts of walking are extracted. Following the identification of walking, a deep learning model will be used to group the walking of fallers and non-fallers.

Expected Results: Using the SVM classifier and deep learning model, patients with MS will be identified as a faller or non-faller based on walking IMU data.

Conclusion: The preliminary results obtained in this study suggest that it is possible to create a model that can distinguish between an MS patient classified as a faller or non-faller. This is a vital step in the process of creating a real-time fall risk model.

Primary Faculty Mentor Name

Ryan McGinnis

Secondary Mentor NetID

rgurchie, ltulipan

Graduate Student Mentors

Reed Gurchiek, Lindsey Tulipani

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Biomedical Engineering

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Health Sciences

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Using Machine Learning and Wearables to Classify Multiple Sclerosis Fall Risk

Introduction: In the United States, there are nearly 1 million people living with Multiple Sclerosis. Multiple Sclerosis (MS) is a neurological disorder commonly characterized by fatigue, walking difficulties, numbness or tingling, and several other symptoms. These symptoms increase the risk of the MS population falling; which can prove detrimental for disease progression. Therefore, it is vital to provide patients with an accurate assessment of their fall risk and alert them when a fall is likely. Recent developments in wearable technologies has shown promise in using inertial mass units (IMUs) to quantify gait over extended periods of time, thus, allowing data to be gathered at home and in daily life routines. The purpose of this project is to develop an algorithm that determines when patients with MS are walking and determine whether the patient is classified as a faller or non-faller. This is an important step for ultimately creating a model that can predict fall risk in real time.

Materials and Methods: Data were collected from 29 subjects; each performing several daily life activities in the laboratory environment and from a thigh and chest mounted IMU at home for 48 hours. This data is analyzed using a support vector machine (SVM) classifier and bouts of walking are extracted. Following the identification of walking, a deep learning model will be used to group the walking of fallers and non-fallers.

Expected Results: Using the SVM classifier and deep learning model, patients with MS will be identified as a faller or non-faller based on walking IMU data.

Conclusion: The preliminary results obtained in this study suggest that it is possible to create a model that can distinguish between an MS patient classified as a faller or non-faller. This is a vital step in the process of creating a real-time fall risk model.