Presentation Title

Wearables and Deep Learning Classify Fall Risk from Gait in Multiple Sclerosis

Project Collaborators

Lindsey J. Tulipani (Graduate Student Mentor), Reed D. Gurchiek (Graduate Student Mentor), Dakota A. Allen, Lukas Adamowicz, Dale Larie, Andrew J. Solomon (Medical Center Collaborator), Nick Cheney (Collaborating Mentor), and Ryan S. McGinnis (PI)

Abstract

Falls are a significant problem for persons with multiple sclerosis (PwMS). Clinical fall risk assessments are reactive, which prevents the deployment of preventative interventions and motivates the need for new fall risk assessments in this population. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen and thus capturing a measure of fall risk. Moreover, they require the use of laboratory-based measurement technologies, which prevents clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (19% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (29%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Primary Faculty Mentor Name

Ryan McGinnis

Secondary Mentor NetID

ncheney

Secondary Mentor Name

Nick Cheney

Graduate Student Mentors

Lindsey Tulipani, Reed Gurchiek

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Biomedical Engineering

Primary Research Category

Health Sciences

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Wearables and Deep Learning Classify Fall Risk from Gait in Multiple Sclerosis

Falls are a significant problem for persons with multiple sclerosis (PwMS). Clinical fall risk assessments are reactive, which prevents the deployment of preventative interventions and motivates the need for new fall risk assessments in this population. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen and thus capturing a measure of fall risk. Moreover, they require the use of laboratory-based measurement technologies, which prevents clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (19% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (29%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.