Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Authors

Masoud Abdollahi, Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Ehsan Rashedi, Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Sonia Jahangiri, Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Pranav Madhav Kuber, Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Nasibeh Azadeh-Fard, Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Mary Dombovy, Rochester Regional HealthFollow

Department

Neurology

Document Type

Article

Publication Title

Sensors (Basel, Switzerland)

Abstract

BACKGROUND: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy.

OBJECTIVE: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols.

METHODS: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models-Support Vector Machine, Logistic Regression, and Random Forest-were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated.

RESULTS: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk.

CONCLUSION: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.

First Page

812

DOI

10.3390/s24030812

Volume

24

Issue

3

Publication Date

1-26-2024

PubMed ID

38339529

Recommended Citation

Abdollahi, M., Rashedi, E., Jahangiri, S., Kuber, P. M., Azadeh-Fard, N., & Dombovy, M. (2024). Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking. Sensors (Basel, Switzerland), 24 (3), 812. https://doi.org/10.3390/s24030812