Date of Award

6-2024

Degree Name

MS in Mechanical Engineering

Department/Program

Mechanical Engineering

College

College of Engineering

Advisor

Eric Espinoza-Wade

Advisor Department

Mechanical Engineering

Advisor College

College of Engineering

Abstract

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak acceleration (PA), mean squared jerk (MSJ), and area under the curve (AUC) as kinematic features. These features were extracted from the DLC output and compared to those derived from the assumed ground-truth data from IMU sensors worn by the participants. The accuracy of this analysis was quantified using percent mean squared error (PMSE) between each IMU sensor and DLC.

PMSE analysis indicates that DLC-based kinematic features capture aspects of both accelerometer and gyroscope for the control participant. PA (8.78%) and AUC (3.28%) align more closely with the gyroscope, while MSJ (5.20%) demonstrates greater agreement with the accelerometer. On the other hand, for the stroke participant, DLC estimations for all kinematic features predominantly reflect data from the accelerometer. Across all datasets, AUC has the smallest PMSE values, suggesting that, based on our data, motor effort and energy expenditure in the tasks are best represented by DLC. Additionally, PMSE values for the stroke dataset are higher than those for the control, highlighting DLC's limitations in accurately detecting finer details of motion data in individuals with UE impairments. The results indicate that DLC reasonably estimates kinematic data for both participants, although further refinement of the methods is necessary to enhance the analysis of stroke data.

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