Comparison of IMU-based elbow joint angle estimation methods for improved clinical assessment of spasticity and rigidity

Marin, Nadja

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https://hdl.handle.net/2142/122074 Copy

Description

Title

Comparison of IMU-based elbow joint angle estimation methods for improved clinical assessment of spasticity and rigidity

Author(s)

Marin, Nadja

Issue Date

2023-12-08

Director of Research (if dissertation) or Advisor (if thesis)

Hsiao-Wecksler, Elizabeth T

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Inertial measurement unit
• IMU
• spasticity
• rigidity
• joint angle estimation
• sensor fusion algorithm

Abstract

Current clinical practices for assessing spasticity and rigidity involve passive stretch tests and grading the muscle response on a clinical scale (e.g., Modified Ashworth Scale for spasticity, Unified Parkinson’s Disease Rating Scale for rigidity). While these scales are widely used, they are limited in that they do not provide quantitative assessment of spasticity and rigidity. In our lab, a device called the Position, Velocity, and Resistance Meter (PVRM) was developed by Song [2] and modified by Pei [1]. The PVRM was designed to collect quantitative data: angular position and velocity for a given joint and resistive forces/moments felt by a clinician during a passive stretch test. The PVRM consists of a load cell – to record the force applied by the clinician to move the joint, two inertial measurement units (IMUs) – one on each consecutive body segment, and two electromyography sensors – over the agonist/antagonist muscle group pair for the joint. A clinical study in China using the PVRM was designed to build a large quantitative database of elbow kinematics and kinetics in patients with different levels of spasticity and rigidity, including healthy controls (expected n = 110) [1]. A clinical protocol was developed to describe the data collection and clinical assessment methods. During 2021-2022, preliminary data were collected from only 15 patients due to COVID-19 limitations. The angular segment data from each IMU were processed together to compute the joint angular data using a simple “subtraction method”. The subtraction method had limitations; it assumed that the shoulder abduction angle was zero throughout the clinical assessment, but some patients were unable to maintain zero shoulder abduction. As a result, the elbow angle estimated by the subtraction method degraded with nonzero shoulder abduction angles. In this thesis, a new joint angle estimation method, a sensor fusion algorithm adapted from Seel and colleagues ([3]–[5]), was investigated as an alternative to the subtraction method. To evaluate the effectiveness of each joint angle estimation method, two testbeds were developed to simulate motions of the right arm from the China clinical study testing protocol. Then, joint angle estimates computed using the IMU data were compared to the “ground truth” angle from the testbed system. The benchtop testbed simulated the motion of the arm by placing the IMUs on a test rig with a stepper motor and PVC piping for the forearm; encoder data from the stepper motor were used for ground truth data. The motion capture testbed better simulated the actual testing protocol by placing motion markers on wearable modules for each IMU. Ground truth joint movement data were estimated using a motion capture camera system. Two different testing protocols, the standard test (ST) and clinical simulation (CS), were developed for each testbed to investigate the joint estimation methods, and to also assess the impact of different testing parameters: shoulder abduction angles, directions of motion (flexion vs. extension), arm motion speeds, and shoulder flexion angles. For each testbed, the elbow angle was estimated from the IMU data using both the subtraction method and the sensor fusion algorithm. Each elbow angle estimate was compared to the testbed ground truth to determine which estimate was more accurate. The results from both testbeds were used to inform a revised PVRM clinical protocol that could be used once the China clinical study data collection is continued. Based on the results, the sensor fusion algorithm is recommended as the more accurate joint angle estimation method. The revised PVRM protocol advises the clinician to keep the patient’s arm at shoulder abduction angles < 90°. In addition, lower shoulder flexion angles are recommended to increase the accuracy of the elbow angle estimate. Higher motion speeds generally gave more accurate results, but the slow speed was also able to achieve acceptable errors with lower shoulder abduction angles.

Graduation Semester

2023-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Nadja Marin

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