This thesis presents an approach of developing FUGL-MEYER test using Microsoft Kinect. This method of developing the application for FUGL-MEYER test will change the measurement format from subjective to objective. This approach can solve the intertester reliability problem of the FUGL-MEYER test. Software is developed to calculate the different joint angles so that it can give measurements in objective format and data gets stored in text file in respective test folders.
Testing has been done with human skeleton to check the distance from Kinect which gives more accurate results with minimum deviation. This thesis also gives some future development ideas. The main aim of this study is to apply this approach in development of FUGL-MEYER test to enhance the current measurement strategy of the test.
Table of Contents
CHAPTER 1: Introduction
1.1 Problem Statement
1.2 Aim
1.3 Objectives
1.4 Thesis Outline
CHAPTER 2: Background and Literature Review
2.1 Previous work in Neuro-Rehabilitation
2.2 Fugl – Meyer
CHAPTER 3: Requirement Specification
3.1 Purpose
3.2 System Overview
3.3 Analysis and Design
CHAPTER 4: Implementation and Testing
4.1 Implementation
4.2 Measurement
4.3 Testing
4.4 Discussion
CHAPTER 5: Conclusion
5.1 Summary of work
5.2 Future work proposed
APPENDICES:
Appendix A: List of FUGL-MEYER tests, Raw Data
A.1 List of FUGL-MEYER test
A.2 Raw Data
Objectives and Topics
The primary objective of this thesis is to develop a software application that transforms the traditional, subjective Fugl-Meyer assessment into an objective, technology-driven process using the Microsoft Kinect sensor. By tracking limb movements and calculating joint angles, the system aims to provide standardized numerical scores, thereby reducing inter-clinician variability and facilitating more precise monitoring of stroke patient recovery.
- Automated calculation of joint angles using the Microsoft Kinect SDK.
- Development of an objective scoring system for the Fugl-Meyer test.
- Creation of a user-friendly Graphical User Interface (GUI) for patients.
- Data logging and storage in text files to support remote clinical feedback.
- Evaluation of optimal Kinect-to-patient distance to minimize measurement deviation.
Auszug aus dem Buch
1.1 Problem statement
Stroke is a major cause of disability for adults. It takes long time to make them move without anyone’s assist.[1, 9, 11] To patients progress FUGL-MEYER developed one test. FUGL-MEYER developed method for evaluation of patient progress. This method applies cumulative numerical score to check the progress of patient. Every test has 3 readings and it’s in subjective form. Tests are divided into 2 parts 1) Upper Extremity 2) Lower Extremity.[13] 3 different readings define the progress of the patient. 0 score of test shows patient is not able to perform the test, 1 score shows patient can partially performs the test, 2 score means patient can perform the test completely. Total score for upper extremity is 66 and for lower extremity are 34.[13, 21, 22] On the basis of this scores clinician can tell the patient of the progress.
Due to its subjective form different clinician may give different results for same test and for same person. Pamela Duncan, Martha Propst, Steven Nelson proposed one paper on reliability of FUGL-MEYER test. It shows that FUGL-MEYER test is most reliable when performed by one clinician on one patient. Inter tester reliability was limited because small number of subjects. FUGL-MEYER assessment is reliable to check the progress of patient when only 1 examines the entire test.[14]
To remove this dependency of one examiner an attempt is made in this project to develop FUGL-MEYER test in objective form using Microsoft Kinect sensor. Microsoft Kinect sensor captures the movement of patient to give the proper measurement. Even if more than one clinician examines the same patient results for the test are going to be the same because Microsoft Kinect is calculating the readings as per the test. Measurements of the test should be in objective form ranging from 0.0 to 2.0. Results are calculated at the interval of 200 milliseconds on a frame of 3 seconds to get more accurate results.
Summary of Chapters
CHAPTER 1: Introduction: Discusses the motivation for developing an objective Fugl-Meyer test to overcome the limitations of subjective clinician evaluation using Microsoft Kinect.
CHAPTER 2: Background and Literature Review: Provides an overview of neuro-rehabilitation, existing robot-mediated therapies, and detailed insights into the Fugl-Meyer assessment protocol.
CHAPTER 3: Requirement Specification: Details the hardware and software needs for the project, including the system architecture and the specific goals of the application.
CHAPTER 4: Implementation and Testing: Describes the C++ development process, angle calculation algorithms, and the results of performance testing on skeletal models.
CHAPTER 5: Conclusion: Summarizes the project achievements, evaluates the accuracy of the proposed method, and outlines potential future improvements.
Keywords
Fugl-Meyer test, Stroke rehabilitation, Microsoft Kinect, Objective assessment, Joint angle calculation, Neuro-rehabilitation, Motion tracking, C++, Sensorimotor recovery, Clinical reliability, Assistive technology, Human-computer interaction.
Frequently Asked Questions
What is the core purpose of this research?
The research aims to automate the Fugl-Meyer assessment by replacing subjective clinical observation with objective measurements derived from Microsoft Kinect motion tracking.
Which assessment scale is being modernized?
The work focuses on the Fugl-Meyer (FMA) test, a globally recognized standard for evaluating motor function in stroke survivors.
What is the primary technical hurdle addressed?
The study tackles the issue of "inter-tester reliability," where different clinicians may assign different scores to the same patient's performance.
What technology is used for motion tracking?
The project utilizes the Microsoft Kinect sensor to capture patient movements, which are then processed via C++ algorithms to calculate joint angles.
How is the accuracy of the system validated?
The system was tested on a skeleton model at various distances to determine an optimal sensor configuration that minimizes measurement error.
What are the main outputs of the software?
The software generates standardized numerical scores based on standardized movement tests, which are stored in text files for clinical review.
How does the GUI assist the patient during the test?
The GUI provides both textual and visual (video) instructions, allowing patients to understand and execute the required movements independently.
Can this system be used remotely?
Yes, the thesis highlights that results are stored in files that can be easily emailed to clinicians, facilitating remote monitoring and feedback.
- Citar trabajo
- Nishant Raut (Autor), 2012, Autonomous assessment of Patient Progress. A software suite allowing objective and qualitative assessment of arm movements of a person with brain or physical injury, Múnich, GRIN Verlag, https://www.grin.com/document/906580
