Noninvasive Assessment of Joint Motion Over Long Durations: System Evaluation and Data Analysis Methods

<p>The development, progression, and treatment of degenerative musculoskeletal diseases, such as carpal tunnel and shoulder impingement syndromes, may be better characterized when joint motions are assessed over long durations outside clinical, laboratory, or rehabilitation settings that involve standardized assessment, exercise protocols, and/or regimented movements. Assessment methods for human movement capture beyond laboratory or clinical experiments are typically limited to short capture times of less than one hour. Noninvasive, long-duration measurements of joint motion in occupational settings provides more insight into movement patterns and quantitative assessments regarding joint usage, which lead to a better understanding of the cumulative effects associated with repetitive joint motions.</p> <p>A small, autonomous data logging system using several bi-axial electrogoniometers has been developed to record three-dimensional joint motions over long durations of eight or more hours (Bernstein, 2008). The system was used for measurements of the wrists and shoulders to better characterize upper extremity musculoskeletal behaviors across multiple work sites. While the system had been previously constructed, field use of the data logging system and data analysis remained unexplored. The development of the system for practical field use, including sensor placement, data processing, and validation of captured joint movement, was the objective of this thesis.</p> <p>Fully analyzed wrist data, comparing field subjects completing identical tasks and different tasks at three different work sites, clearly identified variations in joint motion. Results indicate that progressive changes in motion patterns can be identified using frequency distribution histograms. Significant differences in measured motion exist between the electrogoniometer system and an observational motion analysis method that indicate the estimation of joint movement patterns from select durations within the work-day captured is less than 20% of the time exceeding ergonomic thresholds. Also, a difference of 10% to 20% between the results of the two motion assessment methods occurs within the selected durations.</p> <p>The information regarding the wrist can be analyzed without translational issues, but the complexity of the shoulder required more research to properly translate the voltage response of the electrogoniometer into shoulder position. To clearly correlate human shoulder movement and sensor response, an experiment was conducted with ten known regions of angular deviation within a range of 0° to 90° flexion and 0° to 90° abduction. The accuracy of prediction of human shoulder movement based on electrogoniometer response was limited to less than 80% within the ten selected regions.</p> <p>Further analysis of the existing long-duration field data on over 200 subjects will lead to the development of normal and abnormal motion pattern definitions. The quantification of variations in joint motions between subjects during work tasks can assist in identifying occupational risks.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2011

Author dc:creator

• Qadri, Maria J

Contributors dc:contributor

• Anthony J. Brammer; Martin G. Cherniack; Nicholas Warren
• Donald R. Peterson

• Biomechanics
• Motion Capture
• Joint Movement
• Computational Analysis

Repository record dc:identifier

https://digitalcommons.lib.uconn.edu/gs_theses/70

OAI identifier oai:identifier

oai:digitalcommons.lib.uconn.edu:gs_theses-1102