Dimension Reduction by Fourier Method for Soleus Muscle Activity Pattern for Anterior Cruciate Ligament Deficient and Healthy Subjects

• Activity Number: 79
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistical Consulting
• Author(s): Resmi Gupta*+ and Jeff R. Houck and Gregory E. Wilding
• Companies: Cincinnati Children's Hospital Medical Center and Ithaca College Rochester Center and The State University of New York at Buffalo
• Keywords: data reduction ; fourier analysis ; trigonometric regression

Abstract

Electromyography (EMG) signals used to record muscle activation patterns during walking often pose challenges when working with clinicians. Because of the periodic, high dimensional nature of EMG data, dimension reduction remains a crucial component of analysis.EMG signals were used to record soleus muscle activity patterns for healthy and anterior cruciate ligament (ACL) deficient (copers, non-copers) subjects. The purpose of this study was to compare soleus muscle activation patterns for ACL deficient (copers, non-copers) and healthy subjects across four discrete motor-related tasks (anticipated: side-step, straight; unanticipated: side-step, straight) using fourier coefficients. A total of 11 fourier coefficients were used to define soleus muscle activation patterns (quarter million data points) during each task for each subject. Linear mixed model results yielded overall group difference between copers, non-copers and healthy subjects for all walking tasks. However, non-significant difference between copers and healthy subjects reflected similar muscle activation pattern to maintain knee stability. Implications for practice in clinical research will be presented.