Study design: Technical note.
Objectives: To provide background theory and information and to describe relevant applications of autocorrelation and cross-correlation methodology as they apply to the field of motor control in human movement and rehabilitation research.
Background: Commonly used methodologies for pattern and event recognition, determination of muscle activation timing for investigation of movement coordination, and motor control are generally difficult to implement, particularly with large datasets. A brief description of the underlying mathematical theory of correlation analyses is given, followed by 4 different examples of how this methodology is useful for research in the movement sciences.
Methods: Examples demonstrating the utility of correlation analyses are presented from several different studies conducted at the University of Waterloo.
Results: Autocorrelation was used to demonstrate the presence of 60-Hz noise in an electromyography signal that was not visible in the raw data. A "top-down" paraspinal muscle activation pattern was demonstrated for healthy adults during gait, with the use of cross-correlation. Cross-correlation was also used to quantify coactivation of bilateral gluteus medius muscles during standing in individuals who developed low-back pain. Gender differences in gluteus medius control of mediolateral center of pressure were seen with the use of cross-correlation.
Conclusion: Autocorrelation and crosscorrelation have been shown to be an effective tool for several different applications in the movement sciences. Examples of the method's utility include noise detection within a signal, determination of relative muscle activation onsets for postural control, objective quantification of muscle coactivation, and relating muscle activations with mechanical events.