Application of Autocorrelation and Cross-correlation Analyses in Human Movement and Rehabilitation Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".