A Systematic Method for Outlier Detection in Human Gait Data

When it comes to observing and measuring human gait data for further analysis, determining whether the observed behavior is within the normal range of variability, or should be considered abnormal, is very challenging. Moreover, usually gait data are multivariate including motion capture, electromyography, force measurements, etc., each source having its own unique causes of irregularities and anomalies. This paper introduces a unique algorithm for outlier detection in periodic gait data using multiple sources and multiple procedures to improve the overall accuracy. The proposed algorithm's performance is evaluated using realistic synthetic gait data to gauge its accuracy to a truly objective known solution. It is shown that the proposed method is able to detect 91.2% of the true outliers in an extensive synthetic dataset, while only producing false positives at a rate of 0.1%, outperforming other procedures usually utilized in gait data outlier detection. The proposed method is a systematic way of removing outliers from gait data, with direct applications to human biomechanics, rehabilitation and robotics, and can be applied to other scientific fields dealing with periodic data.