Abstract
An algorithm for extracting individual signs of a person’s gait according to the data of a mobile phone accelerometer is considered. The developed algorithm includes the stage of data segmentation, correlation selection of motion patterns, and data clustering procedures. By selecting noisy segments, the quality of data clustering and the extraction of individual features is improved. The results of the algorithm work allow us to refine the existing models of changes in human motor behavior in personalized medicine and security systems. The article also discusses the features of data segmentation and the influence of external factors on the measurement results.
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