Changepoint detection in biometric data: retrospective nonparametric segmentation methods based on dynamic programming and sliding windows

Shestoperov A.I., Ivchenko A.V., Fomina E.V.
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This paper is dedicated to the analysis of medical and biological data obtained through locomotor training and testing of astronauts conducted both on Earth and during spaceflight. These experiments can be described as the astronaut’s movement on a treadmill according to a predefined regimen in various speed modes. During these modes, not only the speed is recorded but also a range of parameters, including heart rate, ground reaction force, and others, are collected. In order to analyze the dynamics of the astronaut’s condition over an extended period, it is necessary to perform a qualitative segmentation of their movement modes to independently assess the target metrics. This task becomes particularly relevant in the development of an autonomous life support system for astronauts that operates without direct supervision from Earth. The segmentation of target data is complicated by the presence of various anomalies, such as deviations from the predefined regimen, arbitrary and varying duration of mode transitions, hardware failures, and other factors. The paper includes a detailed review of several contemporary retrospective (offline) nonparametric methods for detecting multiple changepoints, which refer to sudden changes in the properties of the observed time series occurring at unknown moments. Special attention is given to algorithms and statistical measures that determine the homogeneity of the data and methods for detecting change points. The paper considers approaches based on dynamic programming and sliding window methods. The second part of the paper focuses on the numerical modeling of these methods using characteristic examples of experimental data, including both “simple” and “complex” speed profiles of movement. The analysis conducted allowed us to identify the preferred methods, which will be further evaluated on the complete dataset. Preference is given to methods that ensure the closeness of the markup to a reference one, potentially allow the detection of both boundaries of transient processes, as well as are robust relative to internal parameters.

Keywords: aerospace medicine, locomotion test, time series, changepoint, segmentation, nonparametric, retrospective, dynamic programming, sliding window
Citation in English: Shestoperov A.I., Ivchenko A.V., Fomina E.V. Changepoint detection in biometric data: retrospective nonparametric segmentation methods based on dynamic programming and sliding windows // Computer Research and Modeling, 2024, vol. 16, no. 5, pp. 1295-1321
Citation in English: Shestoperov A.I., Ivchenko A.V., Fomina E.V. Changepoint detection in biometric data: retrospective nonparametric segmentation methods based on dynamic programming and sliding windows // Computer Research and Modeling, 2024, vol. 16, no. 5, pp. 1295-1321
DOI: 10.20537/2076-7633-2024-16-5-1295-1321
URL: http://crm-en.ics.org.ru/journal/article/3529/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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