Application of Statistical Pattern Recognition in a Generating-Element Space for Human Activity Classification Using Smartphone Sensors

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dc.contributor.authorЗаболотній, Сергій Васильович
dc.contributor.authorZabolotnii, Serhii
dc.contributor.authorХотунов, Владислав Ігорович
dc.contributor.authorKhotunov, Vladyslav
dc.contributor.authorЧепинога, Анатолій Володимирович
dc.contributor.authorChepynoha, Anatolii
dc.contributor.authorKlopotovskyi, Pavlo
dc.contributor.authorКлопотовський, Павло Анатолійович
dc.date.accessioned2026-06-05T12:17:41Z
dc.date.available2026-06-05T12:17:41Z
dc.date.issued2026
dc.description.abstractHuman Activity Recognition (HAR) using smartphone sensors has become a key component in health monitoring, context-aware computing, and smart environments. Traditional machine learning approaches often assume a Gaussian distribution of sensor data, which may not hold true in real-world scenarios. This paper presents a novel application of the mathematical framework of decomposition in a space with a generating element to address problems in statistical pattern recognition. This approach, based on Kunchenko’s theory of stochastic polynomials, enables the effective processing of non-Gaussian data by constructing optimal polynomial features based on the minimization of the mean square error of the decomposition. We apply this method to the UCI HAR dataset, which contains accelerometer and gyroscope data from 30 individuals performing six daily activities. Our approach generates polynomial features of various orders that capture higher-order statistical dependencies in the sensor signals. Experimental results demonstrate that augmenting traditional time-domain and frequency-domain features with these polynomial features improves classification accuracy when using a SVM classifier. The method is particularly effective in distinguishing between static activities (such as sitting and standing), which are traditionally challenging to classify. The proposed approach offers a theoretically grounded feature engineering technique that enhances HAR performance while maintaining computational efficiency suitable for real-time applications.
dc.identifier.citationЗаболотній С.В., Хотунов В.І., Чепинога А.В., Клопотовський П.А. Application of Statistical Pattern Recognition in a Generating-Element Space for Human Activity Classification Using Smartphone Sensors. Lecture Notes (Book chapter). 2026. https://doi.org/10.1007/978-3-032-18415-3_4
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-032-18415-3_4
dc.identifier.urihttps://dr.csbc.edu.ua/handle/123456789/2244
dc.publisherSpringer Nature B.V.
dc.subjectTECHNOLOGY
dc.subjectSOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems science::Information technology
dc.titleApplication of Statistical Pattern Recognition in a Generating-Element Space for Human Activity Classification Using Smartphone Sensors
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