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However, gait signals acquired from inertial measurement unit (IMU) sensors are often noisy, redundant, and high\u2010dimensional, making feature extraction and classification challenging. To address this, this study employs the multilevel discrete wavelet transform (DWT) to decompose gait signals into time\u2013frequency representations, allowing effective isolation of relevant motion components. Statistical features, mean, variance, kurtosis, and skewness are then extracted to perform dimensionality reduction, summarizing the signal distribution while preserving discriminative information. The combination of detail and approximation coefficients improves classification accuracy, with variance features and the random forest model achieving an average accuracy above 94% and a maximum accuracy of 96%, outperforming the decision tree and softmax regression. These results demonstrate that integrating DWT and statistical analysis provides a computationally efficient, interpretable, and reliable framework for gait identification. Future work will focus on real\u2010time implementation, multimodal biometric fusion, and low\u2010power edge\u2010based systems for continuous authentication.<\/jats:p>","DOI":"10.1155\/jece\/7926551","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:27:17Z","timestamp":1765884437000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multilevel DWT Statistical Feature Analysis for Gait Recognition: A Machine Learning Approach"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-4711","authenticated-orcid":false,"family":"Istiqomah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9712-965X","authenticated-orcid":false,"given":"Achmad","family":"Rizal","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"crossref","unstructured":"LeeL.andGrimsonW. 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