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However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.<\/jats:p>","DOI":"10.3390\/s22113968","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"3968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Unified Local\u2013Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-6635","authenticated-orcid":false,"given":"Sonia","family":"Das","sequence":"first","affiliation":[{"name":"National Institute of Technology Rourkela, Rourkela 769008, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sukadev","family":"Meher","sequence":"additional","affiliation":[{"name":"National Institute of Technology Rourkela, Rourkela 769008, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Upendra Kumar","family":"Sahoo","sequence":"additional","affiliation":[{"name":"National Institute of Technology Rourkela, Rourkela 769008, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.3390\/s120202255","article-title":"Gait analysis using wearable sensors","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/brainsci4010001","article-title":"Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems","volume":"4","author":"Castermans","year":"2013","journal-title":"Brain Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hamid, H., Naseer, N., Nazeer, H., Khan, M.J., Khan, R.A., and Shahbaz Khan, U. 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