{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:43:11Z","timestamp":1777570991898,"version":"3.51.4"},"reference-count":48,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:p> Human activity recognition is an application of machine learning with the aim of identifying activities from the gathered activity raw data acquired by different sensors. In medicine, human gait is commonly analyzed by doctors to detect abnormalities and determine possible treatments for the patient. Monitoring the patient\u2019s activity is paramount in evaluating the treatment\u2019s evolution. This type of classification is still not enough precise, which may lead to unfavorable reactions and responses. A novel methodology that reduces the complexity of extracting features from multimodal sensors is proposed to improve human activity classification based on accelerometer data. A sliding window technique is used to demarcate the first dominant spectral amplitude, decreasing dimensionality and improving feature extraction. In this work, we compared several state-of-art machine learning classifiers evaluated on the HuGaDB dataset and validated on our dataset. Several configurations to reduce features and training time were analyzed using multimodal sensors: all-axis spectrum, single-axis spectrum, and sensor reduction. <\/jats:p>","DOI":"10.1142\/s0129065723500582","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T13:35:25Z","timestamp":1693834525000},"source":"Crossref","is-referenced-by-count":10,"title":["Human Gait Activity Recognition Using Multimodal Sensors"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0899-3564","authenticated-orcid":false,"given":"Diego","family":"Teran-Pineda","sequence":"first","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of M\u00e1laga Bulevar Louis Pasteur, 35, 29071, M\u00e1laga, Spain"},{"name":"Biomedical Research Institute of M\u00e1laga (IBIMA), C\/ Doctor Miguel D\u00edaz Recio, 28, 29010, M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6519-1213","authenticated-orcid":false,"given":"Karl","family":"Thurnhofer-Hemsi","sequence":"additional","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of M\u00e1laga Bulevar Louis Pasteur, 35, 29071, M\u00e1laga, Spain"},{"name":"Biomedical Research Institute of M\u00e1laga (IBIMA), C\/ Doctor Miguel D\u00edaz Recio, 28, 29010, M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2232-4562","authenticated-orcid":false,"given":"Enrique","family":"Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of M\u00e1laga Bulevar Louis Pasteur, 35, 29071, M\u00e1laga, Spain"},{"name":"Biomedical Research Institute of M\u00e1laga (IBIMA), C\/ Doctor Miguel D\u00edaz Recio, 28, 29010, M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"S0129065723500582BIB001","first-page":"230","volume-title":"Proc. 2009 Innovative Technologies in Intelligent Systems and Industrial Applications","author":"Senanayake C.","year":"2009"},{"key":"S0129065723500582BIB002","first-page":"1142","volume-title":"Proc. 2020 IEEE Int. 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