{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:00:23Z","timestamp":1774944023956,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["NRF-2020R1A2C1013308"],"award-info":[{"award-number":["NRF-2020R1A2C1013308"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body\u2019s joints. To consider all of the joints, it is essential to determine a person\u2019s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach\u2019s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach\u2019s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.<\/jats:p>","DOI":"10.3390\/s22239113","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T03:58:16Z","timestamp":1669262296000},"page":"9113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Gait-Based Real-Time Gender Classification System Using Whole Body Joints"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhammad","family":"Azhar","sequence":"first","affiliation":[{"name":"Department of Computer Science & IT, University of Malakand, Chakdara 18800, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sehat","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Computer Science & IT, University of Malakand, Chakdara 18800, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalil","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Malakand, Chakdara 18800, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikram","family":"Syed","sequence":"additional","affiliation":[{"name":"School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaehyuk","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","article-title":"On using gait in forensic biometrics","volume":"56","author":"Bouchrika","year":"2011","journal-title":"J. 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