{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:32:12Z","timestamp":1769164332167,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"name":"Italian Ministry of Education, University and Research","award":["009X3L8SW 004"],"award-info":[{"award-number":["009X3L8SW 004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2013]]},"abstract":"<jats:p>Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes\u2019 classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25\u201335 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon\u2019s mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon\u2019s mapping on the whole dataset. In the Bayes\u2019 approach, the two features were then fed to a Bayes\u2019 classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes\u2019 approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes\u2019 scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.<\/jats:p>","DOI":"10.1155\/2013\/343084","type":"journal-article","created":{"date-parts":[[2013,11,27]],"date-time":"2013-11-27T17:59:37Z","timestamp":1385575177000},"page":"1-7","source":"Crossref","is-referenced-by-count":14,"title":["SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds"],"prefix":"10.1155","volume":"2013","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3380-6994","authenticated-orcid":true,"given":"Maurizio","family":"Schmid","sequence":"first","affiliation":[{"name":"Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, 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