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The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.<\/jats:p>","DOI":"10.1017\/s0263574717000467","type":"journal-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T04:08:01Z","timestamp":1509941281000},"page":"395-407","source":"Crossref","is-referenced-by-count":7,"title":["Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT"],"prefix":"10.1017","volume":"36","author":[{"given":"Nicholas B.","family":"Melo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos E. 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