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Embed. Comput. Syst."],"published-print":{"date-parts":[[2013,6]]},"abstract":"<jats:p>\n            An unsupervised learning algorithm is presented for segmentation and evaluation of motion data from the on-body Orient wireless motion capture system for mobile gait analysis. The algorithm is\n            <jats:italic>model-free<\/jats:italic>\n            and operates on the\n            <jats:italic>latent space<\/jats:italic>\n            of the motion, by first\n            <jats:italic>aggregating<\/jats:italic>\n            all the sensor data into a single vector, and then modeling them on a low-dimensional manifold to perform segmentation. The proposed approach is contrasted to a\n            <jats:italic>basic, model-based<\/jats:italic>\n            algorithm, which operates directly on the joint angles computed by the Orient sensor devices. The latent space algorithm is shown to be capable of retrieving qualitative features of the motion even in the face of noisy or incomplete sensor readings.\n          <\/jats:p>","DOI":"10.1145\/2485984.2485989","type":"journal-article","created":{"date-parts":[[2013,7,1]],"date-time":"2013-07-01T12:27:28Z","timestamp":1372681648000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Latent space segmentation for mobile gait analysis"],"prefix":"10.1145","volume":"12","author":[{"given":"Aris","family":"Valtazanos","sequence":"first","affiliation":[{"name":"University of Edinburgh"}]},{"given":"D. K.","family":"Arvind","sequence":"additional","affiliation":[{"name":"University of Edinburgh"}]},{"given":"Subramanian","family":"Ramamoorthy","sequence":"additional","affiliation":[{"name":"University of Edinburgh"}]}],"member":"320","published-online":{"date-parts":[[2013,7,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.03.013"},{"key":"e_1_2_1_2_1","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"Bishop C. 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Gaussian process latent variable models for visualisation of high dimensional data. In Advances in Neural Information Processing Systems.  Lawrence N. 2003. Gaussian process latent variable models for visualisation of high dimensional data. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_12_1","series-title":"Lecture Notes in Computer Science","volume-title":"-G","author":"Lei Y.-K.","year":"2010"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/11744085_28"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/1018430.1021276"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/10.605434"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 13th International Conference in Central Europe on Computer Graphics (WSCG'05)","author":"Quirion S."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Roweis S. T. and Saul L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 2323--2326.  Roweis S. T. and Saul L. K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 2323--2326.","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Rui Y."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.840727"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Tenenbaum J. B. de Silva V. and Langford J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290 5500 2319--2323.  Tenenbaum J. B. de Silva V. and Langford J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. 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