@InProceedings{Kulic2009c,
  Title                    = {Detecting changes in motion characteristics during sports training},
  Author                   = {Kuli\'{c}, D. and Venture, G. and Nakamura, Y.},
  Booktitle                = {Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society},
  Year                     = {2009},
  Pages                    = {4011--4014},

  Abstract                 = {This paper proposes a stochastic approach for representing and analyzing the gradual changes that occur in human movement during sports training. Human movement primitives are described using factorial hidden Markov models, and compared using the Kullback-Liebler distance, a measure of information divergence between two models. This representation is combined with an automated segmentation and clustering approach to enable the system to autonomously extract and group together movement primitives from continuous observation of human movement data. The proposed system is tested on a human movement dataset obtained over 4 months during training for a marathon. Experimental results demonstrate that the system is able to detect gradual changes in the human movement.},
  Doi                      = {10.1109/IEMBS.2009.5333502},
  ISSN                     = {1557-170X},
  Keywords                 = {Kullback-Liebler distance;automated segmentation;clustering approach;factorial hidden Markov models;human movement;information divergence;motion characteristics;sports training;stochastic approach;biomechanics;hidden Markov models;image motion analysis;motion measurement;stochastic processes;Adult;Algorithms;Athletes;Automation;Biomechanics;Cluster Analysis;Computer Simulation;Equipment Design;Female;Humans;Markov Chains;Motion;Movement;Running;},
  Timestamp                = {2011.06.10}
}

{"_id":"dGJSDBc3Zp7TQEc8G","bibbaseid":"kuli-venture-nakamura-detectingchangesinmotioncharacteristicsduringsportstraining-2009","downloads":0,"creationDate":"2017-09-14T16:34:36.689Z","title":"Detecting changes in motion characteristics during sports training","author_short":["Kulić, D.","Venture, G.","Nakamura, Y."],"year":2009,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/jfslin/jfslin.github.io/master/jf2lin.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Detecting changes in motion characteristics during sports training","author":[{"propositions":[],"lastnames":["Kulić"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Venture"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Nakamura"],"firstnames":["Y."],"suffixes":[]}],"booktitle":"Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society","year":"2009","pages":"4011--4014","abstract":"This paper proposes a stochastic approach for representing and analyzing the gradual changes that occur in human movement during sports training. Human movement primitives are described using factorial hidden Markov models, and compared using the Kullback-Liebler distance, a measure of information divergence between two models. This representation is combined with an automated segmentation and clustering approach to enable the system to autonomously extract and group together movement primitives from continuous observation of human movement data. The proposed system is tested on a human movement dataset obtained over 4 months during training for a marathon. Experimental results demonstrate that the system is able to detect gradual changes in the human movement.","doi":"10.1109/IEMBS.2009.5333502","issn":"1557-170X","keywords":"Kullback-Liebler distance;automated segmentation;clustering approach;factorial hidden Markov models;human movement;information divergence;motion characteristics;sports training;stochastic approach;biomechanics;hidden Markov models;image motion analysis;motion measurement;stochastic processes;Adult;Algorithms;Athletes;Automation;Biomechanics;Cluster Analysis;Computer Simulation;Equipment Design;Female;Humans;Markov Chains;Motion;Movement;Running;","timestamp":"2011.06.10","bibtex":"@InProceedings{Kulic2009c,\n Title = {Detecting changes in motion characteristics during sports training},\n Author = {Kuli\\'{c}, D. and Venture, G. and Nakamura, Y.},\n Booktitle = {Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society},\n Year = {2009},\n Pages = {4011--4014},\n\n Abstract = {This paper proposes a stochastic approach for representing and analyzing the gradual changes that occur in human movement during sports training. Human movement primitives are described using factorial hidden Markov models, and compared using the Kullback-Liebler distance, a measure of information divergence between two models. This representation is combined with an automated segmentation and clustering approach to enable the system to autonomously extract and group together movement primitives from continuous observation of human movement data. The proposed system is tested on a human movement dataset obtained over 4 months during training for a marathon. Experimental results demonstrate that the system is able to detect gradual changes in the human movement.},\n Doi = {10.1109/IEMBS.2009.5333502},\n ISSN = {1557-170X},\n Keywords = {Kullback-Liebler distance;automated segmentation;clustering approach;factorial hidden Markov models;human movement;information divergence;motion characteristics;sports training;stochastic approach;biomechanics;hidden Markov models;image motion analysis;motion measurement;stochastic processes;Adult;Algorithms;Athletes;Automation;Biomechanics;Cluster Analysis;Computer Simulation;Equipment Design;Female;Humans;Markov Chains;Motion;Movement;Running;},\n Timestamp = {2011.06.10}\n}\n\n","author_short":["Kulić, D.","Venture, G.","Nakamura, Y."],"key":"Kulic2009c","id":"Kulic2009c","bibbaseid":"kuli-venture-nakamura-detectingchangesinmotioncharacteristicsduringsportstraining-2009","role":"author","urls":{},"keyword":["Kullback-Liebler distance;automated segmentation;clustering approach;factorial hidden Markov models;human movement;information divergence;motion characteristics;sports training;stochastic approach;biomechanics;hidden Markov models;image motion analysis;motion measurement;stochastic processes;Adult;Algorithms;Athletes;Automation;Biomechanics;Cluster Analysis;Computer Simulation;Equipment Design;Female;Humans;Markov Chains;Motion;Movement;Running;"],"downloads":0},"search_terms":["detecting","changes","motion","characteristics","during","sports","training","kulić","venture","nakamura"],"keywords":["kullback-liebler distance;automated segmentation;clustering approach;factorial hidden markov models;human movement;information divergence;motion characteristics;sports training;stochastic approach;biomechanics;hidden markov models;image motion analysis;motion measurement;stochastic processes;adult;algorithms;athletes;automation;biomechanics;cluster analysis;computer simulation;equipment design;female;humans;markov chains;motion;movement;running;"],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}