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In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.<\/jats:p>","DOI":"10.1145\/2980179.2982440","type":"journal-article","created":{"date-parts":[[2016,11,11]],"date-time":"2016-11-11T17:02:54Z","timestamp":1478883774000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Data-driven inverse dynamics for human motion"],"prefix":"10.1145","volume":"35","author":[{"given":"Xiaolei","family":"Lv","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences and University of Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxiang","family":"Chai","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihong","family":"Xia","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,5]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Sensor Networks and Information Processing, 2013 IEEE Eighth International Conference on, IEEE, 271--276","author":"Adelsberger R.","unstructured":"Adelsberger , R. , and Troster , G . 2013. 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