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In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below $$2.8\\pm 2.1^{\\circ }$$<\/jats:tex-math>\n \n 2.8<\/mml:mn>\n \u00b1<\/mml:mo>\n 2<\/mml:mn>\n .<\/mml:mo>\n \n 1<\/mml:mn>\n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Our promising results demonstrate the robustness of the novel CoLiTrack approach to make \u201cnext-generation prostheses\u201d more user-friendly, functional and safe.<\/jats:p>","DOI":"10.1007\/s00371-021-02138-x","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T10:29:28Z","timestamp":1619346568000},"page":"2635-2645","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-time limb tracking in single depth images based on circle matching and line fitting"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6265-8172","authenticated-orcid":false,"given":"Michael","family":"Tschiedel","sequence":"first","affiliation":[]},{"given":"Michael Friedrich","family":"Russold","sequence":"additional","affiliation":[]},{"given":"Eugenijus","family":"Kaniusas","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Vincze","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,25]]},"reference":[{"key":"2138_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-019-01740-4","author":"J Wu","year":"2020","unstructured":"Wu, J., Hu, D., Xiang, F., Yuan, X., Su, J.: 3D human pose estimation by depth map. 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