If you would like to contribute your own algorithm to pyCGM2, please submit it via the contact page. We welcome community contributions to expand the library.
Gap filling
Gløersen, Øyvind; Federolf, Peter (2016) Predicting Missing Marker Trajectories in Human Motion Data Using Marker Intercorrelations. In : PloS one, vol. 11, n° 3, e0152616. DOI: 10.1371/journal.pone.0152616.
Burke, M.; Lasenby, J. (2016) Estimating missing marker positions using low dimensional Kalman smoothing. In : Journal of biomechanics, vol. 49, n° 9, p. 1854–1858. DOI: 10.1016/j.jbiomech.2016.04.016.
Code contribution, we thank Mickael Burke for sharing scripts.
Gait event methods
Zeni, J.A., Richards, J.G., Higginson, J.S., 2008. Two simple methods for determining gait events during treadmill and overground walking using kinematic data 27, 710–714. https://doi.org/10.1016/j.gaitpost.2007.07.007
O’Connor, Ciara M.; Thorpe, Susannah K.; O’Malley, Mark J.; Vaughan, Christopher L. (2007) Automatic detection of gait events using kinematic data. In : Gait & posture, vol. 25, n° 3, p. 469–474. DOI: 10.1016/j.gaitpost.2006.05.016.
Dumphart, Bernhard; Slijepcevic, Djordje; Zeppelzauer, Matthias; Kranzl, Andreas; Unglaube, Fabian; Baca, Arnold; Horsak, Brian (2023) Robust deep learning-based gait event detection across various pathologies. In : PloS one, vol. 18, n° 8, e0288555. DOI: 10.1371/journal.pone.0288555.