Towards Automated Performance Status Assessment: Temporal Alignment of Motion Skeleton Time Series

[1]  Gérard G. Medioni,et al.  Home Monitoring Musculo-skeletal Disorders with a Single 3D Sensor , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[3]  Fernando De la Torre,et al.  Canonical Time Warping for Alignment of Human Behavior , 2009, NIPS.

[4]  J. Sloan,et al.  Patient‐physician disagreement regarding performance status is associated with worse survivorship in patients with advanced cancer , 2008, Cancer.

[5]  Cyrus Shahabi,et al.  Patient reported outcomes can improve performance status assessment: a pilot study , 2019, Journal of Patient-Reported Outcomes.

[6]  Fernando De la Torre,et al.  Generalized Canonical Time Warping , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  George Trigeorgis,et al.  Deep Canonical Time Warping , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  E. McFadden,et al.  Toxicity and response criteria of the Eastern Cooperative Oncology Group , 1982, American journal of clinical oncology.

[9]  Ming Li,et al.  Mining Human Mobility to Quantify Performance Status , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[10]  Ming Li,et al.  Low‐dimensional dynamical characterization of human performance of cancer patients using motion data , 2018, Clinical biomechanics.

[11]  Sridhar Mahadevan,et al.  Manifold Warping: Manifold Alignment over Time , 2012, AAAI.

[12]  Abubakar Abid,et al.  Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders , 2018, NeurIPS.

[13]  Antonio Ortega,et al.  Validation of Automated Mobility Assessment Using a Single 3D Sensor , 2016, ECCV Workshops.

[14]  Ruzena Bajcsy,et al.  Berkeley MHAD: A comprehensive Multimodal Human Action Database , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).