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Results: 295
Number of items: 295
  • Hu, N., Englebienne, G., Lou, Z., & Kröse, B. (2015). Latent Hierarchical Model for Activity Recognition. IEEE Transactions on Robotics, 31(6), 1472-1482. https://doi.org/10.1109/TRO.2015.2495002
  • Open Access
    Hu, N., Englebienne, G., Lou, Z., & Kröse, B. (2015). A Hierarchical Representation for Human Activity Recognition with Noisy Labels. In W. Burgard (Ed.), IROS Hamburg 2015 conference digest: IEEE/RSJ International Conference on Intelligent Robots and Systems : September 28-October 02, 2015, Hamburg, Germany (pp. 2517-2522). IEEE. https://doi.org/10.1109/IROS.2015.7353719
  • Hogenboom, A., Smit, I., & Kröse, B. (2014). Demonstration: A digital coach for self-tracking athletes. In M. Auvray, & C. Duriez (Eds.), Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014, Versailles, France, June 24-26, 2014: proceedings (Vol. II, pp. 430-431). (Lecture Notes in Computer Science; Vol. 8619). Springer. https://doi.org/10.1007/978-3-662-44196-1
  • Kröse, B. (2014). Analysis of Home Health Sensor Data. In J. van Hoof, G. Demiris, & E. J. M. Wouters (Eds.), Handbook of Smart Homes, Health Care and Well-Being Springer. https://doi.org/10.1007/978-3-319-01904-8_21-1, https://doi.org/10.1007/978-3-319-01583-5_21
  • Hu, N., Bormann, R., Zwölfer, T., & Kröse, B. (2014). Multi-user identification and efficient user approaching by fusing robot and ambient sensors. In 2014 IEEE International Conference on Robotics and Automation (ICRA 2014): Hong Kong, China, 31 May - 7 June 2014. Proceedings (pp. 5299-5306). IEEE. https://doi.org/10.1109/ICRA.2014.6907638
  • Hu, N., Englebienne, G., Lou, Z., & Kröse, B. (2014). Learning latent structure for activity recognition. In 2014 IEEE International Conference on Robotics and Automation (ICRA 2014): Hong Kong, China, 31 May - 7 June 2014. Proceedings (pp. 1048-1053). IEEE. https://doi.org/10.1109/ICRA.2014.6906983
  • Hu, N., Englebienne, G., & Kröse, B. (2014). A two-layered approach to recognize high-level human activities. In 2014 IEEE RO-MAN: the 23rd IEEE International Symposium on Robot and Human Interactive Communication : August 25-29, 2014 Edinburgh, Scotland, UK (pp. 243-248). IEEE. https://doi.org/10.1109/ROMAN.2014.6926260
  • Javier Ordoñez, F., Englebienne, G., de Toledo, P., van Kasteren, T., Sanchez, A., & Kröse, B. (2014). In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models. IEEE Pervasive Computing, 13(3), 67-75. https://doi.org/10.1109/MPRV.2014.52
  • Rieping, K., Englebienne, G., & Kröse, B. (2014). Behavior Analysis of Elderly using Topic Models. Pervasive and Mobile Computing, 15, 181-199. https://doi.org/10.1016/j.pmcj.2014.07.001
  • Robben, S., Pol, M., & Kröse, B. (2014). Longitudinal Ambient Sensor Monitoring for Functional Health Assessments: A Case Study. In UbiComp'14 adjunct: proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: September 13-17, 2014, Seattle, WA, USA (pp. 1209-1216). Association for Computing Machinery. https://doi.org/10.1145/2638728.2638812
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