Feature space analysis for human activity recognition in smart environments

Conference poster

Chinellato, E., Hogg, D. and Cohn, A. 2016. Feature space analysis for human activity recognition in smart environments. 12th International Conference on Intelligent Environments (IE). London, United Kingdom 14 - 16 Sep 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 194-197 https://doi.org/10.1109/IE.2016.43
TypeConference poster
TitleFeature space analysis for human activity recognition in smart environments
AuthorsChinellato, E., Hogg, D. and Cohn, A.
Abstract

Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets.

Conference12th International Conference on Intelligent Environments (IE)
Page range194-197
ISSN2472-7571
ISBN
Hardcover9781509040568
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print14 Sep 2016
Online27 Oct 2016
Publication process dates
Deposited08 Mar 2018
Accepted15 Jul 2016
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/IE.2016.43
LanguageEnglish
Book title2016 12th International Conference on Intelligent Environments (IE)
Permalink -

https://repository.mdx.ac.uk/item/87893

Log in to edit

Download files

Accepted author manuscript
IE2016v2.pdf

  • 47
    total views
  • 12
    total downloads
  • 2
    views this month
  • 2
    downloads this month

Export as

Related outputs

Affective visuomotor interaction: a functional model for socially competent robot grasping
Chinellato, E., Ferretti, G. and Irving, L. 2019. Affective visuomotor interaction: a functional model for socially competent robot grasping. Martinez-Hernandez, U., Vouloutsi, V., Mura, A., Mangan, M., Minoru, A., Prescott, T. and Verschure, P. (ed.) 8th International Conference, Living Machines 2019. Nara, Japan 09 - 12 Jul 2019 Springer, Cham. pp. 51-62 https://doi.org/10.1007/978-3-030-24741-6_5
The competitive and multi-faceted nature of neural coding in motor imagery: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by V. Mohan et al.
Chinellato, E. 2019. The competitive and multi-faceted nature of neural coding in motor imagery: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by V. Mohan et al. Physics of life reviews. https://doi.org/10.1016/j.plrev.2019.02.003
Advances in human-computer interactions: methods, algorithms, and applications
Solari, F., Chessa, M., Chinellato, E. and Bresciani, J. 2018. Advances in human-computer interactions: methods, algorithms, and applications. Computational Intelligence and Neuroscience. 2018. https://doi.org/10.1155/2018/4127475
Sensorial computing
Varsani, P., Moseley, R., Jones, S., James-Reynolds, C., Chinellato, E. and Augusto, J. 2018. Sensorial computing. in: Filimowicz, M. and Tzankova, V. (ed.) New Directions in Third Wave Human-Computer Interaction: Volume 1 - Technologies Cham, Switzerland Springer. pp. 265-284
The STRANDS project: long-term autonomy in everyday environments
Hawes, N., Burbridge, C., Jovan, F., Kunze, L., Lacerda, B., Mudrova, L., Young, J., Wyatt, J., Hebesberger, D., Kortner, T., Ambrus, R., Bore, N., Folkesson, J., Jensfelt, P., Beyer, L., Hermans, A., Leibe, B., Aldoma, A., Faulhammer, T., Zillich, M., Vincze, M., Chinellato, E., Al-Omari, M., Duckworth, P., Gatsoulis, Y., Hogg, D., Cohn, A., Dondrup, C., Pulido Fentanes, J., Krajnik, T., Santos, J., Duckett, T. and Hanheide, M. 2017. The STRANDS project: long-term autonomy in everyday environments. IEEE Robotics & Automation Magazine. 24 (3), pp. 146-156. https://doi.org/10.1109/MRA.2016.2636359
Decoding information for grasping from the macaque dorsomedial visual stream
Filippini, M., Breveglieri, R., Akhras, M., Bosco, A., Chinellato, E. and Fattori, P. 2017. Decoding information for grasping from the macaque dorsomedial visual stream. The Journal of Neuroscience. 37 (16), pp. 4311-4322. https://doi.org/10.1523/JNEUROSCI.3077-16.2017
An incremental von mises mixture framework for modelling human activity streaming data
Chinellato, E., Mardia, K., Hogg, D. and Cohn, A. 2017. An incremental von mises mixture framework for modelling human activity streaming data. International Work-Conference on Time Series Analysis (ITISE 2017). Granada, Spain 18 - 20 Sep 2017 pp. 379-389
Adaptive saccade controller inspired by the primates' cerebellum
Antonelli, M., Duran, A., Chinellato, E. and Del Pobil, A. 2015. Adaptive saccade controller inspired by the primates' cerebellum. IEEE International Conference on Robotics and Automation (ICRA). Seattle, Washington, USA 26 - 30 May 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 5048-5053 https://doi.org/10.1109/ICRA.2015.7139901
Motor interference in interactive contexts
Chinellato, E., Castiello, U. and Sartori, L. 2015. Motor interference in interactive contexts. Frontiers in Psychology. 6. https://doi.org/10.3389/fpsyg.2015.00791
The visual neuroscience of robotic grasping: achieving sensorimotor skills through dorsal-ventral stream integration
Chinellato, E. and Del Pobil, A. 2016. The visual neuroscience of robotic grasping: achieving sensorimotor skills through dorsal-ventral stream integration. Springer.
Unsupervised grounding of textual descriptions of object features and actions in video
Alomari, M., Chinellato, E., Gatsoulis, Y., Hogg, D. and Cohn, A. 2016. Unsupervised grounding of textual descriptions of object features and actions in video. 15th International Conference Principles of Knowledge Representation and Reasoning (KR 2016). Cape Town, South Africa 25 - 29 Apr 2016 Association for the Advancement of Artificial Intelligence (AAAI). pp. 505-508
Learning the visual–oculomotor transformation: effects on saccade control and space representation
Antonelli, M., Duran, A., Chinellato, E. and Del Pobil, A. 2015. Learning the visual–oculomotor transformation: effects on saccade control and space representation. Robotics and Autonomous Systems. 71, pp. 13-22. https://doi.org/10.1016/j.robot.2014.11.018
The multiform motor cortical output: kinematic, predictive and response coding
Sartori, L., Betti, S., Chinellato, E. and Castiello, U. 2015. The multiform motor cortical output: kinematic, predictive and response coding. Cortex. 70, pp. 169-178. https://doi.org/10.1016/j.cortex.2015.01.019
A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot
Antonelli, M., Gibaldi, A., Beuth, F., Duran, A., Canessa, A., Chessa, M., Solari, F., Del Pobil, A., Hamker, F., Chinellato, E. and Sabatini, S. 2014. A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot. IEEE Transactions on Autonomous Mental Development. 6 (4), pp. 259-273. https://doi.org/10.1109/TAMD.2014.2332875