Recognising user actions during cooking task (Cooking task dataset)
IMU Data : [research data]
Krüger, Frank
1980-
VerfasserIn
aut
Hein, Albert
1982-
VerfasserIn
aut
Yordanova, Kristina
1985-
VerfasserIn
aut
Kirste, Thomas
1962-
VerfasserIn
aut
software, multimedia
Computerdaten
Forschungsdaten
gw
2011
monographic
XA-DE
Rostock
University
publisher
2011
Rostock
University Library
distributor
eng
1 Online-Ressource
The dataset contains the data of acceleration sensors attached to a person during the execution of a kitchen task. It consists of 7 datasets that describe the execution of preparing and having a meal: preparing the ingredients, cooking, serving the meal, having a meal, cleaning the table, and washing the dishes. Each of these datasets consists of the raw acceleration and angular rates that were recorded with motion capturing system based on wearable inertial measurement units (IMUs).The aim of the experiment is to investigate the ability of activity recognition approaches to recognise fine-grained user activities based on acceleration data. The results from the dataset can be found in the PlosOne paper "Computational State Space Models for Activity and Intention Recognition. A Feasibility Study" by Krüger et al.<eng>
Frank Krüger, Albert Hein, Kristina Yordanova, Thomas Kirste
004
http://purl.uni-rostock.de/rosdok/id00000154
10.1371/journal.pone.0109381
Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, and Thomas Kirste. Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381, 11 2014
10.1016/j.ijar.2015.04.003
Martin Nyolt, Frank Krüger, Kristina Yordanova, Albert Hein, and Thomas Kirste. Marginal filtering in large state spaces. International Journal of Approximate Reasoning, 61:16–32, June 2015
10.5220/0005275204970502
Martin Nyolt, Kristina Yordanova, and Thomas Kirste. Checking models for activity recognition. In Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), pages 497–502, Lisbon, Portugal, January 2015
10.18453/rosdok_id00001381
Kristina Yordanova. Methods for Engineering Symbolic Human Behaviour Models for Activity Recognition. PhD thesis, Institute of Computer Science, Rostock, Germany, June 2014
10.13140/RG.2.1.3294.1521
Kristina Yordanova and Thomas Kirste. Towards systematic development of symbolic models for activity recognition in intelligent environments. In Proceedings of The 3rd Workshop on AI Problems and Approaches for Intelligent Environments held at ECAI 2014, Prague, Czech Republic, August 2014.
10.18453/rosdok_id00000154
1135716614
rda
DE-627
190925
1677725605
20251226T180531.0
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