resarch data

Krüger,  Frank ;  Hein,  Albert ;  Yordanova,  Kristina ;  Kirste,  Thomas

Recognising user actions during cooking task (Cooking task dataset) – IMU Data

Rostock :  Universität Rostock ,  2017

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.

DOI:  10.18453/rosdok_id00000154


25.011 MB
MD5: 372ff318c83228534b4e0bdb3475d720
1.055 MB
MD5: 8a2648d2e2fb512f497108bf6c08bfb8

Creative Commons Lizenzvertrag
Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz.

Jahr der Erstellung:2011
Lokaler Identifikator:D2011-KTA-KHY
Schlagworte:activity recognition
kitchen task assessment
cooking task

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.

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.

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.

Kristina Yordanova. Methods for Engineering Symbolic Human Behaviour Models for Activity Recognition. PhD thesis, Institute of Computer Science, Rostock, Germany, June 2014.
URN: urn:nbn:de:gbv:28-diss2014-0133-5

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.

DNB-Sachgruppe:004 Informatik
Fakultät:Fakultät für Informatik und Elektrotechnik

Persistente URL:http://purl.uni-rostock.de/rosdok/id00000154
erstellt am:2017-10-18
zuletzt geändert am:2018-01-10
Die UB Rostock stellt die Metadaten zu diesem Dokument unter Public Domain und verzichtet damit weltweit auf alle urheberrechtlichen und verwandten Schutzrechte.