Reference Data Sets
FootSLAM and Videos
Dr. Patrick Robertson
Dr. Michael Angermann
Dr. Korbinian Frank
Aug 3, 2012
María José Vera Nadales,
Current Developments section added.
Android application provided for free download in the Implementation section.
Implementation details added, open source OSGI bundle linked.
Porting to Android completed.
Results section and Inference Test added.
Movie for Ubicomp 2010 available here.
Slides and papers for ION-GNSS 2010 are available in the publications section.
This site is online.
Master thesis work completed and now available here.
Knowledge about the current motion related activity (e.g. Sitting
) of a person is information that is required or useful for a number of applications. Technical advances in recent years have reduced prices for sensors capable of providing the necessary motion and pose related
signals, in particular MEMS based inertial measurement units (IMUs). In addition to low cost sensors, unobtrusiveness is a requirement for an
activity recognition system: We achieve this by mounting one IMU on the belt of the user.
Our signal processing approach is a multi-tier one. We first compute features from the raw accelerations and turn rates
and use these for classification with Bayesian Network techniques trained from a semi naturalistic, labelled data set.
In the following, you will find the links to download our Java and Android implementations
These results stem from a four-fold cross-validation of classification with a grid based filter, using a BN with learnt parameters and structure and a manually defined hidden Markov model. Features are computed at 4 Hz,
with sliding windows and a recognition delay of 0.5 s taken into account. The data were recorded under semi-naturalistic conditions.
The setup installed at DLR (as shown in the video below
) is composed of a wired sensor and some computing unit, computing the Bayesian inference. The single components and versions are described shortly in the following:
The data set used to achieve our results was collected from 16 male and female subjects aged between 23 and 50 and anotated manually by an observer. In total it contains about 4.5 hours of annoted activities Sitting
We provide our labeled dataset - over 4 hours worth of recorded and labeled human activities - which we would like to share with the research community in a zip file about 300 MB here
The archive contains 'readme' files with details of the data. In case you have any questions, just email
You can follow the ELIB link to our internal electronic library where you can download the paper as PDF and retrieve citation information, e.g. for BibTeX. Additional papers are available on our pages on related topics (links at the top left of this page).
Frank, Korbinian (2011) Adaptive and Tractable Bayesian Context Inference for Resource Constrained Devices. PhD Thesis, October 2011. Department of Computing, Mathematics and Physics, Waterford Institute of Technology, Ireland. Supervised by Dr. Tom Pfeifer and Dr. Patrick Robertson. Examined by: Declon O'Sullivan, Trinity College Dublin. Publisher: multicon multimedia consulting, 22 Dec. 2011, Schöneiche, Germany. ISBN 978-3930736188.[bibtex]
Frank, Korbinian and Vera Nadales, María Josefa and Robertson, Patrick and Pfeifer, Tom (2010) Bayesian Recognition of Motion Related Activities with Inertial Sensors. 12th ACM International Conference on Ubiquitous Computing, 26 - 29 Sep. 2010, Copenhagen, Danmark. ISBN 978-1-4503-0283-8.[bibtex]
Frank, Korbinian and Vera Nadales, María Josefa
and Robertson, Patrick
and Angermann, Michael (2010)
Reliable Real-Time Recognition of motion related human activities
using MEMS inertial sensors. ION GNSS 2010, 21. Sep. - 24. Sep. 2010,
Portland, Oregon, USA.
This paper received
the best presentation award in its session at ION. Download paper:
Vera Nadales, Maria Josefa (2010) Recognition of Human Motion Related Activities from Sensors. Master's Thesis, University of Malaga, Escuela Técnica Superior de Ingeniería de Telecomunicación.
Frank, Korbinian and Roeckl, Matthias and Vera Nadales, Maria Josefa and Robertson, Patrick and Pfeifer, Tom (2010) Comparison of Exact Static and Dynamic Bayesian Context Inference Methods for Activity Recognition. In: Managing Ubiquitous Communications and Services. Seventh IEEE International Workshop, MUCS 2010, Mannheim, Germany, 29th March 2010. Proceedings (12), pp. 41-47. Schöneiche: multicon verlag. 7th International Workshop on Managing Ubiquitous Communications and Services (MUCS 2010) part of PerCom 2010, 29 Mar - 2 Apr 2010, Mannheim, Germany. ISBN 978-3-930736-15-7.[bibtex]
Currently we are further developping our work to be even less obtrusive. There are several steps:
- Exchanging the wired one with a wireless IMU. This needs calibration of that IMU and the creation of a Java driver module.
- Transfer of computation processes to the IMU internal CPU. Reduce sending rate and computational demands of smartphone app.