Data Format for Storing ANT+ Sensors Data

Petr Ježek, Roman Mouček


Medical treatment of sudden and especially chronic diseases has become more expensive. People suffering from a variety of diseases had been traditionally treated in hospitals for a long time. Fortunately, the current situation has been changing also thanks to relatively cheap body sensors and development of systems for home treatment. It brings inconsiderable cost savings and improves patients’ comfort. On the other hand, it puts demands on the used technical infrastructure and home treatment system developers who must solve integration of different systems. A crucial point is a definition of unified data formats facilitating transfer and storage of data to/in remote databases. There are standards and APIs such as Zigbee, Bluetooth low energy or ANT+ that define a protocol for data transfer. However, they do not define a suitable format for long term data storing. In this paper, data coming from ANT+ sensors have been studied and metadata related to all kinds of body sensors and raw data and metadata specific to individual sensors have been defined. Then a framework organizing data and metadata obtained from ANT+ sensors into an open and general data format suitable for long term storage of sensor data is introduced. Finally, a sample use-case showing the transfer of data from a sensor into a data storage is presented.


  1. Bjaalie, J. G. and Grillner, S. (2007). Global neuroinformatics: the International Neuroinformatics Coordinating Facility. J Neurosci, 27(14):3613-5.
  2. Bui, A. L., Horwich, T. B., and Fonarow, G. C. (2011). Epidemiology and risk profile of heart failure. Nature Reviews Cardiology, 8(1):30-41.
  3. Davison, A. P., Brizzi, T., Guarino, D., Manette, O. F., Monier, C., Sadoc, G., and Frégnac, Y. (2013). Helmholtz: a customizable framework for neurophysiology data management. Frontiers in Neuroinformatics, (25).
  4. Farahani, S. (2008). ZigBee Wireless Networks and Transceivers. Newnes, Newton, MA, USA.
  5. Grewe, J., Wachtler, T., and Benda, J. (2011). A bottom-up approach to data annotation in neurophysiology. Frontiers in Neuroinformatics, 5(16). Group (2013). Hierarchical data format.
  7. Heydon, R. (2012). Bluetooth low energy: the developer's handbook. Prentice Hall.
  8. Innovations, D. (2013). Ant message protocol and usage.
  9. Kyriacou, E., Pattichis, C., and Pattichis, M. (2009). An overview of recent health care support systems for eemergency and mhealth applications. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 1246-1249.
  10. Le Franc, Y., Gonzalez, D., Mylyanyk, I., Grewe, J., Jezek, P., Moucek, R., and Wachtler, T. (2014). Mobile metadata: bringing neuroinformatics tools to the bench. Frontiers in Neuroinformatics, (53).
  11. Mehmood, N. Q., Culmone, R., and Mostarda, L. (2014). An ontology driven software framework for the healthcare applications based on ant+ protocol. In Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on, pages 245-250. IEEE.
  12. Sheth, A., Henson, C., and Sahoo, S. (2008). Semantic sensor web. Internet Computing, IEEE, 12(4):78-83.
  13. Stoewer, A., Kellner, C., Benda, J., Wachtler, T., and Grewe, J. (2014). File format and library for neuroscience data and metadata. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014.
  14. Surie, D., Laguionie, O., and Pederson, T. (2008). Wireless sensor networking of everyday objects in a smart home environment. In Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. International Conference on, pages 189-194.
  15. Teeters, J. L., Benda, J., Davison, A. P., Eglen, S., Gerhard, S., Gerkin, R. C., Grewe, J., Harris, K., Jackson, T., Moucek, R., Pröpper, R., Sessions, H. L., Smith, L. S., Sobolev, A., Sommer, F. T., Stoewer, A., and Wachtler, T. (2013). Considerations for developing a standard for storing electrophysiology data in hdf5. Frontiers in Neuroinformatics, (69).
  16. Yuriyama, M. and Kushida, T. (2010). Sensor-cloud infrastructure - physical sensor management with virtualized sensors on cloud computing. In Network-Based Information Systems (NBiS), 2010 13th International Conference on, pages 1-8.
  17. Zaloker, J. (2014). Ant/ant+. Arrow M2M representative.
  18. Zehl, L., Denker, M., Stoewer, A., Jaillet, F., Brochier, T., Riehle, A., Wachtler, T., and Grün, S. (2014). Handling complex metadata in neurophysiological experiments. Frontiers in Neuroinformatics, (29).

Paper Citation

in Harvard Style

Ježek P. and Mouček R. (2017). Data Format for Storing ANT+ Sensors Data . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 396-400. DOI: 10.5220/0006229103960400

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Data Format for Storing ANT+ Sensors Data
SN - 978-989-758-213-4
AU - Ježek P.
AU - Mouček R.
PY - 2017
SP - 396
EP - 400
DO - 10.5220/0006229103960400

in Bibtex Style

author={Petr Ježek and Roman Mouček},
title={Data Format for Storing ANT+ Sensors Data},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},