MoBio - A Mobile Application for Collecting Data from Sensors

Petr Ježek, Roman Mouček

Abstract

There are a lot of sensors for monitoring human health and/or fitness level on the market. They facilitate collection of data from the human body and advanced devices even facilitate data transfer to remote servers where the collected data are further processed. While health data, obtained e.g. from accelerometers or chest straps, are collected rather frequently, brain electrophysiology data, obtained from surface electrodes, are still collected relatively rarely. However, integration and correlation of brain signals with other sensory data would be very interesting for next research of physical and mental health. Although capturing brain signals in real environment still faces technological difficulties, current development of common infrastructure seems to be useful. Then this article deals with various architectures and data formats used for storage and transfer of sensory data and their possible integration with existing neuroinformatics approaches. As a solution we introduced a terminology describing data from a limited collection of sensors. The terminology is implemented in the odML format and integrated in a proof-of-concept Android application. Data transfer, storage and visualisation as well as integration with a remote neuroinformatics resource are presented.

References

  1. 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).
  2. eMarketer (2015). 2 billion consumers worldwide to get smart(phones) by 2016. http://www.emarketer.com/ Article/2-Billion-Consumers-WorldwideSmartphones-by-2016/1011694.
  3. Grewe, J., Wachtler, T., and Benda, J. (2011). A bottom-up approach to data annotation in neurophysiology. Frontiers in Neuroinformatics, 5(16).
  4. HDF5 Group (2013). Hierarchical data format. http:// www.hdfgroup.org/HDF5/.
  5. INCF (2013). International neuroinformatics coordinating facility. http://www.incf.org/.
  6. Jezek, P. and Moucek, R. (2012). System for eeg/erp data and metadata storage and management. Neural Network World, 22(3):277-290.
  7. Jezek, P. and Moucek, R. (2013). Eeg/erp portal for android platform. Frontiers in Neuroinformatics, (46).
  8. (2014a). Describing neurophysiology data and metadata with oen, the ontology for experimental neurophysiology. Frontiers in Neuroinformatics, (44).
  9. Le Franc, Y., Gonzalez, D., Mylyanyk, I., Grewe, J., Jezek, P., Moucek, R., and Wachtler, T. (2014b). Mobile metadata: bringing neuroinformatics tools to the bench. Frontiers in Neuroinformatics, (53).
  10. Lowe, S. and Laighin, G. (2012). The age of the virtual trainer. Procedia Engineering, 34:242 - 247.
  11. Moucek, R., Bruha, P., Jezek, P., Mautner, P., Novotny, J., Papez, V., Prokop, T., Rondík, T., S?tebeta?k, J., and Vareka, L. (2014). Software and hardware infrastructure for research in electrophysiology. Frontiers in Neuroinformatics, 8(20).
  12. Seeger, C., Buchmann, A., and Van Laerhoven, K. (2011). Wireless sensor networks in the wild: Three practical issues after a middleware deployment. In Proceedings of the 6th International Workshop on Middleware Tools, Services and Run-time Support for Networked Embedded Systems, MidSens 7811, pages 1:1-1:6, New York, NY, USA. ACM.
  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. Teeters, J., Harris, K., Millman, K., Olshausen, B., and Sommer, F. (2008). Data sharing for computational neuroscience. Neuroinformatics, 6(1):47-55.
  15. Teeters, J. L., Benda, J., Davison, A. P., Eglen, S., Gerhard, S., Gerkin, R. C., Grewe, J., Harris, K., Jackson, T., Moucek, R., Pr öpper, 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. 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).
Download


Paper Citation


in Harvard Style

Ježek P. and Mouček R. (2016). MoBio - A Mobile Application for Collecting Data from Sensors . In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016) ISBN 978-989-758-180-9, pages 115-121. DOI: 10.5220/0005896901150121


in Bibtex Style

@conference{ict4awe16,
author={Petr Ježek and Roman Mouček},
title={MoBio - A Mobile Application for Collecting Data from Sensors},
booktitle={Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)},
year={2016},
pages={115-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005896901150121},
isbn={978-989-758-180-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)
TI - MoBio - A Mobile Application for Collecting Data from Sensors
SN - 978-989-758-180-9
AU - Ježek P.
AU - Mouček R.
PY - 2016
SP - 115
EP - 121
DO - 10.5220/0005896901150121