Remote Assessment of Disease and Relapse – Central Nervous System- RADAR-CNS

Authors

  • J.M. Garzón-Rey Department of Microelectronics and Electronic Systems, Universidad Autónoma de Barcelona, Barcelona, Spain Biomonitoring Group, Biomedical Research Networking Center (CIBER-BBN), Spain
  • J. Aguilo Department of Microelectronics and Electronic Systems, Universidad Autónoma de Barcelona, Barcelona, Spain Biomonitoring Group, Biomedical Research Networking Center (CIBER-BBN), Spain

DOI:

https://doi.org/10.14738/tmlai.54.3293

Keywords:

Central Nervous System Disorders, Depression, Multiple sclerosis and epilepsy

Abstract

Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) is a major international research project. It aims to develop new ways of measuring major depressive disorder, epilepsy and multiple sclerosis (MS) using wearable devices and smartphone technology. RADAR-CNS aims to improve people’s quality of life and change how depression, epilepsy and MS are managed and treated. Data from mobile devices can give a full picture of a person’s condition at a level of detail which was previously impossible. This offers the potential to detect changes in behavior, sleep, or mood before the individual themselves is aware of it. This could help them to predict or even avoid a relapse. To achieve this, we are creating a pipeline for developing, testing and implementing remote measurement technologies for depression, multiple sclerosis (MS) and epilepsy.  Depression, multiple sclerosis and epilepsy are all disorders of the central nervous system. While the symptoms and disability experienced by individuals with each condition are different, they all have a significant effect on people’s wellbeing. For doctors and people with these long-term or chronic conditions, understanding how their disease changes over time can help with its management. But in chronic conditions most of the symptoms and episodes happen outside of the health care environment. Measuring individuals’ symptoms, mood and daily function continuously, could help people gain better insight into their condition. RADAR-CNS receives funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA.

 

References

(1) H. U. Wittchen, F. Jacobi, J. Rehm, A. Gustavsson, M. Svensson, B. Jönsson, J. Olesen, C. Allgulander, J. Alonso, C. Faravelli, L. Fratiglioni, P. Jennum, R. Lieb, A. Maercker, J. van Os, M. Preisig, L. Salvador-Carulla, R. Simon, and H.-C. Steinhausen, “The size and burden of mental disorders and other disorders of the brain in Europe 2010,” Eur. Neuropsychopharmacol., vol. 21, no. 9, pp. 655–679, 2011.

(2) “MS Info, Research, News and Support | Multiple Sclerosis Society UK.” [Online]. Available: https://www.mssociety.org.uk/. [Accessed: 03-Apr-2017].

(3) “Home : National Multiple Sclerosis Society.” [Online]. Available: http://www.nationalmssociety.org/. [Accessed: 04-Mar-2017].

(4) “Epilepsy Action | Epilepsy information and advice.” [Online]. Available: https://www.epilepsy.org.uk/. [Accessed: 04-Mar-2017].

(5) “Epilepsy Information, Support & Research | Epilepsy Society.” [Online]. Available: https://www.epilepsysociety.org.uk/. [Accessed: 04-Mar-2017].

(6) J. P. Piccini, D. N. Simon, B. A. Steinberg, L. Thomas, L. A. Allen, G. C. Fonarow, B. Gersh, E. Hylek, P. R. Kowey, J. A. Reiffel, G. V. Naccarelli, P. S. Chan, J. A. Spertus, and E. D. Peterson, “Differences in Clinical and Functional Outcomes of Atrial Fibrillation in Women and Men,” JAMA Cardiol., vol. 1, no. 3, p. 282, Jun. 2016.

(7) “RADAR-CNS | RADAR-CNS.” [Online]. Available: http://www.radar-cns.org/. [Accessed: 04-Mar-2017].

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Published

2017-09-01

How to Cite

Garzón-Rey, J., & Aguilo, J. (2017). Remote Assessment of Disease and Relapse – Central Nervous System- RADAR-CNS. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.3293

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems