Innovation 2017-11-20T23:54:20+00:00

Research & Innovation

Qolware is constantly pushing the boundary of technological possibilities to improve your quality of life.

Validating detection of epileptic seizures under real world conditions

At Qolware we are constantly working with academic, industrial and clinical partners to create solutions that improve patients’ quality of life. At the moment, we are conducting a pilot project with the Epilepsy Centre of the Ludwig Maximilians University of Munich (LMU) to provide epileptic patients with state-of-the-art technology that helps them monitoring their therapy and reaching help automatically in case of emergency.

Detecting falls automatically

At Qolware we have more than 8 years of experience in algorithm development using sensor data, with particular focus on fall detection and human movement analysis. During the last years we have been working continuously to improve the detection accuracy of our algorithms and validate them using real data. Latest advances in wearable technology allowed us to go a step further in the development of smarter, robuster and more efficient algorithms. The latest version of our fall detector uses sensor fusion to collect data from the environment and react according to it, detecting when someone falls and summon help automatically if needed.

Relevant references

  1. Integrated Accelerometry-Based System for Functional Mobility Assessment and Fall Detection. Cristina Soaz Gonzalez. Doctoral dissertation. Institute for Data Processing. Technical University of Munich. April, 2015.
  2. Step Detection and Parameterization for Gait Assessment Using a Single Waist-Worn Accelerometer. Soaz, C.; Diepold, K. Transactions on Biomedical Engineering, IEEE Year: 2015, Volume: PP, Issue: 99 Pages: 1 - 1, DOI: 10.1109/TBME.2015.2480296
  3. A new method to estimate the real upper limit of the false alarm rate in a 3D accelerometry-based fall detector for the elderly. C. Soaz, C. Lederer, M. Daumer. In the Proceedings of the 34th IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBS). San Diego, California, USA. August 2012. DOI: 10.1109/EMBC.2012.6345915
  4. Towards an integrated 3D accelerometry platform to assess the risk of falling: a feasibility study. Logistic data management and data extraction in an European multi-center setting. Soaz C., Daumer M. for the VPHOP Consortium. Abstract. In the XII International Symposium on 3D Analysis of Human Movement Technology & Treatment. Bologna, Italy, July 2012.
  5. Progressive adaptation in physical activity and neuromuscular performance during 520d confinement. D.L. Belavý, U. Gast, M. Daumer, E. Fomina, R. Rawer ,H. Schießl, S. Schneider, H. Schubert, C. Soaz, D. Felsenberg. Year 2013 PLoS ONE 8(3): e60090. doi: 10.1371/journal.pone.0060090
  6. Accuracy of the actibelt® accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis. Robert W. Motl , Madeline Weikert, Yoojin Suh, Jacob J. Sosnoff, John Pula, Cristina Soaz, Michaela Schimpl, Christian Lederer, Martin Daumer. In Gait &Posture, Volume 35, Issue 2, February 2012.
  7. Concept and prototypical realisation of an accelerometry-based integrated fall detection and prevention system for the Elderly. Daumer M., Schimpl M., Soaz C., Neuhaus A. In 2nd International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM), Glasgow, Scotland, May 2011.