Open Topics

Integrated accelerometry-based Fall Detection System

Background

Falls have an increasing social and economic impact,specially in the developed countries, as life expectancyof our elderly rises dramatically. Fall-related injuriesincreases morbidity, mortality and premature use of healthcare services reducing also the time the elderly can liveautonomously at their own homes. Fall detection systemsmay notify emergency responders when no one apart fromthe injured is present. However, their real-world applicationis limited by a number of factors such as high false positiverates, low-compliance, poor usability and short batterylifetime. In order to improve these aspects we developed afall detection algorithm with a low false alarm rate to beused in a miniaturized 3D accelerometer integrated in a beltbuckle - the actibelt® - developed at the Sylvia LawryCenter for Multiple Sclerosis Research. The following thesisconsist of implementing the fall detection algorithm in themicrocontroller of the accelerometer and programming anAndroid-based application in order to link the actibelt®to a smart phone for fall detection management and notification.

Master thesis 1:

Customized Android Interface for a fall detection system basedon a smart phone and a wireless acceleration sensor- the actibelt®BLU

Tasks:

Prerequisites:

good knowledge of programming (preferably Android)

Master thesis / Bachelor thesis 2:

Real-time implementation of an accelerometry-based fall detectionalgorithm on the MSP430 microcontroller of the actibelt®BLU

Tasks:

Prerequisites:

Good knowledge of C

References:

[1] M. Daumer, K. Thaler, E. Kruis, W. Feneberg, G. Staude,and M. Scholz. Steps towards a miniaturized, robust and autonomousmeasurement device for the long-term monitoring of the activity ofpatients - ActiBelt. Biomedizinische Technik/Biomedical Engineering.2007; 52: 149-155.[2] A new method to estimate the real upper limit of the false alarmrate in a 3D accelerometry-based fall detector for the elderly.C. Soaz, C. Lederer, M. Daumer. In the Proceedings of the 34th IEEEAnnual International Conference of the Engineering in Medicine andBiology Society (EMBS). San Diego, California, USA. August 2012.DOI: 10.1109/EMBC.2012.6345915

Supervisors:

Cristina Soaz, M.Eng.Lehrstuhl für Datenverarbeitung (Prof. Diepold)E-Mail: cristina.soaz@tum.de , Tel.: +49 (0)89 289 23608

M.Sc. Munish Jassi,Chair of Electronic Design Automation (Prof. Schlichtmann)E-Mail: munish.jassi@mytum.de, Tel.: +49 (89) 289 – 23651

External supervisor:

Dr. Martin DaumerScientific Director of Sylvia Lawry Centre- The Human Motion InstituteE-Mail: daumer@slcmsr.org,Tel.: +49 89 2060 269 50

Dr.-Ing. Dmitry ZhelondzUniversität der Bundeswehr München

Technology provider:

Trium Analysis Online GmbH

Advisor:

Prof. Dr.-Ing. Klaus DiepoldLehrstuhl für Datenverarbeitung

Finished Theses