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Abstract

Abstract

Characteristic movements of human body parts ranging from eye twitches to limbs jerky movements have been used for decades by physicians as clinical indicators of certain neurological disorders. Through a multidisciplinary research approach, our team, composed of medical experts, signal-processing specialists, wireless sensing experts, and computer scientists, aims at developing a sophisticated framework for automatic characterization of certain clinical conditions via identification of a proposed unique sequence (a signature pattern) of limb movements in relation to other body parts.

We argue that a set of movement data collected from human subjects via strategically located movement sensors fused with other supporting data, such as gyroscopic movements and relative locations of sensors, can be processed by advanced intelligent signal processing techniques. Using medical expert systems fed with knowledge provided by the contributing medical experts this can be used to characterize and classify typical and atypical human movements. The collected data is then processed using machine learning algorithms which is trained to automatically detect and characterize a set of movement disorders and classify them into specific clinical diagnosis such as specific types of seizures. In particular, our work ambitiously aims at developing a prototype proof-of-concept seizure remote monitoring and detection system. This would demonstrate the applicability of our developed methodology in real-life scenarios, using commercial of-the-shelf wireless sensing platforms coupled to our intelligent expert-based signal-processing platform.

We believe that the outcomes of this applied research will pave the roads for new methods in clinical diagnosis of various neurological diseases and monitoring progress and outcome of treatment that will, in turn, reduce human suffering and medical costs. Further, when coupled with our wireless technology and positioning methods, DC-MOVE can initiate or trigger an alerting response that could be life-saving.

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/content/papers/10.5339/qfarf.2010.BMP16
2010-12-13
2020-01-19
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References

  1. T. Khattab, A. Mohamed, K. Shaban, B. Uthman, L. Streletz, A. Abu-Dayya, Detection and classification of human movement (DC-MOVE), QFARF Proceedings, 2010, BMP16.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2010.BMP16
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