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Abstract
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| Over the past decades various technologies, methodologies and systems have been proposed for the diagnosis and monitoring of the Parkinson?s disease (PD). Several studies investigated various parameters of the gait of PD patients. Others focused on the evaluation and quantification of the patients? motor status and various disease symptoms. In addition, there are several solutions based on the analysis of electromyographic signals, speech recognition and even hand writing examination. The work presented here is based on the development of an intelligent closed loop system that seamlessly integrates a wide range of wearable micro-sensors, constantly monitoring several motor signals of the patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients. The system compares the patient specific profile with his evaluation, checking whether his status is stable, improving or worsening. As result, the system allows the personalization of the timing and medication dosing of the treatment. The system has been tested with 112 patients in Navarra (Spain), Ioannina (Greece) and Modena (Italy). The results obtained for the bradykinesia, tremor and akinesia symptoms? assessment match the subjective medical evaluation, with an accuracy around 95%. These data are promising, since they suggest an overall valid closed loop system, able to detect PD symptoms based on motor signals and additional information, supporting the health professionals? activity and therefore leading to an improved quality of life of the patients. | |
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International
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Si |
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Congress
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XIX World Congress on Parkinson's Disease and Related Disorders |
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960 |
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Place
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Shanghai, China |
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Reviewers
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Si |
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ISBN/ISSN
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1353-8020 |
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Start Date
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11/12/2011 |
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End Date
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14/12/2011 |
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From page
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102 |
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To page
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102 |
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Abstracts of WFN XIX World Congress on Parkinson's Disease and Related Disorders |