Implementing real-time step detection algorithm in EyesWeb environment
Monitoring the gait of Parkinson's patients
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Many methods are used for measuring and defining the presence of Parkinson's disease. One important aspect of the disease is gait disturbance, which can be discovered in the early stages of the disease. This disturbance can be measured using three dimensional accelerometers, which are present in the Shimmer device. These devices are light weighted wireless sensor nodes which can be easily mounted on the patient's limbs. Data captured by the device can be streamed to a computer for real-time processing. The Matlab algorithms for step detection are converted to C++ code so they can be used in th...
Many methods are used for measuring and defining the presence of Parkinson's disease. One important aspect of the disease is gait disturbance, which can be discovered in the early stages of the disease. This disturbance can be measured using three dimensional accelerometers, which are present in the Shimmer device. These devices are light weighted wireless sensor nodes which can be easily mounted on the patient's limbs. Data captured by the device can be streamed to a computer for real-time processing. The Matlab algorithms for step detection are converted to C++ code so they can be used in the EyesWeb environment. Whereas Matlab code can only serve post-processing, the Shimmer's Bluetooth datastream can now be processed in real-time, since the EyesWeb environment supports real-time execution. Hence the Visual Studio environment is needed to design the algorithms into C++ based EyesWeb blocks. This real-time detection algorithm can be applied easily in small and inexpensive lab environments to calculate and obtain the Parkinson's disease severity parameters. Hereby conclusions can be made to prevent the progress of the disease and to treat Parkinson's disorder in early stages.
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