Quantification system of Parkinson’s disease

Technological advances in signal processing, electronics, embedded systems and neuroscience have allowed the design of devices that help physicians to better assess the evolution of neurological diseases. In this context, we are interested in the development of an intelligent system for the quantification of Parkinson’s disease (PD). In order to achieve this, the system contains two parts: a wireless sensor network and an embedded system. The wireless sensor network is used to measure motor defects of the patient; it is constituted of several nodes which communicate among themselves. These nodes are intelligent sensors; they contains accelerometers, EMG and blood pressure sensors to detect any malfunction of the patient’s motor activities. As regards to the embedded system, it allows analyzing the patient’s voice signal in order to extract a descriptor that characterizes PD. The network detects the patient’s posture and measures his or her tremors. The voice analysis system measures the degradation of the patient’s condition. The embedded system combines the three decisions using the Chair–Varshney rule. The data fusion between the sensor network and the embedded system, will quantify the disease to facilitate the diagnostic for the physician, while providing the ability to effectively assess the evolution of the patient’s health.