Ricerc@Sapienza

Patients affected by chronic diseases need frequent outpatient visits for assessing the disease and symptoms evolution and the therapy. However, having a complete medical case is not such a trivial task since it is often based on the patient's subjective feelings, making a focused treatment not feasible. Using remote long-time monitoring in domestic environment can represent an efficient and valid alternative to patient commuting and hospitalization. In this frame, wearable sensor systems allow the implementation of telemedicine strategies with the aim of providing an objective and complete overview of the patient's health by recording specific physical and physiological parameters associated to the disease during the whole day and in free-living-conditions. In Parkinson's Disease (PD), an uncertain comprehension of patient's condition can lead to a wrong medicine dose and to complications which can determine the manifestation of several disturbs, such as the Freezing of Gait (FOG). FOG is a very common and risky symptom of PD, which often leads to catastrophic injuries and falls. Starting from the scientific collaboration between the research group of Prof. Irrera and some Neurologists of the Sapienza Department of Human Neuroscience on the specific topic of the automatic recognition of FOG, We propose here to use a wearable system including inertial sensors and surface Electromyography (sEMG) to predict the FOG occurrence in order to alert the patient and prevent falls. The challenge is to find a specific correlation between characteristic patterns of the leg muscle action potentials and inertial signals which are typical of a condition immediately preceding the FOG manifestation. The entire analysis will be performed in a low-power and stand-alone wearable system, able to monitor the patient in a free-living environment. The achievement of this target would be an important step toward the reduction of the patient fall risk and the improvement of the life quality.