The remote monitoring and management of Parkinson's Disease (PD) patients is still a challenge. Nowadays, there is no operationally and economically feasible system that can truly support PD patients at home. Wearable sensor systems and camera-based systems for health monitoring are an emerging trend and are expected to enable proactive personal health management. Long-term physical activity monitoring may provide valid information about disease progression, rehabilitation success, and effects of medical and surgical interventions in PD patients.
By using ad-hoc machine learning tools and hardware technologies, the monitoring of medical treatment will be improved, thus obtaining a better control with reduction of motor fluctuations, as well as quantify the risk of falls in patients suffering from PD. The goal of avoiding hospitalization and replace it with home monitoring is relevant because patients would increase their well-being with an optimized therapy, avoid hospitalization and keep their lifestyle. Actually, the National Health System would save a relevant amount of economic resources with relevant socio-economic advantages.
This project aims at developing an integrated data processing system that, on the basis of different biological/mechanical signals collected from the body and from the environment, will implement a decision support system for suited diagnostic or preventive actions. This will be obtained by using in a novel fashion different and complementary sensor technologies and suited data fusion procedures. A machine learning approach will be adopted for which the quality model is learnt from ground truth data, which represent activity samples at the best affordable quality. Although the use of a body area network of wearable sensing unit enables to investigate the dynamics of all the different body segments, this solution is not really unobtrusive for the user and it is difficult to implement such an approach in unsupervised settings.
Application of ICT and sensor technologies is an innovative healthcare strategy, which can help in managing specific diseases in all of those situations where conventional methods are poorly effective. There is a limited but growing evidence about older peoples, patients and other stakeholders moving towards the use of new technologies dealing with chronic diseases, in particular PD, and more generally in the promotion of active and independent living. What we do know is that PD patients are likely to embrace such technologies if they are congruent with their own beliefs, attitudes, lifestyle and aspirations and are designed in such a way as to be accessible to them. What we do not know is the nature of those beliefs, attitudes, lifestyles and aspirations that will specifically result in high uptake and adherence to any assistive technology intervention, nor what the optimum design parameters of the technologies are for them.
Patients who are not in the physical conditions for going frequently to the hospital for outpatient visits, patients living far from the hospitals, patients who need continuous monitoring. In this frame, the technologies adopted in the preset project, that is wearable devices and body sensor networks, are attracting most attention for the treatment of chronic diseases of elderly, who need monitoring of the symptoms and adjustment of the therapy. Actually, the clinical management of patients with advanced PD is rather challenging, as PD is characterized by fluctuations in the clinical status that may vary significantly within the same day and between different days.
The innovation of the present research is in the use of advanced medical techniques possibly helpful to achieve a long-term monitoring of patient's clinical conditions at home, automatically, objectively, continuously and precisely, which would gain tremendous advances in the management of PD patients in more advanced stages of the disease. Over recent years, novel techniques of movement analysis based on the use of wearable inertial biosensors appeared. However, current technologies based on inertial sensors are not able to discriminate passive and active movements, thus precluding clear identification of pathological muscle activation. Without any information on muscle activation, it is difficult to discriminate voluntary and involuntary movements that commonly lead to motor fluctuation. The progress in respect to the state-of-art solutions is the creation and validation of innovative wearable and wireless technology based on integration of surface electromyography, able to recognize specific voluntary and involuntary movements of the limbs, with inertial sensors, able to monitor gait and movement disorders.
The accuracy of wearable systems based on IMU sensors will be improved by completing the inertial measurements with the study of the muscle activity, so that the clinical condition states will be better classified. The cramps, muscle contractions, abnormalities in neuromuscular functions and a variety of symptoms related to dyskinesia in the progression of the PD will be studied objectively and quantitatively by capturing also the muscle activity by sEMG.
This innovative system will enable a progress for clinicians, who will be able to better assess the motor condition of PD patients and consequently to better manage motor symptoms in advanced PD. By using ad-hoc machine learning tools and hardware technologies, the monitoring of medical treatment will be improved, thus obtaining a better control with reduction of motor fluctuations, as well as quantify the risk of falls in patients suffering from PD. The automatic remote long-term monitoring of motor symptoms would also improve the therapeutic strategy and optimize drug therapy by reducing the overall load of L-Dopa/day.
The goal of avoiding hospitalization and replace it with home monitoring is relevant and is a progress because patients would increase their wellbeing with an optimized therapy, avoid hospitalization and keep their lifestyle. Actually, the National Health System would save a relevant amount of economic resources with relevant socio-economic advantages. Overall, the global social and health context is constantly evolving, with the general aging of the population and the consequent increase in the frequency of chronic diseases. Our society must find a trade-off between two concurrent but competing needs: on the one hand, the need to ensure wellbeing for an aging population, in which the incidence of diseases typical of the elderly, such as PD, has percentages always higher; on the other hand, there is a need to reduce healthcare costs. Telemedicine and e-health systems may be the solution. Being PD widespread over the age of 70, leading to chronic disability of movement, is one of the most representative examples of a disease in which remote monitoring can provide a significant social impact and valuable economic benefits.