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We find that most of the recent works on this issue did not care so much about the information of mobile Routing in intermittently connected mobile ad hoc devices' carriers. This reduces the possibilities of networks (ICMAN) is a very challenging problem considering the users' social behavior, which is seen as because disconnections are prevalent and the lack of one of the most interesting parameters for mobile users knowledge about network dynamics hinders good [1,6]. In our approach, the carrier's information, called decision making. In ICMAN one of the most important node profile, plays an important role in predicting the decisions is how to choose the most suitable mobility of the nodes. We consider that people are not intermediate(s) to forward the message to the likely to move around randomly. Rather, they move in destination. We propose in this document a new a predictable fashion based on repeating behavioral algorithm (PROPICMAN) that, based on context patterns at different timescales (day, week, month). If a information, allows the sender to select the neighbor(s) node has visited a place several times before, it is such that the message has the highest probability to likely that it will visit this location again in the future. reach the destination.For example, because this person likes pasta, and the place is an Italian restaurant. Thus, relevant

Bipolar Disorder is a severe form of mental illness. It is characterized by alternated episodes of mania and depression, and it is treated typically with a combination of pharmacotherapy and psychotherapy. Recognizing early warning signs of upcoming phases of mania or depression would be of great help for a personalized medical treatment. Unfortunately, this is a difficult task to be performed for both patient and doctors. In this paper we present the MONARCA wearable system, which is meant for recognizing early warning signs and predict maniac or depressive episodes. The system is a smartphone-centred and minimally invasive wearable sensors network that is being developing in the framework of the MONARCA European project.

Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.

This paper presents the lessons learnt on the design, development and evaluation of a pervasive computing-based system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a set of relevant checklist items in the development of innovative solutions for mental health treatment and in a broader way for future research on personal health systems.