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.

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.

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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.

The 21 st-century data-driven economy is rapidly evolving and large companies like Telecom operators are forced to adapt their business. They are shifting their focus from traditional but exhausted connectivity provider market towards a more services based market. Here competition is high, and other stakeholders are trying to monopolize the data-driven world of personalized services. But, Telecom operators are the custodians of Call Detail Records (CDRs), which captures mobility activities and social ties of a large number of users. Recently researchers observed that CDRs are the most valuable form of data to perform user-centric analysis, especially when related to mobility and habits. In this paper, we demonstrate that CDRs can be used to provide personalized and timely services. Specifically, we show that it can be used to provide a recommendation service, one of the most popular personalized services. In addition, we demonstrate the advantage of leveraging human behavior characteristics for such services. Our REGULA recommendation algorithm, that builds on the analysis of human habits, outperforms the state of the art recommendation algorithms. We advocate that Telecom operators can leverage CDRs to provide personalized services in a data-driven world and can significantly alter the landscape of timely and personalized services.