This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design... Show More

This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health 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 starting point for identifying important non-functional requirements involved in mobile health provisioning that are fundamental for the successful implementation of mobile health services in real life contexts.

It is increasingly recognized that stress has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user... Show More

It is increasingly recognized that stress has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysiological parameter during daily life can be greatly helpful in stress evaluation. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors.In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed mobile platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality, offer interesting opportunities for innovative personal health-care solutions to stress.

It is a strange paradox that the public is talking about health technology but cares more about disease technology: people address chronic diseases, people want to change unhealthy behaviors, people... Show More

It is a strange paradox that the public is talking about health technology but cares more about disease technology: people address chronic diseases, people want to change unhealthy behaviors, people aim to help carers and nurses - but people hardly ever look at those who are and want to remain healthy. This is even stranger, as times of health outnumber periods of disease in most persons` lifetimes. Somewhat surprisingly, technology available today is not yet optimally suited to help staying healthy. The authors discuss challenges with respect to the adaption of health behavior models, long-term interaction, quality of data, design of devices, primary use of data, and life-long data. And the authors suggest understanding technical systems for wellbeing as navigational systems, guiding a person through life on a healthy path.

This paper presents a new adaptive m-learning model supporting collaborative and context-aware learning. A seamless integration between learners' location information and a set of associated... Show More

This paper presents a new adaptive m-learning model supporting collaborative and context-aware learning. A seamless integration between learners' location information and a set of associated learning context dimensions is used to facilitate the provision of pervasive and ubiquitous learning services. The new model adopts nearest search algorithm in order to group spatially related mobile learners, constructing learning-oriented virtual communities and achieving a collaborative learning experience. The presented model implements two virtual community construction modes, described as client-based and server-based collaboration modes. A preliminary evaluation methodology was conducted, measuring the successful implementation of the proposed new model, and confirming the establishment of the virtual community after considering a set of learning context dimensions; such as learning collaboration type, learning style and learners' location information. Results have confirmed that both collaboration modes were successful in establishing the virtual communities between mobile learners. However, the server-based mode was more scalable than client-based mode while handling the increased number of mobile peers, in which less response time was experienced and a smaller learning grid area was formed.

Since the introduction of iPhone in 2007, smartphones have become very popular (e.g., the number of worldwide smartphone sales has surpassed the number of PC sales in 2011). The feature of high... Show More

Since the introduction of iPhone in 2007, smartphones have become very popular (e.g., the number of worldwide smartphone sales has surpassed the number of PC sales in 2011). The feature of high mobility and small size of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even laptops. Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.