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In European Union, between 0.2 and 2.6% of the population suffer from psychotic disorders (Wittchen et al., 2011). The largest group is patients with schizophrenia, and around 40% can be considered resistant to pharmacological treatment (Essock et al., 1996; Kennedy et al., 2014). Standard intervention in patients with treatment-resistant schizophrenia (TRS) is difficult due to the persistence of positive symptoms, extensive periods of hospitalization, and higher risk of comorbidities. The full scope of this scenario generates a financial burden on society because of frequent emergency visits, hospitalizations and chronic use of polytherapy (de Silva et al., 2012), as well as a huge impact in the humanistic burden. This concerns patients and caregivers, and involves several dimensions such as quality of life, treatment side effects, caregiver burden, social impairment, suicide, violence, and healthy lifestyle among the most important (Millier et al., 2014 ) In this sense, we have developed the m-RESIST, an intervention programme based on m-Health that offer to TRS patients an holistic approach to: i) integrate psychiatric and psychosocial assistance with other medical health-carers; ii) better monitorization of symptoms through a personalised and optimised therapeutic process; iii) promote acceptance and self-management of disease and co-morbidities; and iv) potentiate involvement of patients and their caregivers in the therapeutic process. Moreover, there are no studies in TRS patients that collect real-time information from patients that allow monitoring, evaluation and intervention in the main areas of the disease, as well as in situations of risk. In this regard, m-RESIST brings an excellent opportunity to access this information, as well as permit an immediate and tailored intervention. This program will have integrated a mobile application that will deliver three possible intervention packages: treatment adherence, healthy lifestyle and symptoms management that include training strategies based on cognitive behaviour therapy for psychosis. Clinical algorithms have been created for each intervention package in order to deliver tailored recommendations to patients, caregivers and clinicians, which will be based on clinical and real-time sensor data. Moreover, a risk level ranking system will be used in order to prioritize actions in risk situations. On-going challenges include a pilot test to assess acceptability, usability and satisfaction of patients, caregivers and clinicians with the m-RESIST intervention program. m-RESIST project is founded by Horizon 2020 Framework Programme of the European Union. PHC26 2014 m-RESIST “Mobile Therapeutic Attention for patients with Treatment Resistant Schizophrenia”. The authors listed in this abstract are only a part of the m-RESIST Group.

Providing indigenous communities with ICT tools and methods for collecting and sharing their Traditional Ecological Knowledge is increasingly recognised as an avenue for improvements in environmental governance and social-environmental justice. In this paper we show how we carried out a usability engineering effort in the "wild" context of the Congolese rainforest -- designing, evaluating and iteratively improving novel collaborative data collection interfaces for non-literate forest communities that can subsequently be used to facilitate communication and information sharing with logging companies. Working in this context necessitates adopting a thoroughly flexible approach to the design, development, introduction and evaluation of technology and the modes of interaction it offers. We show that we have improved participant accuracy from about 75% towards 95% and provide a set of guidelines for designing and evaluating ICT solutions in "extreme circumstances" -- which hold lessons for CSCW, HCI and ICT4D practitioners dealing with similar challenges.

University College London's Extreme Citizen Science research group (UCL ExCiteS) is experimenting with ways to incorporate the most marginalized communities into participatory citizen science activities through which they can share their indigenous knowledge. The group works with communities at the extremes of the globalized world--both because of nonliteracy and the remote or forbidding environments they inhabit. These groups are the gatekeepers of some key environments on which the future health of the planet depend--from tropical forests to Arctic sea-ice. This article presents the methodologies and tools the group is developing to give these people a voice. This article is part of a special issue on pervasive analytics and citizen science.

Recent advances in sensing technologies are leading to the development of miniaturised sensors that could be used as stand-alone devices, connected to smartphones or even embedded in smartphones. These sensors and apps create opportunities for more detailed environmental monitoring, as compared to official monitoring networks, and to involve the general public in environmental monitoring through participatory data collection and monitoring schemes. However, depending on the aspects of the environment that are monitored, technical complexity can differ quite a lot. Proper monitoring often requires important efforts in developing and validating sensing devices and in processing the collected data. This chapter deals with low-cost sensing devices and smartphone applications for physico-chemical environmental parameters, that can be used, possibly with some training, by non-specialised people, and as such create new opportunities for collection of novel data and improved monitoring of the environment. It starts with examples of novel sensing devices and apps for different environmental domains, and proceeds with a detailed overview of the possible added value, the technical challenges and future prospects in two specific domains that recently received a lot of interest, air quality and sound monitoring.

With this poster we announce the imminent release of Sapelli, a new mobile data collection and sharing platform designed with a particular focus on non-literate and illiterate users.

Participatory sensing enables a person-centric collection of environmental measurement data with, in principle, high granularity in space and time. In this paper we provide concrete proof that participatory techniques, when implemented properly, can achieve the same accuracy as standard noise mapping techniques. We do this through a citizen science experiment for noise mapping a 1 km2 area in the city of Antwerp using NoiseTube, a participatory sensing framework for monitoring ambient noise. At the technical side, we set up measuring equipment in accordance with official norms insofar as they apply, also carrying out extensive calibration experiments. At the citizen side, we collaborated with up to 13 volunteers from a citizen-led Antwerp-based action group. From the data gathered we construct purely measurement-based noise maps of the target area with error margins of about 5dB, comparable to those of official simulation-based noise maps. We also report on a survey evaluating NoiseTube, as a system for participative grassroots noise mapping campaigns, from the user perspective.

This paper describes a project initiated by non-literate indigenous people to equip their own "citizen scientists" with rugged smartphones running adapted software that enable them to share some of their detailed environmental knowledge in ways that improve the sustainable management of their forest. Supporting local people to share their environmental knowledge in scientifically valid and strategically targeted ways can lead to improvement in environmental governance, environmental justice and management practices. Mbendjele hunter-gatherers in the rainforests of Congo are working together with the ExCiteS Research Group at University College London to make their local knowledge about commercial hunters' activities improve the control of commercial hunters and diminish the harassment they often experience at the hands of "eco-guards" who enforce hunting regulations. Developing and deploying a system for non-literate users introduces a range of challenges that we have tried to solve. Our Anti-Poaching data collection platform, running on Android smartphones, is based on a decision tree of pictorial icons and employs various smartphone sensors to augment observations. We describe its development here.

Participatory sensing is a crowd-sourcing technique which relies both on active contribution of citizens and on their location and mobility patterns. As such, it is particularly vulnerable to privacy concerns, which may seriously hamper the large-scale adoption of participatory sensing applications. In this paper, we present a privacy-preserving system architecture for participatory sensing contexts which relies on cryptographic techniques and distributed computations in the cloud. Each individual is represented by a personal software agent, which is deployed on one of the popular commercial cloud computing services. The system enables individuals to aggregate and analyse sensor data by performing a collaborative distributed computation among multiple agents. No personal data is disclosed to anyone, including the cloud service providers. The distributed computation proceeds by having agents execute a cryptographic protocol based on a homomorphic encryption scheme in order to aggregate data. We show formally that our architecture is secure in the Honest-But-Curious model both for the users and the cloud providers. Our approach was implemented and validated on top of the NoiseTube system [1], [2], which enables participatory sensing of noise. In particular, we repeated several mapping experiments carried out with NoiseTube, and show that our system is able to produce identical outcomes in a privacy-preserving way. We experimented with real and simulated data, and present a live demo running on a heterogeneous set of commercial cloud providers. The results show that our approach goes beyond a proof-of-concept and can actually be deployed in a real-world setting. To the best of our knowledge this system is the first operational privacy-preserving approach for participatory sensing. While validated in terms of NoiseTube, our approach is useful in any setting where data aggregation can be performed with efficient homomorphic cryptosystems.

Noise pollution is a major problem in cities around the world. The current methods to assess it neglect to represent the real exposure experienced by the citizens themselves, and therefore could lead to wrong conclusions and a biased representations. In this paper we present a novel approach to monitor noise pollution involving the general public. Using their mobile phones as noise sensors, we provide a low cost solution for the citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. Our prototype, called NoiseTube, can be found online [1].