Abstract-Mobile and environmental sensing technology can be used to assess human behaviour and mental health trajectories outside of laboratories and in ecologically-relevant settings. To achieve maximum benefit, the set of equipment and the monitoring patterns must be personalised to respect individual needs and fit into individual lifestyles.We have developed a sensor network infrastructure for mobile phones and homecare using a rule-oriented programming architecture to monitor the activity signatures of people with Bipolar Disorder (BD). We believe that the use of this rule-based paradigm within the network for a mental health setting to be a contribution of this work.We are evaluating the effectiveness of the technology in an ongoing technical trial with control participants as a precursor to studying the effectiveness of the system for use with people with BD. In this paper, we report the design and development of the monitoring system along with preliminary findings from the technical trial of the system, and discuss future developments.

The Bee Lab project applies Citizen Science and Open Design to beekeeping, enabling participants to construct monitoring devices gathering reciprocal data, motivating participants and third parties. The presented approach uses design workshops to provide insight into the design of kits, user motivations, promoting reciprocal interests and address community problems. This paper signposts issues and opportunities in the process of designing Citizen Science tools for communities using Open Design to solve individual problems, including: downloadable design for social/local change, laypeople creating technology and repairable kits.

No abstract

Abstract. This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as 'data management', can benefit from einfrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences.

One in ten of the (UK) population will suffer a disabling mental disorder at some stage in their life. Bipolar disorder is one such illness and is characterized by periods of depression or manic activity interspersed with stretches of normality. Some patients are able to manage this condition via their self-awareness that enables them to detect the onset of debilitating episodes and so take effective action. Such self management can be achieved through a paper-based process, although more recently PDAs have been used with success. This presentation will introduce the Personalised Ambient Monitoring (PAM) concept that aims to augment such processes by automatically providing and merging environmental details and information relating to personal activity. Essentially the PAM project is investigating what may be loosely referred to as ‘electronic' monitoring to automatically record ‘activity signatures' and subsequently use this data to issue alerts. The types of data that we are considering using includes: location and activity (e.g. via GPS and accelerometers); and environment (e.g. temperature and light levels). Other types of sensor under consideration are passive IR sensors (within the home); and sound processing to log the audio ‘environment'. The use of such monitoring will be agreed between the patient and their health care team and it is anticipated that different patients will be comfortable with different sensor packages, thus personalizing the monitoring. Although such tele-monitoring is now generally common, its use in the treatment of the mentally ill is still in its infancy. This paper will consider the specific problems faced in applying it to this community along with the aims of this project. In addition, the use of modelling to predict the effects of the possible problems of sparse data that is expected, and to predict the effect on the overall patient pathway will be considered