Jesse Michael Blum

The Design and Evaluation of Personalised Ambient Mental Health Monitors

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.

Personalised Ambient Monitoring (PAM) of the mentally ill

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.

Testing a grassroots citizen science venture using open design, "the bee lab project"

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.

Enabling Quantitative Data Analysis Through e-Infrastructure

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

The timestreams platform: artist mediated participatory sensing for environmental discourse

Ubiquitous and pervasive computing techniques have been used to inform discourses around climate change and energy insecurity, traditionally through data capture and representation for scientists, policy makers and the public. Research into re-engaging the public with sustainability and climate change issues reveals the significance of emotional and personal engagement alongside locally meaningful, globally-relevant and data-informed climate messaging for the public. New ubiquitous and pervasive computing techniques are emerging to support the next generation of climate change stakeholders, including artists, community practitioners, educators and data hackers, to create scientific data responsive artworks and performances. Grounded in our experiences of community based artistic interventions, we explore the design and deployments of the Timestreams platform, demonstrating usages of ubiquitous and pervasive computing within these new forms of discourse around climate change and energy insecurity.

The ethics of wearable cameras in the wild

Improvements in wearable camera technologies are providing academic and industry researchers with new ways to answer questions about participant behaviour. Although promising, these methods raise a number of ethical concerns in regards to agency, accountability, third party trust, and the delegation of responsibility. In this paper we consider the use of wearable cameras in research through the utilisation and adaptation of the Ethical Technology Assessment (eTA) method within a workshop involving a group of stakeholders, including researchers, technologists, and research participants. We conclude the paper with recommended principles for researchers and ethics review committees interested in assessing the usage of wearable cameras for conducting research outside of lab settings.

A social science data-fusion tool and the Data Management through e-Social Science (DAMES) infrastructure

The last two decades have seen substantially increased potential for quantitative social science research. This has been made possible by the significant expansion of publicly available social science datasets, the development of new analytical methodologies, such as microsimulation, and increases in computing power. These rich resources do, however, bring with them substantial challenges associated with organizing and using data. These processes are often referred to as ‘data management’. The Data Management through e-Social Science (DAMES) project is working to support activities of data management for social science research. This paper describes the DAMES infrastructure, focusing on the data-fusion process that is central to the project approach. It covers: the background and requirements for provision of resources by DAMES; the use of grid technologies to provide easy-to-use tools and user front-ends for several common social science data-management tasks such as data fusion; the approach taken to solve problems related to data resources and metadata relevant to social science applications; and the implementation of the architecture that has been designed to achieve this infrastructure.

Exploring the Relationship between Location and Behaviour in Out of Hours Hospital Care

‘Out of Hours’ (OoH) hospital care involves a small number of doctors covering a very large number of patients. These doctors are working in stressful environments, performing complex tasks and making difficult task prioritisation decisions, yet little data exists to aid in improving the working practices or to ensure junior doctors are adequately prepared for OoH working. Historically, this has been owing to complex and expensive processes to capture this data; however recent advances in indoor positioning technologies has the potential to automate and improve the capture and availability of data that may help alleviate the burden of OoH care on at a personal and hospital level. This paper describes our work to combine cutting edge indoor positioning technologies from OoH working with and a newly deployed in-ward electronic tasking system. Here we describe data collection via traditional methods, clinical tasking systems, and indoor positioning solutions. We further describe our understanding from such data of the effect of physical layout and current working practices on task completion and time spent in transit, which ultimately may inform improvements to working practice within OoH care. Finally we discuss potential relevance to other work domains.

A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder

This paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD). The modelling work was part of an EPSRC-funded project, also involving biomedical engineers and computer scientists, to develop a prototype PAM system. BD is a chronic, disabling mental illness associated with recurrent severe episodes of mania and depression, interspersed with periods of remission. Early detection of the onset of an acute episode is crucial for effective treatment and control. The aim of PAM is to enable patients with BD to self-manage their condition, by identifying the persons normal ‘activity signature’ and thus automatically detecting tiny changes in behaviour patterns which could herald the possible onset of an acute episode. PAM then alerts the patient to take appropriate action in time to prevent further deterioration and possible hospitalisation. A disease state transition model for BD was developed, using data from the clinical literature, and then used stochastically in a Monte Carlo simulation to test a wide range of monitoring scenarios. The minimum best set of sensors suitable to detect the onset of acute episodes (of both mania and depression) is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensors.

Exploring conflicts in rule-based sensor networks

This paper addresses rule conflicts within wireless sensor networks. The work is situated within psychiatric ambulatory assessment settings where patients are monitored in and around their homes. Detecting behaviours within these settings favours sensor networks, while scalability and resource concerns favour processing data on smart nodes incorporating rule engines. Such monitoring involves personalisation, thereby becoming important to program node rules on the fly. Since rules may originate from distinct sources and change over time, methods are required to maintain rule consistency. Drawing on lessons from Feature Interaction, the paper contributes novel approaches for detecting and resolving rule-conflict across sensor networks.