Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model

Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabling more accurate, efficient data capture. The complex nature of these data streams in combination with challenge of deriving meaning from missing data mean pose a significant analytic challenge. The signature method is derived from stochastic analysis and has the ability to capture important properties of complex ordered time series data. To explore whether the onset of episodes of mania and depression can be identified using self-reported mood data.Comment: 12 pages, 3 tables, 10 figure

Distributed sensing devices for monitoring marine environment

The lack of affordable, self-sustaining platforms for monitoring marine water quality means that measurements are done primarily through grab sampling at a limited number of locations and time, followed by analysis back at a centralised facility. This has resulted in huge gaps in our knowledge of water quality. This project aims to develop platforms capable of remote sampling and analysis over extended periods of time. This would provide the building blocks for establishing an 'environmental nervous system' comprised of many distributed sensing devices that share their data in near real-time on the web. The envisaged 'environmental nervous system’ allows marine environment to be closely monitored, enabling the early detection of pollution events to minimise the danger to people and contamination of distribution systems

Ethics, Nanobiosensors and Elite Sport: The Need for a New Governance Framework

Individual athletes, coaches and sports teams seek continuously for ways to improve performance and accomplishment in elite competition. New techniques of performance analysis are a crucial part of the drive for athletic perfection. This paper discusses the ethical importance of one aspect of the future potential of performance analysis in sport, combining the field of biomedicine, sports engineering and nanotechnology in the form of ‘Nanobiosensors’. This innovative technology has the potential to revolutionise sport, enabling real time biological data to be collected from athletes that can be electronically distributed. Enabling precise real time performance analysis is not without ethical problems. Arguments concerning (1) data ownership and privacy; (2) data confidentiality; and (3) athlete welfare are presented alongside a discussion of the use of the Precautionary Principle in making ethical evaluations. We conclude, that although the future potential use of Nanobiosensors in sports analysis offers many potential benefits, there is also a fear that it could be abused at a sporting system level. Hence, it is essential for sporting bodies to consider the development of a robust ethically informed governance framework in advance of their proliferated use

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort