Details of Grant 

EPSRC Reference:	EP/M029484/1
Title:	Development of a Multimodal Lifelogging Platform to Support Self-Reflection & Monitor Inflammation Associated With the Experience of Negative Emotions
Principal Investigator:	Dobbins, Dr C
Other Investigators:	Fairclough, Professor S
Researcher Co-Investigators:
Project Partners:
Department:	Computing and Mathematical Sciences
Organisation:	Liverpool John Moores University
Scheme:	First Grant - Revised 2009
Starts:	31 August 2015	Ends:	30 September 2016	Value (£):	98,964
EPSRC Research Topic Classifications:	Computer Graphics & Visual. Digital Signal Processing Human-Computer Interactions
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 14 Apr 2015 EPSRC ICT Prioritisation Panel - Apr 2015 Announced
Summary on Grant Application Form
The project will develop a mobile lifelogging platform that will deliver measures of cardiovascular disease in an everyday situation, such as driving a car. Driving represents a common daily activity, where experiences and expressions of anger have implications for health and safety. As such, this activity can be associated with high levels of negative emotions that have a cumulative impact on long-term health. Lifelogging is the continuous act of recording and documenting our lives, from the things we do, to the places we visit and even our feelings. Wearable cameras and body sensors allow us to capture rich information from multiple data sources about ourselves. As sensors become more prevalent, within our environment, the range of available data is increasing. This has enabled lifelogs to become richer with information and their use in various application domains, such as digital health, is increasing. The project will explore how multiple streams of physiological and contextual data can be processed and integrated in real-time to detect the user's state. Measures such as heart rate, pulse wave velocity (PWV), speed of the vehicle, location, and first-person photographs of the environment will be brought together to identify instances of anger and inflammation. A range of signal processing approaches will be applied to these data items (e.g. inter-beat interval from the heart rate will be subjected to Fast Fourier Transform) and artefacts will be identified and either removed or incorporated in real-time. Currently, it is straightforward to log overt aspects of behaviour, such as photographs, location and movement. However, this project will combine those markers with covert changes in cardiovascular physiology, which aren't perceived directly by the user. Hence, the project is extending a person's awareness of their bodies, how their behaviour and reactions to situations are directly impacting their bodies and the triggers for such behaviour, e.g. traffic congestion at a junction may raise our heart rate, without the user being consciously aware of this physiological change. Repeating this stressful behaviour daily, over a sustained period, could contribute to the development of cardiovascular disease. Reviewing moments when arterial inflammation occurs and understanding the context of this behaviour leads to an enhanced perception of how daily events affect health. This can lead to positive changes to the person's lifestyle, such as avoiding the junction in question to help prevent triggers leading to the onset of cardiovascular disease. The system will provide a new method to monitor and influence behaviour, which enables us to enhance and bring the field of lifelogging into alignment with advances in digital health. This is achieved using markers that are clinically relevant in the context of lifelogging technologies and developing techniques to process multi-modal signals in real-time. To the best of the author's knowledge, the integration of such biomedical markers that measures physiological changes in context to prevent the onset of disease has not been addressed in any other developments. Overall, the project attempts to reduce a significant real-world problem with an advanced mobile lifelogging platform. The platform will be evaluated in a real-world scenario to assess its capabilities outside of an artificial environment. This will enable us to gauge its robustness as a real and practical solution to log and quantify behaviour. In this way, the data collected will be used to identify moments of arterial inflammation and the context of those times to promote self-reflection and the implementation of behavioural changes.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Further Information:
Organisation Website:	http://www.livjm.ac.uk