Details of Grant 

EPSRC Reference:	EP/W031868/1
Title:	Using high temporal resolution sensor data to support independent living
Principal Investigator:	Mueller, Professor M
Other Investigators:	Bland, Professor ER Leyshon, Professor CS
Researcher Co-Investigators:
Project Partners:	Cornwall Council Leatside Surgery
Department:	Mathematics
Organisation:	University of Exeter
Scheme:	Standard Research
Starts:	01 January 2023	Ends:	31 December 2024	Value (£):	412,004
EPSRC Research Topic Classifications:	Building Ops & Management Environment & Health Mathematical Analysis
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 23 Feb 2022 SI Transform health at home Announced
Summary on Grant Application Form
We will explore the links between patterns of sensor data within the home and health patterns of vulnerable residents. We will monitor internal home environment (temperature, humidity, air quality) and electricity usage over time, and use features in the patterns to detect unusual events. We will use health and wellbeing data from participants to assess whether the usual events detected relate to underlying issues in the home. Once connections between sensor data and underlying health are established, we will aim to predict events in advance to allow earlier or pre-emptive support. To ensure the relevance of this approach we will involve end users throughout using a co-design approach. We have engaged a public involvement and engagement group, and will establish a stakeholder group of representatives of health and care providers. We will recruit 50 participants, who are vulnerable or have existing health conditions. We will draw on our experience of analysis techniques with the comprehensive Smartline data set (including long-term and high-frequency time-series environmental sensor data and electricity usage for four years). We will characterise data, and detect and predict changes in the home suggesting health and wellbeing issues. If successful, this test of feasibility will support early intervention and thus maintaining independent living. We will extract features from the data using the following methods. Fourier analysis will determine dominant frequencies in the sensor data. Autoregressive models will establish the extent of influences from previous readings to current and future readings. Long short-term memory neural networks will be used to predict readings. We will also use neural networks and support vector machines to predict anomalies in advance of them occurring, and cluster analysis to categorise days that have different types of features.
Key Findings
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Potential use in non-academic contexts
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Further Information:
Organisation Website:	http://www.ex.ac.uk