Tag: data

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Privacy-preserving & machine-learned catchment models for national dietary surveillance via digital footprint data

Horizon’s Transitional Assistant Professor, Georgiana Nica-Avram has co-authored a paper ‘Privacy-preserving & machine-learned catchment models for national dietary surveillance via digital footprint data’ which was submitted to the 2022 IEEE International Conference on Big Data.

Abstract

Big data from food retail stores is increasingly being used for population dietary surveillance, epidemiological studies of diet-related diseases, and evaluations of public health interventions. However, for retail data to be useful it is necessary to understand the spatio-temporal variation of when and where food is purchased and consumed. While some customers willingly share home location data with retailers as part of loyalty programs such data is typically too fine-grained/sensitive to be applied for research purposes. The aim of this study was to analyse differences between privacy-preserving models and actual retail catchments, and investigate if machine learning techniques could improve the accuracy of such catchment models. Based on a UK-wide sample of 4 million grocery store loyalty card holders, covering 485 million transactions over 29 months (2019-2021) and distributed across 33,000 neighbourhoods (Lower Super Output Areas, or LSOA), the study demonstrates how models trained on geolocated data perform at predicting, per store, catchment areas which contain 50, 80, and 95% of its customers’ primary location. Through comparative assessment of machine learning approaches, we find better performance from tree-based models (RF, XGB) with the best performance from an XGB model achieving an R2 of 0.72 and MAE of 1.06. To conclude, we review variable importance measures using SHAP values and discuss the relative merits of including specific features when modeling catchment areas. © 2022 IEEE.

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TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which a consensus on a common storage, exchange and archival data format standard, has yet to be reached.

Horizon TAP Yordan Raykov contributed to research featured in this paper which posed a series of format design criteria and reviewed in detail, existing storage and exchange formats. When the researchers judged against these criteria, they found the existing formats lacking, and proposed Time Series Data Format (TSDF), a unified, standardized format for storing all types of physiological sensor data, across diverse disease areas.

Find out more by accessing the paper here.

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£1.2m EPSRC Award for “Fixing The Future: The Right to Repair and Equal-IoT”

Consider this future scenario:

Home insurance firms now mandate installation of smart smoke alarms. One popular low-cost alarm has developed a speaker hardware fault and recently stopped providing software security updates after just 2 years. Adam, who can financially afford to, simply buys another alarm and throws the broken one away. Ben however, cannot afford a new alarm and faces unexpected financial consequences. The lack of security means his home network has been compromised and hackers intercepted his banking log-in credentials. Worse still, the faulty alarm did not warn him of a small housefire in the night, causing damage he cannot afford to repair. The insurance company is refusing to pay the claim, as unknown to Ben, they recently removed the device from their list of accredited alarms. 

October 2022 is the start of Fixing The Future: The Right to Repair and Equal-IoT – a £1.2m EPSRC project that examines how to avoid such future inequalities due to the poor long-term cybersecurity, exploitative use of data and lacking environmental sustainability that defines the current IoT. Presently, when IoT devices are physically damaged, malfunction or cease to be supported, they no longer operate reliably. Devices also have planned obsolescence, featuring inadequate planning for responsible management throughout their lifespan. This impacts on those who lack the socio-economic means to repair or maintain their IoT devices, leaving them excluded from digital life for example, with broken phone screens or cameras.

This project is an ambitious endeavour that aims to develop a digitally inclusive and more sustainable Equal-IoT toolkit by working across disciplines such as law and ethics, Human Computer Interaction, Design and technology.

Law and Ethics: To what extent do current legal and ethical frameworks act as barriers to equity in the digital economy, and how should they be improved in the future to support our vision of Equal-IoT?  We will examine how cybersecurity laws on ‘security by design’ shape design e.g. the proposed UK Product Security & Telecoms Infrastructure Bill; how consumer protection laws can protect users from loss of service and data when IoT devices no longer work e.g. the US FTC Investigation of Revolv smart hubs which disconnected/bricked customer devices due to a buy-out by Nest.

Design: How can grassroots community repair groups help inform next-generation human-centred design principles to support the emergence of Equal-IoT?  We will use The Repair Shop 2049 as a prototyping platform to explore the legal and HCI related insights in close partnership with The Making Rooms (TMR), a community led fabrication lab working as a grassroots repair network in Blackburn. This will combine the expertise of local makers, citizens,   civic leaders and technologists to understand how best to design and implement human-centred Equal-IoT infrastructures and explore the lived experience of socio-economic deprivation and digital exclusion in Blackburn.

Human Computer (Data) Interaction (HDI): How can HCI help operationalise repairability and enable creation of Equal-IoT? We will create prototype future user experiences and technical design architectures that showcase best practice on how Equal-IoT can be built to be more repairable and address inequalities posed by current IoT design. Our series of blueprints, patterns and frameworks will align needs of citizens technical requirements and reflect both constraints and opportunities manufacturers face.

Image created and provided by Michael Stead

This is an exciting collaboration between the Universities of Nottingham, Edinburgh, Lancaster and Napier, along with industry partners –Which?, NCC Group, Canadian Government, BBC R&D, The Making Rooms.

For more information about this project, contact Horizon Transitional Assistant Professor Neelima Sailaja

 

 

 

 

 

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Mercedes Torres Torres – Unobtrusive Behaviour Monitoring via Interactions of Daily Living

Nowadays, most of our daily living entails constant interactions with different utilities and devices. Every time you turn on your kettle, take a shower, send a text or buy something with your card, information about this interaction is collected by different companies, such as your energy, water, telephone provider or bank. These companies may then use that data for different purposes, for example to recalculate your tariffs or to suggest further services.

The goal of the Unobtrusive Behaviour Monitoring project is to combine all the information that we are continuously generating through daily interactions with technology and to feed it back in a constructive and organised way so we can see different patterns in our behaviour. This type of analysis could be particularly insightful for people who suffer from mood disorders, who could use it to monitor their condition, provide early warning signs and facilitate the development of self-awareness of their triggers. Overall, the ultimate goal is to enable patients to take control of their condition. In other words, we want to allow people to benefit from the data they themselves are constantly generating and help them to use it, when they want, to monitor their mood. And, more importantly, we want to do this in a way that respects their privacy and keeps their data safe.

From a machine learning perspective, this project is interesting because it would require the development of algorithms that could work locally in order to protect people’s privacy (i.e. analysis should be done at each person’s home instead of in a centralised database), while also work with sparse or incomplete data (i.e. in the case that one person decided to turn off one of the sources of information).

This project is an on-going collaboration between the Faculty of Engineering, the Institute of Mental Health, and the Faculty of Science. Phase I was funded by  MindTech and its main goal was to explore the literature in the area and to engage with experts. We explored ethics, technical, legal and social aspects of four different sources of data (utility and device usage, car usage and driving style, banking and card transactions, and mobile phone usage) and carried out different engagement activities with patients groups and other researchers in the area.

These activities, particularly our engagement with patients’ groups, were extremely useful. Talking with people with mood disorders who are interested in self-monitoring their condition, helped us identify potential directions and issues from a usability perspective, such as the need for total customisation of sensor (i.e. the possibility of turning off particular sources of data, instead of all of them at the same time) –  or the possibility of adding additional sensors like sleep or activity sensors through wearables like Fit Bit.

For Phase II, we are hoping that we can carry out a pilot study in which we collect some data and develop machine learning algorithms able to cope with distributed information that might be scarce or incomplete.

Mercedes Torres Torres, Transitional Assistant Professor, Horizon Digital Economy Research