Author: Hazel Sayers

This is a short story about how our team—myself, Martin Porcheron, and Joel Fischer —disseminated our research on voice interfaces to UX and design practitioners through a varied campaign spanning over a year and a half to the present.

It began with a technology field trial that formed part of Martins PhD, during which he collected many hours of audio recordings of Amazon Echo use in domestic settings where the devices were deployed for a month. While working towards our first major academic milestone — a CHI 2018 paper (which was submitted in September 2017), we also began discussing and presenting early versions of this work-in-progress with practitioners.

This engagement with practice grew from exchanges with the BBC UX&D design research team with whom we discussed our emerging research and its results. Subsequently this led to us conducting a BBC UX&D “Studio Day” workshop that enabled us to do a more focussed presentation, coupled with a practical exercise for groups of UX&D practitioners, to consider issues raised when designing for voice interfaces. Performing this mini-workshop helped engender a more meaningful discussion than simply presenting what were then very preliminary results.

We presented our work publicly to the Cambridge Usability Group (CUG), and to a UX agency in London. Both activities led us to consider taking the material to a larger conference.

To begin with, these initial engagements with smaller audiences of practitioners enabled us to gauge what might be interesting and relevant for them, as well as providing us with a better sense of what kind of involvement with voice interfaces was realistic for designers. They also enabled us to gradually ‘prototype’ our ideas and presentational formats in a way that integrated our (ongoing) work, allowing us to establish a ‘feedback’ between the research side of things (e.g., developing papers) and practitioner-facing talks.

With our CHI 2018 paper accepted, I then presented our work to Interaction18, a large interaction design industry conference organised by the Interaction Design Association (IxDA). The tight 15 minute slot offered by IxDA required significant sharpening in order to maximise our work’s relevance and impact. My talk at Interaction18 was recorded and made more widely available by the conference organisers. I also decided to transform it into a Medium post which I made available soon after. This resulted in a positive uptake, 1.6k+ views on Medium and counting.

Presenting at a reasonably high-profile event for UX and design practitioners let us establish a formula—in miniature— that we could be confident with, that mostly seemed to ‘work’ for people and be of relevance – as evidenced by in-person feedback and further enquiries afterwards. Critically, it also generated further interest from practitioners in the form of invitations to speak about our work at other venues. In this way, smaller UX conferences and meetups followed and have built on this: The Research Thing, London in April; HCID Open Day at City, University of London in May; The Design Exchange, Nottingham in August; and the Service Design Meetup, London in October.

Whilst not clear as to what all this ultimately will lead to, we have a number of other future events and publications in development. It proves difficult to track and trace our impact on UX and design practitioner communities, even though we can capture how our learning as academics that engaging with practice has helped shape the research in various positive ways: increased conceptual clarity, focus, and a better appreciation for the value of different formats. All of these in turn help sharpen our standard academic dissemination and research approach.

Photo by Leo Cardelli from Pexels

James Pinchin: “I’ve very much enjoyed supervising Kyle Harrington throughout his studies investigating human search behaviour in emergency situations in order to facilitate the development of safer walking technologies for vulnerable people. Co-supervised by Prof. Sarah Sharples, Kyle is based within the Human Factors Research Group and Horizon Digital Economy Research.  Kyle’s research was sponsored by Phillips Research”.

A Socio-Technical approach to missing incidents

Every year the police receive around a quarter of a million reports of missing people in England and Wales alone. Whilst the vast majority of those who become missing return home safely; people with additional care and support needs are far more likely to suffer from physical, emotional or psychological harm. Missing Incidents are not only traumatic for those involved, but are also likely to contribute to overall public spending; both with respect to the resources required for Missing Person searches, but also due to the increased likelihood of the breakdown of familial care following difficult to manage behaviours. Effective responses to these incidents are imperative, but there is little academic research which explores how these practices could be improved and no work at all investigating the decision-making of carers or parents during these incidents.

In his recently submitted thesis, Kyle Harrington draws together several disparate research areas, alongside original research, which helps to elucidate how those responsible for a person with care and support needs search, navigate and make decisions under stress. The work described in his thesis represents an attempt at a systematic understanding of how missing incidents unfold, how decision-making within missing incidents can be predicated, and ultimately what can be done to address the problem. With a focus on decision-making and technology; the thesis uses a three stage approach to describe, predict and address the problem of Missing Incidents. Several key design recommendations were produced which are intended to inform the design of new technologies for supporting missing person searches and may be of use to technology developers, policy-makers, care providers and other stakeholders

For my first postdoctoral position, I was lucky enough to become a member of the Gestational Project, a research project funded by the Bill and Melinda Gates Foundation that was a collaboration between the School of Computer Science and the School of Medicine at the University of Nottingham.

The goal of the project was to create a system that would combine photographs of a newborn’s face, foot and ear to calculate their gestational age right after birth. The gestational age of a baby is important because it measures how premature they are at birth, which directly influences the type of treatment that they will receive. The most accurate dating method right now is an ultrasound scan, but these machines are expensive, require trained personnel and cannot always be deployed to remote areas. In the absence of ultrasound scans, there is a manual method that can be used, the Ballard Score. The Ballard Score looks at the posture and measurements of a newborn in order to determine their gestational age. However, this method is highly subjective and results can vary widely depending on the experience of the person carrying out the exam.

The ultimate idea was to create an accurate system that would combine the objectivity and accuracy of an ultrasound scan and the postnatal measurements of the Ballard Score, therefore allowing non-trained personal to measure the gestational age of newborns. Such method would be useful in remote or under-funded areas where ultrasound machines or trained physicians might not be available or easily accessible.

I found the project fascinating and its area of application, healthcare, was new for me. In my PhD, I had worked in environmental problems, where, if I needed data, I could either download free images from sources like Geograph or go outside and take a picture of plants (you may think I’m exaggerating, but I am not). In other words, data collection was fast, straightforward and I had to think very little about its effects on the design of my algorithm. If accuracy was low, I could go through the motions again, download or take more photos, and see if that helped.

However, when I started working in this project, I realised that my data collection and maintenance habits had to become much more nuanced and serious. Just to get through NHS Ethics took 8 months, which is more than reasonable, since we needed to take photographs of such a protected group. But, even after getting the approval, recruiting participants was, also understandably, a challenge.

To ensure privacy and safety, images had to be stored in encrypted disks and access to them was limited to only the four people working on the project. Only the team at QMC were allowed to recruit and take the photos, so I had no information about the parents or newborns other than their photos and measurements. A downside of this measures is that I have never been able to express my thanks to all the parents (and babies!) who were generous enough to allow the team take photos, even when they were in dangerous circumstances. It was only due to their kindness that we were able to collect any data at all and to them we owe them the success of the project.

The characteristics of the data, including how challenging it was to collect, became a deciding factor in the design of the algorithm. If the accuracy in the calculations was low, I could not rely on additional and instantaneous data collection. Instead, I had to rethink the algorithm design and change it to work with the data we had. Incorporating these notions into our design, we were able to create a system that combined deep learning and regression and that  was able to calculate the gestational age of newborns with 6 days of error in the worst case scenario [1].

All in all, I can say that I loved working on the Gestational Project and that I learned a lot. I think that its challenges made me a more conscious researcher, one who treats data collection and management much more carefully now, independently of the area of application.

Right now, we are working to use the Centre for Doctoral Training Impact Fund to continue to increase the size and diversity of our dataset, hopefully collecting data in other parts of the country and the world. You can find more information about the GestATional Project here.

[1] Torres, M.T., Valstar, M.F., Henry, C., Ward, C. and Sharkey, D., 2017, May. Small Sample Deep Learning for Newborn Gestational Age Estimation. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on (pp. 79-86). IEEE

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