Hello everyone, my name is Thomas Williams and I am a doctoral student at the University of Bath.
Today I will be talking about the study presented in our paper,
"Augmented Reality and Older Adults: A Comparison of Prompting Types".
AR is becoming more prevalent
and as we get closer to the reality of pervasive AR,
it is important to explore how AR can be used in everyday situations.
Older adults can benefit from the use of technology to support completion of tasks
and although augmented reality has been studied extensively for task prompting in *industrial* settings,
less is known about its use in everyday contexts.
Furthermore, older adults are under-represented in AR research.
With our study, we set out to gain a better understanding of what types of visual AR prompts could be effective at enabling older adults to complete tasks in everyday, non-industrial contexts.
We asked:
"What types of visual augmentations are suitable for task prompting to support activities of daily living?"
and
"Does the suitability of AR prompts depend on the types of actions that need to be performed?".
To answer these questions, we designed a lab study to compare
four types of AR visual prompting styles and a text plus audio control prompt.
We considered five basic actions found in many common Activities of Daily Living
and invited adults who were at least 50 years of age to take part.
The actions we chose were related to the four basic commands given by Schwartz's Action Coding System:
TAKE, GIVE, MOVE, and ALTER.
We tested five simple actions, which were:
Pick up (mapping to TAKE),
Put down (mapping to GIVE),
Move (mapping to MOVE ),
and Open and Close, which are both examples of ALTER commands.
These actions were common in the literature and also featured often in our own semi-structured interviews with professionals who support older adults to complete tasks at home.
To test these actions, we designed and 3D-printed four objects, namely:
a cube,
a cylinder,
a pyramid,
and a book-like shape.
Each object could be in an open or closed state, but this was not immediately obvious by looking at them.
These objects sat on a base board with five distinct regions.
The left-hand region acted as a home position for the objects,
and the four coloured areas on the right were used as a target for the put down and move actions.
By designing unfamiliar objects with unfamiliar affordances, we were able to test how well our augmentations were able to prompt each of our chosen actions.
We took inspiration for the designs of our augmentations from existing AR task prompting techniques.
Our four designs were:
1. the use of arrows,
2. highlighting objects in stationary positions,
3. moving models of objects,
and 4. a Ghosthand demonstrating the action.
We compared these with text and audio prompts, which have been used in existing task prompting tools for older adults.
In total, this resulted in 25 combinations of action and augmentation.
The video figure accompanying our paper demonstrates each combination and a textual description is also provided.
We recruited 20 participants for our study.
The board and objects were tracked by a camera that stood to the side of the objects,
and the augmentations were displayed one-by-one on a laptop screen next to the board.
Participants saw every combination of action and augmentation twice and we told participants to perform the action they thought was being communicated to them for each combination.
After each trial, we asked participants to say what they thought the action was, and to give a rating of how confident they were that they were correct.
After participants had seen every combination twice, we asked them to rank the augmentations for each action in order of preference.
We found that participants preferred text plus audio prompts overall,
but the most preferred visual AR prompt was the GHOSTHAND.
Participants preferred these prompts because they were unambiguous.
Our results suggest that AR *is* suitable as a prompting method for these simple actions,
but more work is required to explore more complex actions, for example, those that involve more than one object.
We also found that participants' confidence in performing actions varied according to action and augmentation.
Compared with the Text+Audio prompt for the CLOSE action, participants were less confident with their answers
for the pick up and put down actions, and for the ARROW and HIGHLIGHT augmentations.
This may have been because these combinations were not as clear as others:
Participants often described that they were using clarity as their ranking criterion.
Our work provides the first comparative study of AR with older adults.
Our results highlight several implications for design of AR prompting systems,
including how arrow and highlight augmentations are less effective
than moving object or ghost hand augmentations for a range of actions commonly found in many tasks.
Our study provides a foundation for future work, which could explore
more complex, multi-step tasks in a specific context,
such as making a cup of tea and other activities of daily living.
This work was carried out with my co-authors and supervisors: Simon Jones, Christof Lutteroth, Elies Dekoninck and Hazel Boyd.
Thank you to the participants of this study, without whom this work would not have been possible,
and thank you to you for watching.