Hi everybody I'm Federica Cena from the university of Turin and I am here to present a
personalised tourist guide for people with autism, a work done by the computer science department of the
university of Turin together with the audult autist center of the city of Torino
Developing a a cultural heritage guide that wants to be inclusive shows several challenges: from steering
content recommendation to individual interests and specific capabilities to suggesting
place that could be reach under different types of constraints, from temporal to physical ones. In this paper we focus on needs
of people with autism spectrum disorders. Autism is a spectrum condition, that
means that affects people differently. Some people also show learning
disability and cognitive issues, while others have full intellectual capabilities
What is common in all people with autism is an atypical social functioning which often results
in isolation. They show also a reduced range of activity and interests and a strong preference for a
rigid routine. So, people with autism tend to avoid novel situations that can be perceived
as stressful. At the same time people with autism seems to perceived the word differently
they show in fact atypical sensory perception: theirs brain
seems enable to balance the outside senses. Such idiosincratic sensory aversions may
result in anxiety, sense of oppression, stress and distraction.
These features affects the tourist experience of people with autism. Travelling causes an enormous level of stress in such
people because of breakdown of the routine or the managing of unexpected situations and also
for new interpersonal social relation that they have to manage. The result is that
people with autism tend to avoid traveling or going in un trips. It is necessary to help
people by selecting things to do and place to visit that can be perceived as
safe because they present sensory feature that are compatible with her/him or because
they are similar to what they experienced in the past. A place that can be relevant from a touristic
point of view can be unsuitable for an autistic user because it is very different from
what she is used to experience in the everyday life. So, our idea is to develope
an inclusive tourist guide that can help people with autism in visiting a new place
trying to minimize the level of stress. To this aim we focus on aversions derived
from the user high sensitivity to sensory stimulation. So we aim at suggesting
places, points of interest, that are both relevant to her/him and which present safe feature from
sensory point of view. Such as they can be scarcely crowded or with smooth lights
The target of our mobile guide are autistic audults with medium-high functioning. The starting
point is a project called PIUMA (Personalized Interactive Urban Maps for Autism) which
has the general aim to help people with autism in their everyday movement in a city. The first result of this project
will be a mobile app -currently under development- which shows save point of
interest which will be selected according to both user preferences and aversions.
The recommendation algorithm of this app
will be described in a full paper in the main conference.
In this paper we describe how we intend to extend this first prototype to support autistic people
in exploration of new places, so in touristic exploration. So we need to have more cultural point of interest
with sensory information about them;
we need to design the user interface for presenting such information
and we need to add to the app safe paths and tours. For safe paths we mean paths that satisfy the perceptual needs of a person with autism,
so for example we do not suggest a very busy road to a person who is very bothered by noise. Here we start presenting the recommendation algorithm of Mauro 2020
that can be reused in this context. Lets start with the rating prediction. The idea is to combine the compatibility and
the preferences. For compatibility we measure the overall level of aversion of persons with respect
to a place regarding it sensorial features. While for preferences we mean how
much a user likes a category of an item item. So we try to combine these two values to
estimate the rating of each single place.
Then, a user-specific value can be then learned by analyzing the ratings provided by the user when she interacts with the
mobile app. So we assume that the overall compatibility of a poi, a place (comp in the previous slide) depends on the compatibility of each of its sensory features.
So we need to compute comp by aggregating each single values and we used two
different models: a pessimistic one where the overall compatibility of an item
with user corresponds to the minimun compatibility of all features;
and a more optimistic one where the overall compatibility of an item is the mean of features compatibility.
The best performing model can be empirically identified by optimizing rating estimation
with respect to the rating of place provided by users.
Then, in order to have this recommender we need of course to have a user model.
In our user model we need to collect the preferences for category and the aversions for sensory features.Collecting aversions
is hard. Usually very long questionnaires have to be compiled by user but for our aim we created a shorter
version of the questionnaire starting from state-of-art clinical tests. Then we need ratings for specific point of interest
and again we used a questionnaire for evaluation of 50 places in Torino city centre in 1-5 Likert scale
All these data can then gathered by using PIUMA app when available
Then, we need a model of the place: in particular we need to collect the place's sensory features. And again is not simply
to obtain this information because they
are not present in common open data. So we need to use an ad-hoc crowdsourcing platform, called Maps4all,
that can be used by users to rate places according to their sensory features, so for example
crowding, level of noise, smell, and so on
But we cannot ask user aversion for each value features. we need to ask a
limited number of questions and then we need to interpolate values in order to
estimate the aversion for the missing value. For some feature, for example noise, low values do no trigger aversion,
while high values do. Aversion can be approximated as a linear function. For other features, for example brightness, the user may be challenged both by low or high value. So, aversion can be approximated
by a concave function of the range of values.
Then, we will show the results of a preliminary evaluation of such an algorithm. We have two groups of users: 20 adults with autism
and 128 neurotypical subjects that
have to rate 50 points of interest
in the city of Turin and in this offline evaluation we found
that our model
achieved the best performance by evaluating compatibility
as the cosine similarity between the compatibility of the item features with that of an ideal item which minimise user aversion.
OK. This is of course an on going work. We
start from these results and when we have to modify the algorithm trying
to suggesting only things to do that are somehow similar to what people already know and like
Then we need to perform another evaluation in real cultural heritage scenario
and we are working on add such personalized save paths and tours as well as some
tips and social story to train travelling skills such as how to get a train, how to get a plane and so on..
Then, finally we plan to investigate the adaptation of our approach to other needs for example related to motor disability
by extending the type of features which affect the compatibility of an item.
Thanks for your attention! For more detail on the recommender system and its evaluation see the full paper presentation.