With the end of the first phase of data collection for evaluating SAM, the team had some time to improve the interactive software in SAM. This software is aimed at engaging the children with the assessment, delivering the story stems and supporting the children throughout the whole assesment. A few changes have been made since we have collected data with the previous version into schools. First, we have introduced a new character that will talk to the children during the whole session. Of course, its name is SAM and this cat will be more than happy to help chidren as they interact with our software! We also added an introductory interactive sequence which, we hope, will help children feel cumfortable with the whole procedure. Our goal is to make SAM the more autonomous possible! It will help collecting better data and make the test even easier to administrate!
The team had the opportunity to exhibit this new version at the scientific conference on Interaction Design for Children 2017. This conference is the main venue for all the scientists, the designers and the educators interested in the design of interactive software for children. This year the conference took place in the prestigious University of Stanford in California. We were happy to see so many attendees interested in attachment and in our SAM application. It was a great pleasure to answer all the questions and show what we have designed so far! We will be back next year to show our progress and hopefully present our work in the main track of the conference!
Maki has been at Wester Cleddens Primary School’s Sports Day to say thank you for their wonderful help with SAM Study. A happy, fun afternoon to see the lovely children and their class teachers (pictured). Thank you Wester Cleddens and all the other schools that have helped us in the first phase of the study and we wish you a great summer holiday!
In a previous post, we have discussed how behavioural cues can help us identifying attachment patterns. In this post, the hypothesis that some behavioural cues could be expressed by the way children manipulate the dolls during the story telling was also evoked. To investigate this, we needed to find a way of collecting some data from children that could describe the how the dolls were handled during the play. As we mentionned before, we decided to make our dolls smart so they can communicate some information about their own state. For example, they could notify the SAM application when they are touched, when they are squeezed, or even when they are moved. Because we were also curious about the relationship between the two dolls as it is an important hint in MCAST assesment to categorise attachment, we decided to start with looking at the dolls movements. In this project we are mainly interested in clues that are tied to children’s doing. Therefore, integrating sensors into dolls to track children’s interaction with the toys was the obvious path to take.
Creating toys for children is a challenge and requires desginer skills. Unfortunately, our team is only composed of computer scientists, cognitive development psychologists and chilren psychiatrists. So we ordered a few different woden dolls from a toy store and dismantled them in order to understand how they are built. Fortunately fo us, the simplest model was made out a few elementary wooden pieces for the head and the trunk, and chords by way of arms and legs. The rectangular shaped woden body caught our attention as it reavealed a convenient location to hide some electronic parts that could sense movements. The next step was to find a computer that could suits our requirements.
This past decade has seen computer size decreased tremendously. This has allowed the design of wearable electronic devices possible such as smart watches or fitness tracking bracelets. Another trend that has grown quickly is rapid protyping. Several platfoms such Arduino provide software tools and electronic hardware to prototype new computerized systems that can sense pretty much anything depending on the sensors that are involved. In addition, access to affordable 3D printers has opened a door for on-demand design facilitating even more rapid prototyping. The real challenge for us was to find a computer and sensors small enough that could fit in a box of the size of dolls’ body. After an exhaustive research, we eventually found a company called Tinycitcuits that design tiny Arduino based computers and compatible sensor and communication modules with the same size factor. The next stage was to find a way to attach the computers to the dolls. Luckily for us, Jason an intern in our research group who had some experience in 3D design and printing, designed some plastic shells to protect the computers. These shells were specifically designed to welcome the head and the limbs of the dolls. The picture above shows the smart doll prototype we used to collect the data during the first phase of the project. In the next posts, we will cover in more details what is inside the shells.
In SAM, our objective is to identify attachment patterns to eventually categorise the attachment status of a child given a caregiver. By doing so, we will be able to support medical practitioners to focus their efforts on children that needs attention.
In order to identify attachment patterns, we are looking into behavioural cues from children when they play with our SAM game. We think that one of these cues could be hidden in the way children are manipulating the dolls while they enact their story. To verify this hypothesis, we had to look for a non intrusive mean of observing how children manipulate the dolls in the game.
There are several approaches we could follow. For example, we could measure the position of the dolls into space by analyzing the video recordings we are already collecting for facial behavioural cues. However, this option has many drawbacks. First, it is difficult to get continuous location of the dolls as they may be hidden by children’s hands or the furnitures on the mat. Another challenge is that this approach would require specific computer vision algorithms to identify the dolls in the video recordings, which would take time to design with unpredictable results at the current stage of the project. A more promising option would be to collect data directly from the dolls and get around the limitations of the computer vision strategy.
While sounding great, collecting information directly from the dolls rises many questions. To begin with, what the dolls should tell us? There are so many different type of data we could collect and analyse. For instance, like in computer vision, we could measure the spatial configuration of the doll in space. Measuring the pressure applied on the doll during the game could be another behavioural cue. We could also collect bioemetric data such as skin conductance or heart beat like many smart watches and sport bands already do. Unfortunately, time is a critical resource and in this project we won’t be able to investigate all the possible ways offered to us.
Then, the next question would look into what sensors to integrate to our toys. Fortunately, the high constraints of size and autonomy has guided us to get a good idea of what we needed. Finally, how the dolls should talk to us? This last question is as essential as the previous ones. The way the data are transfered from the dolls to the computer could affect the game experience. For example, if the dolls were connected to the computer using a cable, children would not be able to move the dolls freely during the game degrading the game experience overall. However getting rid of cables is not trivial. This raises again other questions: should we transfer data to the computer as the children are playing the game or should we store the data locally in the dolls and then transfer them to our secure storage server after the children are done playing? Cableless also means that our toys would need to be selfpowered. But what would be the impact of either data transfer solution on the energy consomption and the battery integrated to the dolls?
This post gave a quick overview of the challenges to take into consideration for designing artefacts to help us investigating new ways of measuring attachment. The next post will bring a few insights to overcome these challenges with a preview of our smart dolls prototype.
We have finally collected over 60 samples! While this number does not seem to be very large, it required a lot of efforts to reach this milestone. Because we are designing an automated attachment measurement tool, we need to measure attachment with the existing psychiatric assessments to compare the results with the results of our SAM tool. Therefore, each child in our study had to play with us twice with 8 weeks of intervals between either MCAST or SAM assessments.
Another challenge was getting into Glasgow’s primary schools to collect data. The study requires a lot of equipment such as a large black screen, some tripods, some video cameras, a big laptop, a few computer accessories, some toys and a doll house. Bringing all these items with us each time is laborious as we collected data in several schools at the same time.
It is worth noting that we could not have reached this milestone without the support of the parents and the schools we are visiting. We would like to warmly thank the parents, the children and all the kind teachers and staff members at schools that helped us making the study organisation straightforward.
While it has been an intensive effort, we were delighted by the children’s reactions to our study. They were all so excited to participate and they left our experiment with joy and a big smile everytime! There were some children asking in the school when they could try our games and even some that had done the study came back later in the day to tell us how much fun they had playing with us! These little things also help to keep us motivated to knock out as much work as we can to complete all the tasks that are left.
So, where do we go from here? In an immediate future, we will start analyzing the data we have just collected, manually, as psychiatrics do, and automatically using computers and artificial intelligence. In order to make the computer smart enough to measure attachment, we are going to design some specific algorithms that will use the data we have collected to determine the attachment status of the participant. This will greatly help doctors tofocus on patient in need as we hope that SAM will identify the different attachment categories. We have also collected feedback from children about our game. All the feedback were great and it will allow us to make SAM even better for the next round of data collection!