Author: Aishat Aloba

In previous posts, we have discussed our ongoing work on understanding the differences between child and adult motions to improve recognition of children’s motions. My paper, “Tailoring Motion Recognition Systems to Children’s Motions”, was accepted to the 2019 International Conference on Multimodal Interaction (ICMI) Doctoral Consortium! The doctoral consortium provides an opportunity for PhD students who are at the stage of proposing their dissertation to share their dissertation plans with outside researchers and receive feedback. The paper focuses on my ongoing work and future research plans for my doctoral dissertation. Here is the abstract:

Motion-based applications are becoming increasingly popular among children and require accurate motion recognition to ensure meaningful interactive experiences. However, motion recognizers are usually trained on adults’ motions. Children and adults differ in terms of their body proportions and stages of development of their neuromuscular systems, so children and adults will likely perform motions differently. Therefore, motion recognizers tailored to adults will likely perform poorly for children. My PhD thesis will focus on identifying features that characterize children’s and adults’ motions. This set of features will provide a model that can be used to understand children’s natural motion qualities and will serve as the first step in tailoring recognizers to children’s motions. This paper describes my past and ongoing work toward this end and outlines the next steps in my PhD work.

We will post the camera-ready version as soon as it is available. The ICMI 2019 conference will be held in Suzhou, China in October.

I am a fourth year PhD student at UF. My participation at the ICMI doctoral consortium will allow me to present my research ideas, receive feedback from researchers with varied experience and expertise in research, and network with peers and mentors, who can help with my development, both academically and professionally.

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In a previous post from a few years ago, we mentioned that our findings on the Pose project established that there were perceivable differences between child and adult motion. Our next steps were to quantify what these differences actually were. As a first step to investigating these quantifiable characteristics, we concentrated on temporal and spatial features commonly utilized in the analysis of gait (i.e., walking and running) to analyze the walking (walk in place, walk in place as fast as you can) and running (run in place, run in place as fast as you can) actions in our Kinder-Gator dataset. Our paper presenting this analysis, titled “Quantifying Differences between Child and Adult Motion using Gait Features,” was accepted as an invited paper to HCII 2019: the International Conference on Human-Computer Interaction. The paper details our analysis of nine features with respect to age group (child vs. adult) and actions (walk, walk fast, run, and run fast) and the implications of our results with respect to the design of whole-body interaction prompts and improvement of recognizers for whole-body motions. Here is the abstract:


Previous work has shown that motion performed by children is perceivably different from that performed by adults. What exactly is being perceived has not been identified: what are the quantifiable differences between child and adult motion for different actions? In this paper, we used data captured with the Microsoft Kinect from 10 children (ages 5 to 9) and 10 adults performing four dynamic actions (walk in place, walk in place as fast as you can, run in place, run in place as fast as you can). We computed spatial and temporal features of these motions from gait analysis, and found that temporal features such as step time, cycle time, cycle frequency, and cadence are different in the motion of children compared to that of adults. Children moved faster and completed more steps in the same time as adults. We discuss implications of our results for improving whole-body interaction experiences for children.


Interested readers can find the camera-ready version (preprint) available here. The HCII 2019 conference will take place in Orlando, Florida from July 26 – July 31 during which I will be presenting the paper.

Working on this paper advanced my knowledge of the analysis of gait, and improved my understanding of human movement for both children and adults. I am looking forward to presenting the paper at the conference as the conference will provide an avenue to gain valuable feedback from the audience regarding the conclusions of the paper.

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In our previous post, we mentioned that we published the Kinder-Gator dataset, which contains the motions of 10 children and 10 adults performing motions in front of the Kinect.  Currently, we are exploring recognition of whole-body motions in the dataset. Since we are focusing on whole-body motions, we would like to concentrate on motions in which movement involves one or more limbs in the body. Hence, we only use a subset of the motions in Kinder-Gator, since it also includes motions that just involve hand motion or body poses. To test the performance of their $1 unistroke gesture recognition algorithm; an algorithm designed to help incorporate stroke gestures into games and UI prototypes, Wobbrock et al. [1] defined a representative set of 16 unistroke gestures (e.g., a triangle, an X) that are useful for these applications. Similarly, we want to define a representative set of motions that encompasses the unique combinations of upper and lower limb movements in our dataset. This representative set will be used to evaluate the performance of the recognition algorithms we are currently exploring for whole-body motions. In this blog post, we discuss the steps we are taking to define the representative subset from motions in Kinder-Gator:

  1. EXCLUSION: We are excluding motions that involve drawing shapes and symbols, and motions that involve making symbols and shapes with the body that Kinder-Gator includes. We are excluding the drawing motions because these motions usually involve the movement of the hand or wrist while the rest of the body remains static. Hence, these motions are not good representatives of motions involving the whole-body. Furthermore, we are excluding these motions because they are intended for the recognition of the shape or symbol being performed, rather than the recognition of the motion in its entirety. Examples of motions from the Kinder-Gator dataset that fall within this category include: “Draw the letter A in the air” and “Make the letter X with your body”.
  2. CHARACTERIZATION: As mentioned earlier, we want our representative subset to be unique in terms of upper and lower limb movement. Hence, in this step, we are characterizing motions in terms of the dimensions of movement of the upper and lower limb, and we exclude motions that are too similar in their dimensions of movement, to avoid collisions. To accomplish this, first, we are excluding motions that are mirrors of other motions, since the motions being performed are the same, just with the opposite limb. For example, the motion ‘wave your other hand’ is a mirror of the motion ‘wave your hand’ so we exclude the mirror. Next, we are characterizing the movement of joints in the upper limb (hand and shoulder) and lower limb (knee and foot) along the horizontal x, vertical y, and depth dimensions. By doing this, we expect to identify motions that are similar in their upper and lower limb movement for the next step.
  3. SELECTION: Finally, to identify the final representative subset of motions, we are grouping motions that are similar based on the characterization in the previous step. That is, we are grouping motions that are similar in terms of their upper and lower limb movement. The groupings resulted in 16 groups wherein each group contained a unique combination of upper and lower limb movement. Afterward, we will choose one motion from each group to form the representative subset of motion. Our next step is to use these motions to test the performance of existing recognition algorithms, and then adaptations or new algorithms as well.

Working on the POSE project has been very interesting and has allowed me to gain a better understanding of recognition algorithms. I look forward to gaining more knowledge as I progress further in the project.


  1. Wobbrock, Jacob O., Andrew D. Wilson, and Yang Li. “Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes.” Proceedings of the 20th annual ACM symposium on User interface software and technology. ACM, 2007.
  2. Anthony, L., & Wobbrock, J. O. (2010, May). A lightweight multistroke recognizer for user interface prototypes. In Proceedings of Graphics Interface 2010 (pp. 245-252). Canadian Information Processing Society.
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I just completed my qualifying exam and in this blog post, I will detail what I think I learned from the qualifying exam (quals) process. In the CISE department at UF, the qualifying exam is the first milestone used to assess the student’s ability to successfully complete the Ph.D. program. This exam involves a literature survey of the student’s field of research. For my qualifying exam, I wrote a survey paper titled “An Overview of the Effectiveness of Exertion Games (Exergames) for Children.” I focused on this topic since my research interest involves exertion games (games that promote physical activity) for children. Below are some of the major lessons I learned from the writing process.

  1. START EARLY: The process of writing the qualifying exam involves dedicating a large amount of time, so it is important to identify several semesters before when you would like to write the qualifying exam and inform your advisor and committee members about your decision. It is also important to identify the papers you would like to review and start reading these papers early.
  2. PLAN ACCORDINGLY: The semester I decided to write my qualifying exams, I also attended several conferences and took one class. I found out this was not ideal as this took away from the time I should have used to focus on my quals.
  3. IDENTIFY YOUR RESEARCH QUESTION/CONTRIBUTION: You should review prior surveys in your field of research to identify research questions that are novel. Identifying your research question/contribution would also ensure that your survey is focused and not too broad and will help in gathering papers that can help answer these research questions.
  4. IDENTIFY A METHODOLOGY: It is very essential that the papers reviewed in your survey were gathered using a systematic approach, as this will ensure that your survey is grounded and can be reproducible. Furthermore, it will help others understand why the papers in your survey are the only ones you selected. To ensure that your methodology is good, make sure you formulate your research questions/contribution, identify your inclusion and exclusion criteria, select your keywords, and identify the sources you want to focus on. To structure my methodology, I looked at methodologies employed in other surveys in the exergaming field and from quals written by my peers.
  5. STRUCTURE YOUR PAPER LIST: When creating your paper list, ensure that you have a preliminary structure to group these papers as this will help make sure your survey is on the right track. Furthermore, these groupings can be modified and transformed into sections in your survey.
  6. ORGANIZATION: I cannot overemphasize the importance of ensuring you organize your papers, as this makes the writing process easier. I organized my papers by creating an Excel table that details each paper, its findings, and its contributions. Organizing your papers not only helps to ensure that you are capturing the important points mentioned in each paper, but it also enables you to have a top-down view of all the papers to be reviewed so you can ensure that your survey properly answers the research questions.
  7. YOUR PEERS ARE A RESOURCE: For most students like me, the qualifying exam is most likely the first time you are writing a survey paper, which may seem overwhelming. A useful resource in this situation is your peers who have gone through this process. They know what you are experiencing since they have also been through the process. Hence, you should seek their advice on issues you are facing, look through their exams to familiarize yourself with the writing process, and send drafts so they can provide constructive feedback. By doing this, you will be able to address a lot of comments that would have been identified by your committee.
  8. HAVE A MAJOR TAKE-AWAY: This was one of the mistakes I initially made in my survey. I was trying to capture different ideas which made it difficult for my committee to understand the major takeaway from my survey. Furthermore, the structure of my survey made it more difficult, as it did not match the contribution I was emphasizing. Using the feedback from my committee as a guide, I identified the major idea I wanted readers to take-away from my survey and structured my survey around this idea. Once I did this, I found out that the writing process became easier.
  9. WRITING IS AN ITERATIVE PROCESS: I could remember when I first got the feedback from my committee stating “major reviews.” I felt really downcast because I thought I did not write a good enough paper and I had to rewrite my paper again. However, after speaking to my committee members and thoroughly studying my reviews, I came to the realization that writing is an iterative process and I did not write a bad paper, I just needed to make changes to make the contributions of my survey clearer. Therefore, it is important to realize that as this is the first-time writing a survey paper, you may not get it right the first time, so iterate on it as much as possible because at the end you would have an outstanding paper. Furthermore, one of the main goals of the qualifying exam is for students to be able to submit the survey paper to a journal, so this process will also help strengthen your submission and improve your chances of acceptance.

To conclude, the qualifying exam writing process takes a lot of time but at the end in my view, it is worth it, because you learn a lot during the process. In my case, I gained an understanding of what has been done in the exergaming field and what is yet to be done, which helped to clarify my contributions on the FunFitTech project with respect to designing exergames for children. The qualifying exam process also helps you become a better writer and a more informed researcher in your field.

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In a previous post, we discussed conducting a study in which we used the Kinect to track the motions of ten children and ten adults performing whole-body gestures, for example, wave your hand and jumping jacks. From this study, we created a dataset of the whole-body gestures. Our paper titled, “Kinder-Gator: The UF Kinect dataset of Child and Adults Motions,” was accepted as a short paper to the Eurographics 2018 conference; a premiere conference that showcases innovative research in computer graphics. The paper details the gestures in the dataset, the data collection, and example applications of the dataset in animation, recognition, and human motion characteristics. Here is the abstract:

Research has suggested that children’s whole-body motions are different from those of adults. However, research on children’s motions, and how these motions differ from those of adults, is limited. One possible reason for this limited research is that there are few motion capture (mocap) datasets for children, with most datasets focusing on adults instead. There are even fewer datasets that have both children’s and adults’ motions to allow for comparison between them. To address these problems, we present Kinder-Gator, a new dataset of ten children and ten adults performing whole-body motions in front of the Kinect v1.0. The data contains RGB and 3D joint positions for 58 motions, such as wave, walk in place, kick, and point, which have been manually labeled according to the category of the participant (child vs. adult), and the motion being performed. We believe this dataset will be useful in supporting research and applications in animation and whole-body motion recognition and interaction.

Interested readers can find the camera-ready version of the paper (preprint) available here. I presented the paper at the conference which took place in Delft, Netherlands.

This was my first time presenting at any conference and at first, I felt very nervous about the idea of giving a conference talk. However, after practicing repeatedly in front of my advisors and peers, I became more confident and gave a successful talk at the conference. It was also my first time attending the Eurographics conference and visiting Delft, Netherlands. Overall, I really enjoyed interacting with other researchers and enjoyed listening to talks about the state of the art research in the graphics community. I also loved that the conference included a city tour in which I got to learn about the history of Delft and the role it played in the history of the Netherlands.

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Last spring, I participated in a project in a game design course. The project involved transforming a board game, previously designed by the University of Florida Center for Aquatic and Invasive Plants, to a mobile game. The objective of the project was for children to learn about the effects of invasive species on their real-world environment. At the end of the course, we submitted a work in progress paper discussing our design process. Our work in progress paper titled, “From Board Game to Digital Game: Designing a Mobile Game for Children to Learn about Invasive Species,” was accepted at CHI PLAY 2017, an international conference that combines all areas of play, games, and human-computer interaction (HCI). The paper focused on the design of a mobile strategy game that teaches children about the differences between native, non-native, and invasive species, and the mechanisms used to control invasive species. Here is the abstract:

Invasive species are species that cause economic and ecological harm and/or harm to human health. One challenge to managing invasive species is the lack of awareness about these species and the threats they pose. To mitigate this problem, the University of Florida Center for Aquatic and Invasive Plants developed a classroom board game for children to learn about trade-offs in managing invasive species. The game is effective in increasing knowledge about invasive species and promoting collaborative discussions. However, this board game is only accessible within the classroom. We created a mobile digital game that expands on the goals of the board game. In this paper, we discuss the design of the board and digital versions of the game, and provide some guidelines for designing digital learning games that address real-world problems that have no optimal solution, like the management of invasive species. Future work will evaluate the effectiveness of the digital game in enhancing children’s knowledge about invasive species.

We will post the camera-ready version of the paper soon. This year’s CHI PLAY conference will be held in Amsterdam, Netherlands, and I will be presenting a poster at the conference.

I am a 3rd year PhD student working on whole body interaction and movement based games for children. I am looking forward to attending the talks and sessions at the CHI PLAY conference. I believe these talks will help improve my understanding on creating better technologies for children.

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In our previous update, we were in the process of conducting affinity diagramming sessions to walk the data collected from our focus group sessions with children. Based on our affinity diagram, we extracted themes and brainstormed design ideas for the motion-based exercise game. We next consulted with several domain experts, that is, gym and physical education teachers, to better understand what components of exercise to include in the game. We met with them over the course of several weeks and covered topics such as how they structure, assess, and motivate their classes’ physical activities. The responses from this session will help us ensure that the design ideas we come up with for the game satisfy needs for physical exertion in approved ways and motivate children to stay active. Currently, we are using affinity diagramming to understand the themes that emerged from these design consultations. These themes will be compared to those generated from the children’s focus groups to validate/expand upon our previously brainstormed design ideas.

Working on the FunFitTech project has been an informative experience as it has introduced me to the field of movement-based games, which is one of my research interests. Also, it has reinforced my knowledge on UX design concepts such as focus groups, affinity diagramming, and brainstorming design ideas. Furthermore, I improved my leadership abilities after experienced being a project leader who makes decisions on the next steps for the project.

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In our previous update, we presented our demo prototype of a motion-based exercise game to children ages 5 to 10 for feedback during three focus group sessions. The prototype presented was used as a design probe to help children understand our game concept, thus inspiring them to generate design ideas ranging from what they like and dislike in the current prototype to what features can be improved upon or added to the game. This method of design follows from research by Allison Druin [1], where she discussed the benefits of involving children in the design when developing technologies for children. Currently, we are using affinity diagramming to walk the data collected in our focus groups to understand how to improve our prototype to better motivate children in our target age group to maintain a more active lifestyle while having fun with the game.

I started on this project this semester, and am enjoying every bit of it because it embodies my research interests in whole-body interaction and movement based games. Furthermore, I can apply concepts I learned in my User Experience and Design course, Human Computer Interaction course, and Game Development course which makes the project experience more fulfilling. I am looking forward to improving the prototype and creating a game that children can enjoy.

[1]. Druin, Allison. “The role of children in the design of new technology.” Behaviour and information technology 21.1 (2002): 1-25.

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In a previous post on the MTAGIC project, we presented results of a study that found that interface complexity (simple, abstract interface vs. complex interface) affected children’s performance of some touch interactions and did not affect gesture interactions on smartphone devices. Recently, we have been extending this project to identify the differences in how children perform target touching and gesture drawing tasks when interacting using different input devices. We so far have conducted a user study and have been analyzing and documenting our findings. We are particularly interested in how similar or different our results were to the previous results from the MTAGIC project. We are currently in the process of writing a paper that provides detailed results, and discussions of our findings.

This project started as a class project for a research methods class. Working on this project has been an informative, challenging, and a good learning experience. This project helped broaden my understanding on the topics taught in the class such as descriptive statistics, and experimental designs. Furthermore, it has helped enhance my skills in performing statistical analysis on data from different types of experimental design. A major challenge I faced while analyzing the data was trying to figure out the right anova analysis syntax in R for mixed, repeated-measure experimental design; which was our experimental design, and this project has enabled me to challenge myself in learning to understand and overcome problems. I plan to apply the knowledge gained when performing statistical analysis on data in future research projects.

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In our previous post, we mentioned that our paper “is the motion of a child perceivably different from the motion of an adult?” will be published in the Transactions on Applied Perception (TAP) journal. The paper focused on investigating if naïve viewers can tell the difference between adult and child motion through a two-alternative forced choice survey. We found that naïve viewers can identify a motion belonging to a child versus that belonging to an adult significantly above chance levels. Furthermore, we found that the type of action (e.g., jumping jacks, walking, running) being performed affects the accuracy of people’s perceptions of children or adults, possibly due to coordination or other cues. From these findings, we want to investigate what are the quantifiable characteristics of the motions that can explain the differences between perception of child and adult motions.

Working on the POSE project has been an exciting and informative experience for me in the first year of my PhD program. It has introduced me to the rich field of whole body interaction, which has become the focus of my research. I also plan to apply this knowledge as I dig deeper into the field of whole body interaction and movement-based games for my thesis work.

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