Designing for Older Adults

Designing for Older Adults

In the photo above: Mariko Kamiya (left) with Giovanni Garcia (middle) and Taylor Day (right).

For my DREU internship at UIC, I worked on a research study that aims to improve the accessibility of mobile map applications for adults aged 65 or older. I came up with the idea from watching my parents and grandparents struggle to use all the functions available to them from their smartphones and tablet devices. Most of their problems stemmed from overly complicated user interface design. I knew that map applications in particular could be extremely useful, especially for increasing older adults’ mobility, this individuals are unfairly handicapped if they do not know how to use such navigation applications. Based on informal observations of my parents (aged 55 – 70 years old) using Google Maps on their phones, I knew that Google Maps seemed to rely on its users intuitively knowing how to use the application, but older adults often have less experience using smartphones and thus are less comfortable using mobile applications that someone younger. After reviewing some of the literature in the field, I realized there was little research on how to design mobile applications for older adults, and there was almost no research on the design of mobile map applications for this demographic. So, I proposed a research study that aims to look at the challenges faced by older adults when using map applications on touchscreen smartphones and tablet devices in order to come up with a more accessible map application for this demographic. Professor Chattopadhyay and my colleague Taylor Day helped turn this idea into an actual research proposal that we could submit to the UIC Institutional Review Board, and a couple weeks later we were ready to begin recruiting! From the IRB submission process alone I learned a lot about how much preparation is required for studies involving human subjects, even if the study procedures are as non-invasive as asking participants to complete some simple tasks on a smartphone application.

After receiving IRB approval, Taylor and I began officially recruiting participants, which proved to be more difficult than we thought it would be. We put up flyers, sent out emails, and contacted any family, friends, and acquaintances that we had in the area. Since Taylor and I are not from Chicago, neither of us had many contacts in the city, which made the recruitment process more difficult. I know that if we had done the study in the San Francisco Bay Area, where I grew up, I would have been able to contact a number of family and friends eligible for the study. Despite our lack of Chicago contacts, we ultimately interviewed twelve participants and had enough qualitative data to come up with a set of design guidelines to improve the accessibility of mobile map applications for older adults.

When I was not out doing participant interviews with Taylor, I spent my time learning how to use the Google Maps Javascript API with the goal of being able to build a custom mobile map application using this API. I watched countless video tutorials on both how to use the API and Javascript in general, because this was my first time using Javascript, HTML or CSS. In the last two weeks of the ten-week internship, we began actually building the prototype for our map application that takes into account the design guidelines we came up with as a result of the user studies.

The hardest part about building the app was learning how to work around the limitations of the Google Maps API, because some features that would have been easy to implement in a simple web page (such as adding a transparent overlay to the map) were non-trivial to complete while still retaining the functions of the API. Fortunately, we figured out how to execute the three main features we came up with based on the user interviews. These were: include a step-by-step procedure to get directions to a place, incorporate landmarks into the visual route, and keep certain menu options visible to the user at all times (no hidden menus). We also tried to incorporate Taylor’s probability algorithm that updates the list of destination options during an exploratory search based on the user’s pan and zoom movements on the map, but we did not have enough time to figure out how to incorporate her algorithm into the listview of place results. Her algorithm is, however, running in the background. The code for the function that creates a listview of place results from the exploratory search would have to be completely rewritten in order for it to be compatible Taylor’s code for her algorithm. It is certainly possible to do, but the task required more time than we had before the DREU internship ended. I realize now that I should have waited until Taylor had finished writing the Javascript for her algorithm before writing the code for the listview of search results. One lesson learned – coding in parallel can be efficient but also counterproductive if the pieces of code written rely on entirely different functions.

Please click the link below if you’d like to watch a short demo of the prototype we demonstrated on July 27.

https://drive.google.com/file/d/0Bygy8SfYvGjtVE05QWRzUFVFYXc/view?usp=sharing

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Calendar

November 2017

Sun Mon Tue Wed Thu Fri Sat
1
  • CS 401 - Computer Algorithms I
  • Group meeting
2
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
3
4
5
6
  • CS 401 - Computer Algorithms I
7
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
8
  • CS 401 - Computer Algorithms I
  • Group meeting
9
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
10
11
12
13
  • CS 401 - Computer Algorithms I
14
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
15
  • CS 401 - Computer Algorithms I
  • Group meeting
16
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
17
18
19
20
  • CS 401 - Computer Algorithms I
21
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
22
  • CHI Rebuttal due
  • CS 401 - Computer Algorithms I
  • Group meeting
23
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
24
25
26
27
  • CS 401 - Computer Algorithms I
28
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I
29
  • CS 401 - Computer Algorithms I
  • Group meeting
30
  • CS 412 - Introduction to Machine Learning
  • [Hai] CS 521
  • CS 412 - Intro to Machine Learning
  • CS 491 - Virtual and Augmented Reality
  • CS 583 - Data Mining & Text Mining
  • CS 401 - Computer Algorithms I