Adaptive UI/UX for Smart Geriatric Users
The impact of cognitive abilities on user experience
Overview
In today’s rapidly advancing digital landscape, the accessibility of technology for all demographics is a fundamental challenge. This project seeks to address the pressing needs of geriatric users—individuals aged 60 to 75—by reimagining the user experiences of two dominant apps in India: Google Pay and Uber.
By conducting extensive research and implementing innovative design strategies, we aim to create a seamless and empowering experience for older adults, enhancing their independence in a tech-driven world.
Through sentiment analysis of reviews, psychometric tests, and eye-tracking research, the app dynamically adjusts based on user interaction and cognitive abilities. The redesign also incorporates map-based pickup zones, voice assistants, personalized color schemes, and session-based face lock to enhance usability and security.
Understanding the User
Crystallized Intelligence
Represents the accumulation of knowledge, skills, and experiences over time.
Example: A geriatric user excels at remembering detailed processes like managing finances or navigating familiar routes using digital maps because of their extensive life experience.
Fluid Intelligence
Refers to the ability to solve new, unfamiliar problems and adapt to novel situations.
Example: A senior user might struggle with quickly understanding a newly designed app interface or adjusting to unexpected changes in a digital payment system, as this requires quick problem-solving and adaptability.
Geriatric users have low fluid intelligence but have high crystalline intelligence. This strength should be used to provide a better experience with technology.
The research was categorized into 3 sections. The first thing I researched about was the issues faced by users, then about how user data is being collected for testing the usability of different apps and finally about the methods used to help users have a better experience with technology.
We were able to list out several issues. These issues were identified through a literature review of 105+ research papers in Human-Computer Interaction (HCI) and in-person user surveys and testing involving 92 geriatric participants.
Data driven design
Data-driven design enhances user experiences by utilizing quantitative data and qualitative insights. This approach prevents ineffective designs that can lead to revenue loss and supports business growth. While data guides design decisions, it does not replace creativity. Visualizing data aids in understanding and communication, ensuring effective information flow and clear goals. By balancing different data types, designers can create comprehensive insights and formulate hypotheses for UX experiments, leading to user-centered design solutions.
Eye-Tracking Software Analysis
We employed eye-tracking software to examine the current usability of the apps. The primary goal was to observe how easily users could locate and access frequently used features, particularly focusing on senior users.
Sentiment Analysis of Play Store Reviews
Reviews from the Play Store were analyzed with sentiment analysis, segmented by the age of users. This helped in identifying common challenges and issues faced by older users, highlighting areas that required improvement.
Surveys and Interviews
Before starting the project, we conducted both in-person and online surveys and interviews using convenience sampling. This allowed us to gather direct feedback from our target user group, helping to identify the most prevalent problems and areas for enhancement.
Eye-Tracking with Attention Insight
What is Attention Insight and how does it work?
Attention Insight leverages AI-driven predictive analytics to simulate human eye-tracking, producing highly accurate Attention Heatmaps. These heatmaps highlight which areas of a screen are likely to capture user attention. The tool is built on a robust dataset, including over 5.5 million fixations and validated through MIT's saliency benchmark, achieving up to 96% accuracy. This technology allows designers to evaluate and refine their layouts before launch, ensuring optimal user engagement and performance.
Application in My Project
- Platform Analysis: Used Attention Insight to study Google Pay and Uber, aiming to enhance senior citizen usability.
- Focused Assessment: Analyzed heatmaps to identify high-attention areas and elements needing refinement.
- Usability Enhancements: Made targeted design adjustments to improve navigation and interaction for elderly users.
- Outcome: Helped create a more intuitive, accessible experience tailored to the needs of senior citizens.
Analysis
Sentiment Analysis of different app reviews
To gain deeper insights into user behavior, we analyzed an Indian-based dataset of reviews from popular apps such as Google Pay, Paytm, PhonePe, and Uber. These applications are prominent in the Indian market, making them ideal for our study. We conducted sentiment analysis on the review data to identify key features and areas for improvement, which informed our capstone project. This comprehensive analysis helped us understand user sentiments and optimize the design for better usability. This helped us understand which app needed the most amount of issues.
Analysing data collected through interviews and surveys
We distributed surveys to geriatric users to gather their feedback on the selected apps. This initiative allowed us to collect valuable data on their experiences and opinions, which helped us refine the final features and user flows for our project. A total of 91 individuals participated in the survey, providing insights that were instrumental in enhancing usability for our target demographic.
This data helped us understand our user group and also predict the problems people faced. If person faces an issue with A, how likely are they to face an issue with B.
Outcome of the research
After analyzing the data, we identified key issues:
- Accessibility Challenges: Users with visual impairments, particularly those with color blindness, struggled to navigate due to poor color contrast and design.
- UI Confusion and Anxiety: The complex interface caused confusion, especially for users with tech anxiety. Fear of errors in online banking led many to avoid the platform.
- Navigational Difficulties in Commuting Apps: Users found it hard to input pick-up/drop-off locations accurately, leading to wasted time and money.
The main algorithm implemented:
To enhance the user experience and functionality of the app, the following improvements have been implemented:
Initial Psychometric Test
Users begin by taking a psychometric test, allowing the app to personalize their experience by identifying and addressing their unique challenges.
Frontend Replication and Responsiveness
The app replicates the intuitive designs of Google Pay and Uber, ensuring familiarity while making the entire frontend fully responsive. Built using Python Flask and HTML/CSS/JS, it adapts seamlessly to various devices and screen sizes.
Optimized Features
- User Interaction Tracking: Every button click is tracked, providing valuable insights into user behavior and preferences.
- Personalized Color Schemes: Based on the psychometric test results, users are provided with personalized color schemes to enhance accessibility and comfort.
- Dynamic Button Sizing: Frequently used buttons grow in size, making them easier to find and interact with, improving usability.
- Interactive Highlights: Buttons can be highlighted based on user preferences, offering visual cues for better navigation.
- Face Lock Security: Advanced security is provided through face recognition, where each new face creates a unique session, reducing risks for geriatric users.
- Voice-Driven Navigation: Users can simply state their tasks, and the app navigates and completes them autonomously, enhancing convenience.
- Customizable Pick-Up/Drop-Off Radius: Users can define their travel radius by selecting a polygon on a map, offering greater control over their commuting preferences.
- Seamless Redesign: The UI is streamlined to show only essential information, reducing cognitive load and simplifying navigation.
- Street View Integration: A street view feature helps users leverage visual memory for better place recognition and ease of navigation.
Psychometric Test Integration
The app’s initial psychometric test ensures a highly personalized and optimized user experience, tailored to individual needs and preferences.
This combination of features results in an app that is secure, intuitive, and highly responsive to the diverse needs of its users, especially those with accessibility challenges.
Psychometric Test that the user takes up before interacting with the app
Enhanced Features and User Experience Improvements:
Pay Contacts Page:
- Voice Assistant Integration: Users can simply speak the payment amount, which is automatically entered into the field. This reduces the need for manual typing and minimizes errors.
- Confirmation Process: Once the amount is entered, users confirm the transaction by clicking the "Confirm" button, ensuring a straightforward and user-friendly payment process.
Google Pay Home Page:
- Voice Assistant Button: A dedicated voice assistant button is positioned on the top left of the screen. This feature enables users to access app functionalities through voice commands, providing a hands-free experience.
- Dynamic Button Placement:
- The most frequently used feature for each user is highlighted as the top button in the grid of 8 options, making it easily accessible.
- The most recently used feature is also placed prominently, ensuring that users can quickly return to their recent tasks.
- For returning users, the top two buttons dynamically adjust, with the most used feature being prioritized, followed by the most recent activity.
Enhanced colour contrast which is custom for colourblind people
Pay a Phone Number Page:
Voice Input for Phone Numbers: Users can dictate the phone number, which the app automatically inputs into the respective field, simplifying the process.
The page transitions seamlessly to the Final Payment Page, where users benefit from the same voice-assisted features, streamlining the payment process.
Pick-Up Location Radius:
Polygon Selection on Map: Users can select a specific radius on the map, precisely defining their pick-up area. This feature eliminates ambiguity in specifying locations, such as buildings, lanes, or road sides. By allowing users to select a customized pick-up area, this feature minimizes the common confusion caused by Uber’s default reliance on the last known location.
Simplified UI for Geriatric Users:
The interface is redesigned to focus solely on essential functionalities, tailored to individual preferences. This reduces the cognitive load and mitigates the anxiety often associated with complex layouts.
Voice Input Capability:Users can dictate their commands, making the app more accessible and reducing the need for manual interaction, which is particularly beneficial for older users.
Enhanced Address Entry:
The text box for entering addresses expands to display the entire input, preventing errors and allowing users to review their entries more comfortably.
Street View Integration:This feature offers a visual representation of the destination, helping users utilize their visual memory to recognize and confirm their surroundings, reducing navigation errors and boosting confidence.
Conclusion
In conclusion, these user experience enhancements create a more intuitive, accessible, and personalized app. Comprehensive voice assistance available on key pages—such as Pay Contacts, Final Payment, and Pay a Phone Number—empowers users to accomplish tasks with ease, catering to all users and making the app exceptionally convenient.
The adaptive UI, which tailors itself to individual behaviors, places frequently used features within easy reach, boosting both efficiency and satisfaction. With an accessibility-focused design that includes voice input and simplified interfaces, the app is especially accommodating for geriatric users, making interactions smoother and less stressful. These thoughtful improvements make the app both powerful and user-centered, delivering an experience that truly supports and adapts to its users.
Recognitions
Our Capstone project, Adaptive UI/UX for Smart Geriatric Users, received the Best Capstone Project award in the social category at the Capstone Project Fair at PES University. This recognition, from a panel of professionals and academics from organizations like Reliance Jio, Visa, and Baxter International Inc., validates our commitment to creating inclusive design solutions.
My journey in Human-Computer Interaction (HCI) began in my 5th semester, when I realized my passion for user-centered design. Through coursework, hackathons, and two foundational projects—a peer-to-peer college platform called DrutPals and a redesign of GPay for geriatric users—I developed a deep understanding of simplicity and inclusivity in design. These values became the cornerstone of our Capstone project.
We recognized the unique challenges faced by older adults, including heightened tech anxiety and accessibility issues. Our goal was to design a UI/UX framework that simplifies payment and cab booking interfaces for elderly users in India. By creating a cognitive and content model, we ensured that our designs were intuitive, approachable, and user-friendly. This project taught me that effective design is not just about aesthetics; it’s about empathy, understanding diverse needs, and empowering all users, regardless of age or ability.
The Paper is Published in IEEE! Here is the link to the paper
Adaptive UI/UX for Smart Geriatric UsersPresented this is ICCR Conference 2024
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