Design the entire user interface of WurQ application

WurQ- Fitness Tracking App (Harvard spin-off startup)
My role: Lead User Researcher & Product Designer
Team: CEO, Product Manager, 2 Software Developers
Date: Jul 2022- May 2023

  • What is WurQ?

    WurQ is a fitness tech startup that combines wearable sensors, AI, and signal-processing to quantify strength and functional training.
    It tracks 70+ exercise movements (such as weightlifting, gymnastics, metabolic conditioning), measures biomechanical metrics like range of motion, pacing, rest vs. active time, power output, movement efficiency, and provides personalized feedback and insights.

    Background:

    Traditional fitness wearables excel at counting steps and monitoring heart rate but fall short when it comes to strength and functional training. Athletes who lift weights or do complex movements rarely get accurate data on range of motion, power output, or form quality, leaving them to rely on guesswork or manual logging.

    WurQ bridges this gap with a system of two sensors and a companion app that capture every rep, movement, and heart-rate response. Its algorithms convert raw motion and cardio data into actionable metrics—power, range of motion, pace—helping athletes identify weaknesses, track progress over time, and even compare performance within a community.

    <img src="https://images.squarespace-cdn.com/content/6849df8a3dabe50908c16035/e1d95655-872b-4c14-b7e5-4d4916ef9b42/image+52.png?">

    “I train hard five days a week, but all my tracker gives me is heart rate and calories. I have no idea if my form is improving or if I’m pushing too much and risking injury—and I don’t want to stop mid-set to log anything.”

    _ CrossFit athlete

Project 1: Design pre-workout screens

  • Challenge :

    The process of starting a workout involved too many steps. Athletes had to:

    1. Locate their sensors.

    2. Manually connect them to their phone.

    3. Calibrate the sensors before beginning their session.

    This was especially frustrating when they were in a rush, such as before a class.

    Technical Limitation:

    Since users shared sensors rather than having personal ones, we couldn’t implement an automated connection feature for beta users.

  • Automatic Sensor Detection:

    As soon as users entered the connection screen, the app began searching for sensors—eliminating the need for manual scanning.

    • Seamless Connection: Once users selected both sensors, they were automatically paired without requiring an extra click.

    Simplified Sensor Orientation:

    • Users often wore sensors incorrectly (e.g., placing a chest sensor on the wrist or wearing them in the wrong orientation), leading to inaccurate movement detection.

    • Instead of requiring multiple clicks for verification, users only needed to stand still for three seconds—no buttons to press.

    • The app automatically checked sensor placement and provided real-time guidance if adjustments were needed.

    <img src="https://images.squarespace-cdn.com/content/6849df8a3dabe50908c16035/464ef1a0-bebb-4363-ab24-cb0d6cce5769/Frame+22.png?">

    • 7 fewer clicks to connect sensors.


    • ‍Improved efficiency, fewer errors, and higher accuracy in movement detection.


    • 100% of users understood and found the new process easy to navigate.


    • SPARK Effect: By minimizing effort, users were more likely to take action. Simply standing still for three seconds allowed the app to handle the rest.

Project 2: Design result page

  • The two wearable sensors captured a vast amount of workout data, offering valuable insights for users.
    These sensors allowed us to track key performance metrics such as range of motion (ROM), workout pace, movement patterns, duration per exercise, and rest periods. However, accuracy was crucial—we couldn’t risk sharing unreliable performance data.
    To ensure both accuracy and relevance, we focused on the most valuable metrics identified through interviews and focus groups. We prioritized metrics where we were at least 90% confident in data accuracy.

    Key metrics we prioritized:

    • Timing: Users cared most about how long it took to complete their workout and how much time they spent resting. Many were surprised by the actual rest time.

    • Pace: They wanted insights into when they slowed down during their workout and how their pacing compared to others.

    • Heart Rate (HR): While users already tracked HR with Whoop or Apple Watch, those devices only provided general HR trends. What was missing was HR data specific to each movement, helping users understand their exertion levels in real time.

  • Summary page:

    This page provides users with comprehensive timing data, including:

    • Total workout duration

    • Number of rounds completed

    • Total rest time

    • Detailed timing breakdown for each round

    Key Feature: Pace Graph

    The pace graph is the primary focus of this screen, visualizing an athlete’s speed fluctuations throughout the workout and comparing their pace to the group average.

    <img src="https://images.squarespace-cdn.com/content/6849df8a3dabe50908c16035/a2269ea8-173e-49c7-9a4f-cc8f3ab971c9/Frame+23.png?">

    Heart rate page:

    This page provides users with detailed heart rate data, including:

    • Average heart rate and max heart rate

    • Heart rate throughout the workout

    • Time spent in each heart rate zone per movement

    Key Feature: Heart rate Graph

    The heart rate graph highlights heart rate fluctuations during the workout, allowing users to see how their heart rate changes with each movement and rest period.

    <img src="https://images.squarespace-cdn.com/content/6849df8a3dabe50908c16035/e6fda2ce-f13e-477e-b5a8-10d5e98c25f3/Frame+24.png?">

    In the UX design, the following principles were applied:

    • Fitts’s Law: The graph is the largest visual element on the screen, making it easy for users to locate, focus on, and interpret their performance data.

    • Progressive Disclosure: Additional insights appear when users hover over the graph, minimizing cognitive load and guiding them through the information step by step.

    • 98% User comprehension: Users understood and navigated the graphs.

    • 87% Customer satisfaction: Survey feedback indicated high satisfaction with the app design.

    • Heart rate tracking: Approximately 70% of users prioritized heart rate tracking.

    • Active vs. rest time: About 90% of users found the distinction between active and rest time highly engaging.

    And these are some quotes from our users about pace graph and heart page:

    Pace graph:

    • “Pretty easy to interpret what that meant. This is streamlined and easy to read. It’s perfect.”


    • “This shows me where I lost time and pace—exactly what I needed to know.”

    Heart rate page:

    • “Cool. Oh yeah. Oh wow… This is really cool.”


    • “I really like how you can see your heart rate over time and see which specific movement it corresponds to. I think that's really cool. I like this page a lot.”


    • “Amazing. This is great. Oh my God. Whoop gives you this, but this is better.”

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