Spot Me — Concept app
Preventing Weightlifting Injuries with AI Powered Real-time Form Feedback
Introduction
Spot Me is an AI-powered weightlifting training app that utilizes human pose estimation technology to analyze lifting form in real-time and provides corrective feedback to prevent injury. The idea started when my Uncle seriously hurt himself while weightlifting alone because his form was incorrect, and having a personal trainer was too expensive. After talking to other lifters, I realized this was a shared experience, which motivated me to create a digital solution that made form correction more accessible.
Skills
User Research
Design Systems
Prototyping
Timeline
20 Weeks
Tools
Figma
Adobe AfterEffects
Overview
Leveraging emerging AI technology to help make personalized coaching accessible and prevent injury.
Real-time Form Analaysis
Leverage human pose estimation technology to analyze users' form by recording themselves on their phone.
Feedback Prevents Injuries
Provide corrective and encouraging feedback to support progress and prevent injuries.
Motivational Tracking
Encourage users to lift with correct form, using statistics to track progress and achievements over time.
Research
Incorrect weightlifting form can lead to serious injuries.
There's a common misconception that lifting injuries are related to gender, age, or weight, but that's not the case. In a study by NHI, the only significant association found was between injuries and the weight carried. That means as lifters increased the weight carried with incorrect form, their chances of injury increased.
"The biggest pain is that I don't have a trainer or someone to spot me while working out. How would I know I'm doing something wrong if no one corrects me?"
— Dennis, Lifting for 10+ years
Why are lifters struggling with form?
To gain a holistic perspective on this issue, I interviewed over 10 weightlifters. They ranged from beginners with less than a year of experience to veterans who had been lifting for years, and from those who didn't use any technology to avid trackers. I gathered 150+ data points that revealed key challenges:
Meta insights
Hard to Follow Workout Videos
Only following apps or workout videos makes it challenging to see form.
Expensive Personal Trainers
Most users weren’t ready to invest in a personal trainer, even if they wanted one.
Pushing Limits Too Far
Pushing your limits with improper form prevents progress and causes injuries.
Who am I designing for?
Through my interviews, I determined two types of users with unique challenges but the same root of the problem. Whether you're trying to do an exercise for the first time or perfecting your technique, the lack of personal guidance holds you back.
Personas
Lifting Newbie
Weightlifting 3 months
Behaviors
Just began lifting and following workout videos. They worry about getting hurt when trying a new set, but find the personal trainers too expensive to start with.
Goals
Personalized guidance during workouts to make sure their form is correct and prevent injury.
Dedicated Learner
Weightlifting 5+ years
Behaviors
Working out is essential to their routine. They started researching proper form after a serious injury, not realizing they were doing the exercise incorrectly.
Goals
Consistently improve their form and see results, like a new 'personal best' in weight carried.
Empathy mapping
secondary research
How can technology provide personalized training?
Spotters watch over lifters while lifting
'Spotting' in weight training is when two people take turns looking out for each other while the other person is lifting. This helps ensure proper technique, prevents injury, and boosts confidence.
HPE for digital spotting
I discovered Human Pose Estimation (HPE), which is gaining the most attention in AI fitness, as it can analyze athletes' movements in various scenarios using just a phone camera.
challenge
How can we help lifters with proper form during their workouts, using HPE for live feedback and injury prevention?
development
Leveraging HPE to analyze form in real-time
I realized this was a genuine area of opportunity: a mobile app that could use human pose estimation to analyze lifters' form while weightlifting
Sketches
System map
Divergent Concepts
Tailoring the user experience to fit weightlifting needs
Based on user insights and my brainstorming, I designed prototypes for two divergent concepts to test with users and gather their reactions on which features resonated with them and which didn't.
Concept 1: Live Feedback & Timers
This concept involves users toggling between coach-led videos and Spot Me analysis mode for live feedback. Alert information is bite-sized and given in real time so that users can quickly correct their execution.
Concept 2: Delayed Feedback & In-Depth Analysis
This concept involves users recording themselves, completing the set, and then submitting the video for in-depth feedback. They're able to see all the alerts at once.
Key insights from user testing
By testing these concepts with casual weightlifters who have struggled with their form, I was able to notice patterns when it came to how features would integrate into their lifting routines.
Information
Timer and progress bars weren't needed since users prioritize the quality of each rep over speed.
Alerts
In-depth information is useful in post-analysis, but prefer bite-sized alerts during the workout.
Progress Tracking
Curious to see how this workout’s accuracy compares to past ones, also logged into the app.
Design
1.0
Real-time Form Analysis
Coach-Led Workout Videos
Based on user interviews, it was clear that users preferred following workout programs and videos. The home page leads them to their workout for the day and uses coach-led video tutorials to make it easy to follow along with each set.
Emphasize Rep Name & Number
Weightlifters prefer to take their time and ensure that their form is correct. The timer and progress bars were removed to clearly focus on the information for that set.
Spot Me Toggle
Coach-led videos show how to do the rep correctly from different angles. When users are ready, they can easily switch to "Spot Me" mode and start recording themselves for feedback.
2.0
Feedback Prevents Injuries
Form Analysis and Alerts
By integrating human pose estimation technology, users are able to have their form analyzed just by recording themselves on their phone in the gym. Once the incorrect form has been detected, this triggers the app to alert the user visually and audibly.
During Workout
Post Workout
Bite-Size Alerts Now, More Info Later
In-depth information is interesting to users, but it would be more helpful to read this after the workout. Having bite-sized alerts that pop-up in real time once incorrect form has been detected makes it easier for users to adjust and prevent injury.
Post-Workout Analysis
End the workout with a brief performance recap and an option for detailed information on each set. See all alerts to see your mistakes and learn how to improve.
Workout Overview
Users can check the accuracy of each set in that day's workout. The aim is to provide flexibility in the depth of information based on what users want to know.
3.0
Motivational Tracking
Progress Over Time
Dive even deeper into the progress of each rep and showcase accuracy, number of alerts, and weight carried. Users mentioned how satisfying it is to see their achievements over time.
Reflections
What did I learn from this project?
Design Isn't a Linear Process
At the start of this project, I was uncertain about which fitness problem to tackle. It wasn’t until my mid-fidelity interviews that I focused on weightlifting. The process involved revisiting my research, but that’s the thrill of design— drawing, researching, testing, and repeating. Keeping the user’s needs as my north star guided me to a genuine area of opportunity.
It's Important to Stay Curious
I love getting into UI and UX design, but that’s just one piece of the puzzle. Researching AI has shown me how crucial it is to stay updated with the latest tech to keep innovating. I think it’s all about balancing the tech we have with imagining what it could be to keep pushing the boundaries.
My Other Works
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